0.0

THE QUEST FOR ARTIFICIAL INTELLIGENCEA HISTORY OF IDEAS AND ACHIEVEMENTS
Web Version
Print version published by Cambridge University Press
http://www.cambridge.org/us/0521122937

Nils J. Nilsson
Stanford University

1
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

0

For Grace McConnell Abbott,

my wife and best friend

2
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

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Contents
I

Beginnings

17

1 Dreams and Dreamers

19

2 Clues27

2.1

From Philosophy and Logic . . . . . . . . . . . . . . . . . . . . .

27

2.2

From Life Itself . . . . . . . . . . . . . . . . . . . . . . . . . . . .

33

2.2.1

Neurons and the Brain . . . . . . . . . . . . . . . . . . . .

34

2.2.2

Psychology and Cognitive Science . . . . . . . . . . . . .

37

2.2.3

Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . .

43

2.2.4

Development and Maturation . . . . . . . . . . . . . . . .

45

2.2.5

Bionics . . . . . . . . . . . . . . . . . . . . . . . . . . . .

46

From Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . .

46

2.3.1

Automata, Sensing, and Feedback . . . . . . . . . . . . .

46

2.3.2

Statistics and Probability . . . . . . . . . . . . . . . . . .

52

2.3.3

The Computer . . . . . . . . . . . . . . . . . . . . . . . .

53

2.3

II

Early Explorations: 1950s and 1960s

3 Gatherings

71
73

3.1

Session on Learning Machines . . . . . . . . . . . . . . . . . . . .

73

3.2

The Dartmouth Summer Project . . . . . . . . . . . . . . . . . .

77

3.3

Mechanization of Thought Processes . . . . . . . . . . . . . . . .

81

4 Pattern Recognition

89

3
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
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CONTENTS
4.1

Character Recognition . . . . . . . . . . . . . . . . . . . . . . . .

90

4.2

Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . .

92

4.2.1

Perceptrons . . . . . . . . . . . . . . . . . . . . . . . . . .

92

4.2.2

ADALINES and MADALINES . . . . . . . . . . . . . . .

98

4.2.3

The MINOS Systems at SRI . . . . . . . . . . . . . . . .

98

4.3

Statistical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . 102

4.4

Applications of Pattern Recognition to Aerial Reconnaissance . . 105

5 Early Heuristic Programs

113

5.1

The Logic Theorist and Heuristic Search . . . . . . . . . . . . . . 113

5.2

Proving Theorems in Geometry . . . . . . . . . . . . . . . . . . . 118

5.3

The General Problem Solver . . . . . . . . . . . . . . . . . . . . . 121

5.4

Game-Playing Programs . . . . . . . . . . . . . . . . . . . . . . . 123

6 Semantic Representations

131

6.1

Solving Geometric Analogy Problems . . . . . . . . . . . . . . . . 131

6.2

Storing Information and Answering Questions . . . . . . . . . . . 134

6.3

Semantic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 136

7 Natural Language Processing

141

7.1

Linguistic Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . 141

7.2

Machine Translation . . . . . . . . . . . . . . . . . . . . . . . . . 146

7.3

Question Answering . . . . . . . . . . . . . . . . . . . . . . . . . 150

8 1960s’ Infrastructure

155

8.1

Programming Languages . . . . . . . . . . . . . . . . . . . . . . . 155

8.2

Early AI Laboratories . . . . . . . . . . . . . . . . . . . . . . . . 157

8.3

Research Support . . . . . . . . . . . . . . . . . . . . . . . . . . . 160

8.4

All Dressed Up and Places to Go . . . . . . . . . . . . . . . . . . 163

III

Efflorescence: Mid-1960s to Mid-1970s

9 Computer Vision
9.1

167
169

Hints from Biology . . . . . . . . . . . . . . . . . . . . . . . . . . 171

4
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
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CONTENTS
9.2

Recognizing Faces . . . . . . . . . . . . . . . . . . . . . . . . . . 172

9.3

Computer Vision of Three-Dimensional Solid Objects. . . . . . 173

9.3.1

An Early Vision System . . . . . . . . . . . . . . . . . . . 173

9.3.2

The “Summer Vision Project” . . . . . . . . . . . . . . . 175

9.3.3

Image Filtering . . . . . . . . . . . . . . . . . . . . . . . . 176

9.3.4

Processing Line Drawings . . . . . . . . . . . . . . . . . . 181

10 “Hand–Eye” Research

189

10.1 At MIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
10.2 At Stanford . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
10.3 In Japan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
10.4 Edinburgh’s “FREDDY” . . . . . . . . . . . . . . . . . . . . . . . 193
11 Knowledge Representation and Reasoning

199

11.1 Deductions in Symbolic Logic . . . . . . . . . . . . . . . . . . . . 200
11.2 The Situation Calculus . . . . . . . . . . . . . . . . . . . . . . . . 202
11.3 Logic Programming. . . . . . . . . . . . . . . . . . . . . . . . . 203

11.4 Semantic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 205
11.5 Scripts and Frames . . . . . . . . . . . . . . . . . . . . . . . . . . 207
12 Mobile Robots

213

12.1 Shakey, the SRI Robot . . . . . . . . . . . . . . . . . . . . . . . . 213
12.1.1 A∗ : A New Heuristic Search Method . . . . . . . . . . . . 216
12.1.2 Robust Action Execution . . . . . . . . . . . . . . . . . . 221
12.1.3 STRIPS: A New Planning Method . . . . . . . . . . . . . 222
12.1.4 Learning and Executing Plans. . . . . . . . . . . . . . . 224

12.1.5 Shakey’s Vision Routines . . . . . . . . . . . . . . . . . . 224
12.1.6 Some Experiments with Shakey . . . . . . . . . . . . . . . 228
12.1.7 Shakey Runs into Funding Troubles . . . . . . . . . . . . 229
12.2 The Stanford Cart . . . . . . . . . . . . . . . . . . . . . . . . . . 231
13 Progress in Natural Language Processing

237

13.1 Machine Translation . . . . . . . . . . . . . . . . . . . . . . . . . 237
13.2 Understanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
5
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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CONTENTS
13.2.1 SHRDLU . . . . . . . . . . . . . . . . . . . . . . . . . . . 238
13.2.2 LUNAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
13.2.3 Augmented Transition Networks . . . . . . . . . . . . . . 244
13.2.4 GUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246

14 Game Playing251

15 The Dendral Project255

16 Conferences, Books, and Funding

261

IV Applications and Specializations: 1970s to Early
1980s
265
17 Speech Recognition and Understanding Systems

267

17.1 Speech Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 267
17.2 The Speech Understanding Study Group . . . . . . . . . . . . . . 270
17.3 The DARPA Speech Understanding Research Program . . . . . . 271
17.3.1 Work at BBN . . . . . . . . . . . . . . . . . . . . . . . . . 271
17.3.2 Work at CMU . . . . . . . . . . . . . . . . . . . . . . . . 272
17.3.3 Summary and Impact of the SUR Program . . . . . . . . 280
17.4 Subsequent Work in Speech Recognition . . . . . . . . . . . . . . 281
18 Consulting Systems

285

18.1 The SRI Computer-Based Consultant . . . . . . . . . . . . . . . 285
18.2 Expert Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
18.2.1 MYCIN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
18.2.2 PROSPECTOR . . . . . . . . . . . . . . . . . . . . . . . . . 295
18.2.3 Other Expert Systems . . . . . . . . . . . . . . . . . . . . 300
18.2.4 Expert Companies . . . . . . . . . . . . . . . . . . . . . . 303
19 Understanding Queries and Signals309

19.1 The Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
19.2 Natural Language Access to Computer Systems . . . . . . . . . . 313
19.2.1 LIFER . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
6
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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CONTENTS
19.2.2 CHAT-80 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
19.2.3 Transportable Natural Language Query Systems . . . . . 318
19.3 HASP/SIAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319

20 Progress in Computer Vision327

20.1 Beyond Line-Finding . . . . . . . . . . . . . . . . . . . . . . . . . 327
20.1.1 Shape from Shading . . . . . . . . . . . . . . . . . . . . . 327
20.1.2 The 2 21 -D Sketch . . . . . . . . . . . . . . . . . . . . . . . 329
20.1.3 Intrinsic Images . . . . . . . . . . . . . . . . . . . . . . . . 329
20.2 Finding Objects in Scenes . . . . . . . . . . . . . . . . . . . . . . 333
20.2.1 Reasoning about Scenes . . . . . . . . . . . . . . . . . . . 333
20.2.2 Using Templates and Models . . . . . . . . . . . . . . . . 335
20.3 DARPA’s Image Understanding Program . . . . . . . . . . . . . 338
21 Boomtimes

343

V

347

“New-Generation” Projects

22 The Japanese Create a Stir

349

22.1 The Fifth-Generation Computer Systems Project . . . . . . . . . 349
22.2 Some Impacts of the Japanese Project . . . . . . . . . . . . . . . 354
22.2.1 The Microelectronics and Computer Technology Corporation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
22.2.2 The Alvey Program . . . . . . . . . . . . . . . . . . . . . 355
22.2.3 ESPRIT . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355
23 DARPA’s Strategic Computing Program359

23.1 The Strategic Computing Plan . . . . . . . . . . . . . . . . . . . 359
23.2 Major Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 362
23.2.1 The Pilot’s Associate . . . . . . . . . . . . . . . . . . . . . 363
23.2.2 Battle Management Systems . . . . . . . . . . . . . . . . 364
23.2.3 Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . 366
23.3 AI Technology Base . . . . . . . . . . . . . . . . . . . . . . . . . 369
23.3.1 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . 370
7
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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CONTENTS
23.3.2 Speech Recognition and Natural Language Processing . . 370
23.3.3 Expert Systems . . . . . . . . . . . . . . . . . . . . . . . . 372
23.4 Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 373

VI

Entr’acte379

24 Speed Bumps

381

24.1 Opinions from Various Onlookers . . . . . . . . . . . . . . . . . . 381
24.1.1 The Mind Is Not a Machine . . . . . . . . . . . . . . . . . 381
24.1.2 The Mind Is Not a Computer . . . . . . . . . . . . . . . . 383
24.1.3 Differences between Brains and Computers . . . . . . . . 392
24.1.4 But Should We? . . . . . . . . . . . . . . . . . . . . . . . 393
24.1.5 Other Opinions . . . . . . . . . . . . . . . . . . . . . . . . 398
24.2 Problems of Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . 399
24.2.1 The Combinatorial Explosion . . . . . . . . . . . . . . . . 399
24.2.2 Complexity Theory . . . . . . . . . . . . . . . . . . . . . . 401
24.2.3 A Sober Assessment . . . . . . . . . . . . . . . . . . . . . 402
24.3 Acknowledged Shortcomings . . . . . . . . . . . . . . . . . . . . . 406
24.4 The “AI Winter” . . . . . . . . . . . . . . . . . . . . . . . . . . . 408
25 Controversies and Alternative Paradigms

413

25.1 About Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413
25.2 Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414
25.3 “Kludginess” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 416
25.4 About Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417
25.4.1 Behavior-Based Robots . . . . . . . . . . . . . . . . . . . 417
25.4.2 Teleo-Reactive Programs. . . . . . . . . . . . . . . . . . 419

25.5 Brain-Style Computation . . . . . . . . . . . . . . . . . . . . . . 423
25.5.1 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . 423
25.5.2 Dynamical Processes . . . . . . . . . . . . . . . . . . . . . 424
25.6 Simulating Evolution . . . . . . . . . . . . . . . . . . . . . . . . . 425
25.7 Scaling Back AI’s Goals . . . . . . . . . . . . . . . . . . . . . . . 429

8Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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CONTENTS

VII The Growing Armamentarium: From the 1980s
Onward433
26 Reasoning and Representation

435

26.1 Nonmonotonic or Defeasible Reasoning . . . . . . . . . . . . . . . 435
26.2 Qualitative Reasoning . . . . . . . . . . . . . . . . . . . . . . . . 439
26.3 Semantic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 441
26.3.1 Description Logics . . . . . . . . . . . . . . . . . . . . . . 441
26.3.2 WordNet . . . . . . . . . . . . . . . . . . . . . . . . . . . 444
26.3.3 Cyc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
27 Other Approaches to Reasoning and Representation455

27.1 Solving Constraint Satisfaction Problems . . . . . . . . . . . . . 455
27.2 Solving Problems Using Propositional Logic . . . . . . . . . . . . 460
27.2.1 Systematic Methods . . . . . . . . . . . . . . . . . . . . . 461
27.2.2 Local Search Methods . . . . . . . . . . . . . . . . . . . . 463
27.2.3 Applications of SAT Solvers . . . . . . . . . . . . . . . . . 466
27.3 Representing Text as Vectors . . . . . . . . . . . . . . . . . . . . 466
27.4 Latent Semantic Analysis . . . . . . . . . . . . . . . . . . . . . . 469
28 Bayesian Networks

475

28.1 Representing Probabilities in Networks . . . . . . . . . . . . . . . 475
28.2 Automatic Construction of Bayesian Networks . . . . . . . . . . 482
28.3 Probabilistic Relational Models . . . . . . . . . . . . . . . . . . . 486
28.4 Temporal Bayesian Networks . . . . . . . . . . . . . . . . . . . . 488
29 Machine Learning495

29.1 Memory-Based Learning . . . . . . . . . . . . . . . . . . . . . . . 496
29.2 Case-Based Reasoning . . . . . . . . . . . . . . . . . . . . . . . . 498
29.3 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500
29.3.1 Data Mining and Decision Trees . . . . . . . . . . . . . . 500
29.3.2 Constructing Decision Trees . . . . . . . . . . . . . . . . . 502
29.4 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 507
29.4.1 The Backprop Algorithm . . . . . . . . . . . . . . . . . . 508

9Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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CONTENTS
29.4.2 NETtalk . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509
29.4.3 ALVINN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510
29.5 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . 513
29.6 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . 515
29.6.1 Learning Optimal Policies . . . . . . . . . . . . . . . . . . 515
29.6.2 TD-GAMMON . . . . . . . . . . . . . . . . . . . . . . . . . 522
29.6.3 Other Applications . . . . . . . . . . . . . . . . . . . . . . 523
29.7 Enhancements. . . . . . . . . . . . . . . . . . . . . . . . . . . . 524

30 Natural Languages and Natural Scenes

533

30.1 Natural Language Processing . . . . . . . . . . . . . . . . . . . . 533
30.1.1 Grammars and Parsing Algorithms . . . . . . . . . . . . . 534
30.1.2 Statistical NLP . . . . . . . . . . . . . . . . . . . . . . . . 535
30.2 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 539
30.2.1 Recovering Surface and Depth Information . . . . . . . . 541
30.2.2 Tracking Moving Objects . . . . . . . . . . . . . . . . . . 544
30.2.3 Hierarchical Models . . . . . . . . . . . . . . . . . . . . . 548
30.2.4 Image Grammars . . . . . . . . . . . . . . . . . . . . . . . 555
31 Intelligent System Architectures561

31.1 Computational Architectures . . . . . . . . . . . . . . . . . . . . 563
31.1.1 Three-Layer Architectures . . . . . . . . . . . . . . . . . . 563
31.1.2 Multilayered Architectures. . . . . . . . . . . . . . . . . 563

31.1.3 The BDI Architecture . . . . . . . . . . . . . . . . . . . . 569
31.1.4 Architectures for Groups of Agents . . . . . . . . . . . . . 572
31.2 Cognitive Architectures . . . . . . . . . . . . . . . . . . . . . . . 576
31.2.1 Production Systems . . . . . . . . . . . . . . . . . . . . . 576
31.2.2 ACT-R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578
31.2.3 SOAR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581

VIII

Modern AI: Today and Tomorrow

32 Extraordinary Achievements

10
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591

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32.1 Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591
32.1.1 Chess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 591
32.1.2 Checkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595
32.1.3 Other Games . . . . . . . . . . . . . . . . . . . . . . . . . 598
32.2 Robot Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 600
32.2.1 Remote Agent in Deep Space 1 . . . . . . . . . . . . . . . 600
32.2.2 Driverless Automobiles . . . . . . . . . . . . . . . . . . . . 603

33 Ubiquitous Artificial Intelligence615

33.1 AI at Home . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616
33.2 Advanced Driver Assistance Systems . . . . . . . . . . . . . . . . 617
33.3 Route Finding in Maps

. . . . . . . . . . . . . . . . . . . . . . . 618

33.4 You Might Also Like. . . . . . . . . . . . . . . . . . . . . . . . . . 618
33.5 Computer Games . . . . . . . . . . . . . . . . . . . . . . . . . . . 619
34 Smart Tools

623

34.1 In Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 623
34.2 For Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 625
34.3 For Automated Trading . . . . . . . . . . . . . . . . . . . . . . . 626
34.4 In Business Practices . . . . . . . . . . . . . . . . . . . . . . . . . 627
34.5 In Translating Languages . . . . . . . . . . . . . . . . . . . . . . 628
34.6 For Automating Invention . . . . . . . . . . . . . . . . . . . . . . 628
34.7 For Recognizing Faces . . . . . . . . . . . . . . . . . . . . . . . . 628
35 The Quest Continues

633

35.1 In the Labs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634
35.1.1 Specialized Systems . . . . . . . . . . . . . . . . . . . . . 634
35.1.2 Broadly Applicable Systems . . . . . . . . . . . . . . . . . 638
35.2 Toward Human-Level Artificial Intelligence . . . . . . . . . . . . 646
35.2.1 Eye on the Prize . . . . . . . . . . . . . . . . . . . . . . . 646
35.2.2 Controversies . . . . . . . . . . . . . . . . . . . . . . . . . 648
35.2.3 How Do We Get It? . . . . . . . . . . . . . . . . . . . . . 649
35.2.4 Some Possible Consequences of HLAI . . . . . . . . . . . 652

11
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CONTENTS
35.3 Summing Up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656

12
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0.0

Preface
Artificial intelligence (AI) may lack an agreed-upon definition, but someone
writing about its history must have some kind of definition in mind. For me,
artificial intelligence is that activity devoted to making machines intelligent,and intelligence is that quality that enables an entity to function appropriately
and with foresight in its environment. According to that definition, lots ofthings – humans, animals, and some machines – are intelligent. Machines, suchas “smart cameras,” and many animals are at the primitive end of theextended continuum along which entities with various degrees of intelligence
are arrayed. At the other end are humans, who are able to reason, achievegoals, understand and generate language, perceive and respond to sensory
inputs, prove mathematical theorems, play challenging games, synthesize andsummarize information, create art and music, and even write histories.
Because “functioning appropriately and with foresight” requires so manydifferent capabilities, depending on the environment, we actually have several
continua of intelligences with no particularly sharp discontinuities in any ofthem. For these reasons, I take a rather generous view of what constitutes AI.
That means that my history of the subject will, at times, include some control
engineering, some electrical engineering, some statistics, some linguistics, somelogic, and some computer science.
There have been other histories of AI, but time marches on, as has AI, so
a new history needs to be written. I have participated in the quest for artificial
intelligence for fifty years – all of my professional life and nearly all of the lifeof the field. I thought it would be a good idea for an “insider” to try to tell
the story of this quest from its beginnings up to the present time.
I have three kinds of readers in mind. One is the intelligent lay reader
interested in scientific topics who might be curious about what AI is all about.
Another group, perhaps overlapping the first, consists of those in technical or
professional fields who, for one reason or another, need to know about AI andwould benefit from a complete picture of the field – where it has been, where it
is now, and where it might be going. To both of these groups, I promise nocomplicated mathematics or computer jargon, lots of diagrams, and my best
efforts to provide clear explanations of how AI programs and techniques work.
(I also include several photographs of AI people. The selection of these is13
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0

CONTENTS

somewhat random and doesn’t necessarily indicate prominence in the field.)A third group consists of AI researchers, students, and teachers who
would benefit from knowing more about the things AI has tried, what has andhasn’t worked, and good sources for historical and other information. Knowing
the history of a field is important for those engaged in it. For one thing, manyideas that were explored and then abandoned might now be viable because of
improved technological capabilities. For that group, I include extensive
end-of-chapter notes citing source material. The general reader will missnothing by ignoring these notes. The main text itself mentions Web sites
where interesting films, demonstrations, and background can be found. (Iflinks to these sites become broken, readers may still be able to access themusing the “Wayback Machine” at http://www.archive.org.)
The book follows a roughly chronological approach, with some backing
and filling. My story may have left out some actors and events, but I hope it isreasonably representative of AI’s main ideas, controversies, successes, andlimitations. I focus more on the ideas and their realizations than on the
personalities involved. I believe that to appreciate AI’s history, one has tounderstand, at least in lay terms, something about how AI programs actuallywork.
If AI is about endowing machines with intelligence, what counts as amachine? To many people, a machine is a rather stolid thing. The word
evokes images of gears grinding, steam hissing, and steel parts clanking.
Nowadays, however, the computer has greatly expanded our notion of what amachine can be. A functioning computer system contains both hardware andsoftware, and we frequently think of the software itself as a “machine.” Forexample, we refer to “chess-playing machines” and “machines that learn,”when we actually mean the programs that are doing those things. The
distinction between hardware and software has become somewhat blurredbecause most modern computers have some of their programs built right into
their hardware circuitry.
Whatever abilities and knowledge I bring to the writing of this book stemfrom the support of many people, institutions, and funding agencies. First, myparents, Walter Alfred Nilsson (1907–1991) and Pauline Glerum Nilsson(1910–1998), launched me into life. They provided the right mixture of disdain
for mediocrity and excuses (Walter), kind care (Pauline), and praise andencouragement (both). Stanford University is literally and figuratively myalma mater (Latin for “nourishing mother”). First as a student and later as afaculty member (now emeritus), I have continued to learn and to benefit fromcolleagues throughout the university and especially from students. SRIInternational (once called the Stanford Research Institute) provided a homewith colleagues who helped me to learn about and to “do” AI. I make special
acknowledgement to the late Charles A. Rosen, who persuaded me in 1961 tojoin his “Learning Machines Group” there. The Defense Advanced Research
Projects Agency (DARPA), the Office of Naval Research (ONR), the Air Force
14
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CONTENTS

Office of Scientific Research (AFOSR), the U.S. Geological Survey (USGS),
the National Science Foundation (NSF), and the National Aeronautics andSpace Administration (NASA) all supported various research efforts I was partof during the last fifty years. I owe thanks to all.
To the many people who have helped me with the actual research andwriting for this book, including anonymous and not-so-anonymous reviewers,
please accept my sincere appreciation together with my apologies for notnaming all of you personally in this preface. There are too many of you to list,
and I am afraid I might forget to mention someone who might have madesome brief but important suggestions. Anyway, you know who you are. You
are many of the people whom I mention in the book itself. However, I do wantto mention Heather Bergman, of Cambridge University Press, Mykel
Kochenderfer, a former student, and Wolfgang Bibel of the Darmstadt
University of Technology. They all read carefully early versions of the entire
manuscript and made many helpful suggestions. (Mykel also provided
invaluable advice about the LATEX typesetting program.)
I also want to thank the people who invented, developed, and now
manage the Internet, the World Wide Web, and the search engines that helped
me in writing this book. Using Stanford’s various site licenses, I could locate
and access journal articles, archives, and other material without leaving mydesk. (I did have to visit libraries to find books. Publishers, please allow
copyrighted books, especially those whose sales have now diminished, to bescanned and made available online. Join the twenty-first century!)
Finally, and most importantly, I thank my wife, Grace, who cheerfully
and patiently urged me on.
In 1982, the late Allen Newell, one of the founders of AI, wrote“Ultimately, we will get real histories of Artificial Intelligence. . . , written withas much objectivity as the historians of science can muster. That time iscertainly not yet.”
Perhaps it is now.

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CONTENTS

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0.0Part I

Beginnings

17
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018
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1.0

Chapter 1

Dreams and Dreamers
The quest for artificial intelligence (AI) begins with dreams – as all quests do.
People have long imagined machines with human abilities – automata thatmove and devices that reason. Human-like machines are described in manystories and are pictured in sculptures, paintings, and drawings.
You may be familiar with many of these, but let me mention a few. The
Iliad of Homer talks about self-propelled chairs called “tripods” and golden“attendants” constructed by Hephaistos, the lame blacksmith god, to help him
get around.1∗ And, in the ancient Greek myth as retold by Ovid in his
Metamorphoses, Pygmalian sculpts an ivory statue of a beautiful maiden,
Galatea, which Venus brings to life:2
The girl felt the kisses he gave, blushed, and, raising her bashfuleyes to the light, saw both her lover and the sky.
The ancient Greek philosopher Aristotle (384–322 bce) dreamed of
automation also, but apparently he thought it an impossible fantasy – thus
making slavery necessary if people were to enjoy leisure. In his The Politics,
he wrote3
For suppose that every tool we had could perform its task, either
at our bidding or itself perceiving the need, and if – like. . . thetripods of Hephaestus, of which the poet [that is, Homer] says that
“self-moved they enter the assembly of gods” – shuttles in a loom
could fly to and fro and a plucker [the tool used to pluck thestrings] play a lyre of their own accord, then master craftsmen
would have no need of servants nor masters of slaves.
∗ So as not to distract the general reader unnecessarily, numbered notes containing citations
to source materials appear at the end of each chapter. Each of these is followed by the numberof the page where the reference to the note occurred.

19
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1Dreams and Dreamers

Aristotle might have been surprised to see a Jacquard loom weave of itself or aplayer piano doing its own playing.
Pursuing his own visionary dreams, Ramon Llull (circa 1235–1316), aCatalan mystic and poet, produced a set of paper discs called the Ars Magna(Great Art), which was intended, among other things, as a debating tool for
winning Muslims to the Christian faith through logic and reason. (See Fig.
1.1.) One of his disc assemblies was inscribed with some of the attributes ofGod, namely goodness, greatness, eternity, power, wisdom, will, virtue, truth,and glory. Rotating the discs appropriately was supposed to produce answers
to various theological questions.4

Figure 1.1: Ramon Llull (left) and his Ars Magna (right).
Ahead of his time with inventions (as usual), Leonardo Da Vinci sketched
designs for a humanoid robot in the form of a medieval knight around the year
1495. (See Fig. 1.2.) No one knows whether Leonardo or contemporaries tried
to build his design. Leonardo’s knight was supposed to be able to sit up, moveits arms and head, and open its jaw.5
The Talmud talks about holy persons creating artificial creatures called“golems.” These, like Adam, were usually created from earth. There arestories about rabbis using golems as servants. Like the Sorcerer’s Apprentice,
golems were sometimes difficult to control.
In 1651, Thomas Hobbes (1588–1679) published his book Leviathan about
the social contract and the ideal state. In the introduction Hobbes seems to
say that it might be possible to build an “artificial animal.”6
For seeing life is but a motion of limbs, the beginning whereof is insome principal part within, why may we not say that all automata(engines that move themselves by springs and wheels as doth a20
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Figure 1.2: Model of a robot knight based on drawings by Leonardo da Vinci.
watch) have an artificial life? For what is the heart, but a spring;and the nerves, but so many strings; and the joints, but so many
wheels, giving motion to the whole body. . .
Perhaps for this reason, the science historian George Dyson refers to Hobbes
as the “patriarch of artificial intelligence.”7
In addition to fictional artifices, several people constructed actual
automata that moved in startlingly lifelike ways.8 The most sophisticated ofthese was the mechanical duck designed and built by the French inventor andengineer, Jacques de Vaucanson (1709–1782). In 1738, Vaucanson displayed
his masterpiece, which could quack, flap its wings, paddle, drink water, andeat and “digest” grain.
As Vaucanson himself put it,921
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1Dreams and Dreamers
My second Machine, or Automaton, is a Duck, in which I represent
the Mechanism of the Intestines which are employed in the
Operations of Eating, Drinking, and Digestion: Wherein the
Working of all the Parts necessary for those Actions is exactly
imitated. The Duck stretches out its Neck to take Corn out of yourHand; it swallows it, digests it, and discharges it digested by theusual Passage.

There is controversy about whether or not the material “excreted” by the
duck came from the corn it swallowed. One of the automates-anciens Web
sites10 claims that “In restoring Vaucanson’s duck in 1844, the magician
Robert-Houdin discovered that ‘The discharge was prepared in advance: a sortof gruel composed of green-coloured bread crumb . . . ’.”Leaving digestion aside, Vaucanson’s duck was a remarkable piece ofengineering. He was quite aware of that himself. He wrote11
I believe that Persons of Skill and Attention, will see how difficult
it has been to make so many different moving Parts in this small
Automaton; as for Example, to make it rise upon its Legs, andthrow its Neck to the Right and Left. They will find the different
Changes of the Fulchrum’s or Centers of Motion: they will also seethat what sometimes is a Center of Motion for a moveable Part,
another Time becomes moveable on that Part, which Part thenbecomes fix’d. In a Word, they will be sensible of a prodigious
Number of Mechanical Combinations.
This Machine, when once wound up, performs all its different
Operations without being touch’d any more.
I forgot to tell you, that the Duck drinks, plays in the Water with
his Bill, and makes a gurgling Noise like a real living Duck. In
short, I have endeavor’d to make it imitate all the Actions of theliving Animal, which I have consider’d very attentively.
Unfortunately, only copies of the duck exist. The original was burned in a
museum in Nijninovgorod, Russia around 1879. You can watch, ANAS, amodern version, performing at http://www.automates-anciens.com/video 1/
duck automaton vaucanson 500.wmv.12 It is on exhibit in the Museum ofAutomatons in Grenoble and was designed and built in 1998 by Frédéric
Vidoni, a creator in mechanical arts. (See Fig. 1.3.)
Returning now to fictional automata, I’ll first mention the mechanical,
life-sized doll, Olympia, which sings and dances in Act I of Les Contes
d’Hoffmann (The Tales of Hoffmann) by Jacques Offenbach (1819–1880). In
the opera, Hoffmann, a poet, falls in love with Olympia, only to be crestfallen(and embarrassed) when she is smashed to pieces by the disgruntled Coppélius.

22
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1.0

Figure 1.3: Frédéric Vidoni’s ANAS, inspired by Vaucanson’s duck. (Photograph courtesy of Frédéric Vidoni.)

A play called R.U.R. (Rossum’s Universal Robots) was published by KarelC̆apek (pronounced CHAH pek), a Czech author and playwright, in 1920. (SeeFig. 1.4.) C̆apek is credited with coining the word “robot,” which in Czech
means “forced labor” or “drudgery.” (A “robotnik” is a peasant or serf.)
The play opened in Prague in January 1921. The Robots (always
capitalized in the play) are mass-produced at the island factory of Rossum’s
Universal Robots using a chemical substitute for protoplasm. According to a23
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1Dreams and Dreamers

Web site describing the play,13 “Robots remember everything, and think ofnothing new. According to Domin [the factory director] ‘They’d make fineuniversity professors.’ . . . once in a while, a Robot will throw down his workand start gnashing his teeth. The human managers treat such an event asevidence of a product defect, but Helena [who wants to liberate the Robots]
prefers to interpret it as a sign of the emerging soul.”
I won’t reveal the ending except to say that C̆apek did not look eagerly
on technology. He believed that work is an essential element of human life.
Writing in a 1935 newspaper column (in the third person, which was his habit)
he said: “With outright horror, he refuses any responsibility for the thoughtthat machines could take the place of people, or that anything like life, love, orrebellion could ever awaken in their cogwheels. He would regard this somber
vision as an unforgivable overvaluation of mechanics or as a severe insult tolife.”14

Figure 1.4: A scene from a New York production of R.U.R.
There is an interesting story, written by C̆apek himself, about how hecame to use the word robot in his play. While the idea for the play “was stillwarm he rushed immediately to his brother Josef, the painter, who wasstanding before an easel and painting away. . . . ‘I don’t know what to callthese artificial workers,’ he said. ‘I could call them Labori, but that strikes me
24
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1.0

NOTES

as a bit bookish.’ ‘Then call them Robots,’ the painter muttered, brush inmouth, and went on painting.”15
The science fiction (and science fact) writer Isaac Asimov wrote manystories about robots. His first collection, I, Robot, consists of nine stories
about “positronic” robots.16 Because he was tired of science fiction stories inwhich robots (such as Frankenstein’s creation) were destructive, Asimov’srobots had “Three Laws of Robotics” hard-wired into their positronic brains.
The three laws were the following:First Law: A robot may not injure a human being, or, through inaction,
allow a human being to come to harm.
Second Law: A robot must obey the orders given it by human beings
except where such orders would conflict with the First Law.
Third Law: A robot must protect its own existence as long as suchprotection does not conflict with the First or Second Law.
Asimov later added a “zeroth” law, designed to protect humanity’s interest:17
Zeroth Law: A robot may not injure humanity, or, through inaction, allow
humanity to come to harm.
The quest for artificial intelligence, quixotic or not, begins with dreams
like these. But to turn dreams into reality requires usable clues about how toproceed. Fortunately, there were many such clues, as we shall see.

Notes
1. The Iliad of Homer, translated by Richmond Lattimore, p. 386, Chicago: The
University of Chicago Press, 1951. (Paperback edition, 1961.) [19]2. Ovid, Metamorphoses, Book X, pp. 243–297, from an English translation, circa 1850.
See http://www.pygmalion.ws/stories/ovid2.htm. [19]3. Aristotle, The Politics, p. 65, translated by T. A. Sinclair, London: Penguin Books,
1981. [19]4. See E. Allison Peers, Fool of Love: The Life of Ramon Lull, London: S. C. M. Press,
Ltd., 1946. [20]5. See http://en.wikipedia.org/wiki/Leonardo’s robot. [20]6. Thomas Hobbes, The Leviathon, paperback edition, Kessinger Publishing, 2004. [20]7. George B. Dyson, Darwin Among the Machines: The Evolution of Global Intelligence,
p. 7, Helix Books, 1997. [21]8. For a Web site devoted to automata and music boxes, see
http://www.automates-anciens.com/english version/frames/english frames.htm. [21]9. From Jacques de Vaucanson, “An account of the mechanism of an automaton, or image
playing on the German-flute: as it was presented in a memoire, to the gentlemen of the25
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1

NOTES

Royal-Academy of Sciences at Paris. By M. Vaucanson . . . Together with a description of an
artificial duck. . . . .” Translated out of the French original, by J. T. Desaguliers, London,
1742. Available at http://e3.uci.edu/clients/bjbecker/NatureandArtifice/week5d.html. [21]10. http://www.automates-anciens.com/english version/automatons-music-boxes/
vaucanson-automatons-androids.php. [22]11. de Vaucanson, Jacques, op. cit. [22]12. I thank Prof. Barbara Becker of the University of California at Irvine for telling me about
the automates-anciens.com Web sites. [22]13. http://jerz.setonhill.edu/resources/RUR/index.html. [24]14. For a translation of the column entitled “The Author of Robots Defends Himself,” see
http://www.depauw.edu/sfs/documents/capek68.htm. [24]15. From one of a group of Web sites about C̆apek,
http://Capek.misto.cz/english/robot.html. See also http://Capek.misto.cz/english/. [25]16. The Isaac Asimov Web site, http://www.asimovonline.com/, claims that “Asimov did
not come up with the title, but rather his publisher ‘appropriated’ the title from a shortstory by Eando Binder that was published in 1939.” [25]17. See http://www.asimovonline.com/asimov FAQ.html#series13 for information about
the history of these four laws. [25]26
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2.1

Chapter 2

Clues
Clues about what might be needed to make machines intelligent are scatteredabundantly throughout philosophy, logic, biology, psychology, statistics, andengineering. With gradually increasing intensity, people set about to exploit
clues from these areas in their separate quests to automate some aspects ofintelligence. I begin my story by describing some of these clues and how theyinspired some of the first achievements in artificial intelligence.

2.1

From Philosophy and Logic

Although people had reasoned logically for millennia, it was the Greek
philosopher Aristotle who first tried to analyze and codify the process.
Aristotle identified a type of reasoning he called the syllogism “. . . in which,certain things being stated, something other than what is stated follows ofnecessity from their being so.”1
Here is a famous example of one kind of syllogism:2
1. All humans are mortal. (stated)
2. All Greeks are humans. (stated)
3. All Greeks are mortal. (result)
The beauty (and importance for AI) of Aristotle’s contribution has to dowith the form of the syllogism. We aren’t restricted to talking about humans,
Greeks, or mortality. We could just as well be talking about something else – aresult made obvious if we rewrite the syllogism using arbitrary symbols in theplace of humans, Greeks, and mortal. Rewriting in this way would produce
1. All B’s are A. (stated)
27
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2

Clues
2. All C’s are B’s. (stated)
3. All C’s are A. (result)

One can substitute anything one likes for A, B, and C. For example, allathletes are healthy and all soccer players are athletes, and therefore all soccer
players are healthy, and so on. (Of course, the “result” won’t necessarily betrue unless the things “stated” are. Garbage in, garbage out!)
Aristotle’s logic provides two clues to how one might automate reasoning.
First, patterns of reasoning, such as syllogisms, can be economically
represented as forms or templates. These use generic symbols, which can standfor many different concrete instances. Because they can stand for anything,
the symbols themselves are unimportant.
Second, after the general symbols are replaced by ones pertaining to a
specific problem, one only has to “turn the crank” to get an answer. The useof general symbols and similar kinds of crank-turning are at the heart of all
modern AI reasoning programs.
In more modern times, Gottfried Wilhelm Leibniz (1646–1716; Fig. 2.1)
was among the first to think about logical reasoning. Leibniz was a German
philosopher, mathematician, and logician who, among other things,
co-invented the calculus. (He had lots of arguments with Isaac Newton about
that.) But more importantly for our story, he wanted to mechanize reasoning.
Leibniz wrote3
It is unworthy of excellent men to lose hours like slaves in the laborof calculation which could safely be regulated to anyone else if
machines were used.
andFor if praise is given to the men who have determined the numberof regular solids. . . how much better will it be to bring undermathematical laws human reasoning, which is the most excellent
and useful thing we have.
Leibniz conceived of and attempted to design a language in which all
human knowledge could be formulated – even philosophical and metaphysicalknowledge. He speculated that the propositions that constitute knowledge
could be built from a smaller number of primitive ones – just as all words canbe built from letters in an alphabetic language. His lingua characteristica oruniversal language would consist of these primitive propositions, which would
comprise an alphabet for human thoughts.
The alphabet would serve as the basis for automatic reasoning. His idea
was that if the items in the alphabet were represented by numbers, then a28
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2.1

From Philosophy and Logic

Figure 2.1: Gottfried Leibniz.
complex proposition could be obtained from its primitive constituents bymultiplying the corresponding numbers together. Further arithmetic
operations could then be used to determine whether or not the complex
proposition was true or false. This whole process was to be accomplished by acalculus ratiocinator (calculus of reasoning). Then, when philosophers
disagreed over some problem they could say, “calculemus” (“let us calculate”).
They would first pose the problem in the lingua characteristica and then solve
it by “turning the crank” on the calculus ratiocinator.
The main problem in applying this idea was discovering the componentsof the primitive “alphabet.” However, Leibniz’s work provided important
additional clues to how reasoning might be mechanized: Invent an alphabet ofsimple symbols and the means for combining them into more complex
expressions.
Toward the end of the eighteenth century and the beginning of thenineteenth, a British scientist and politician, Charles Stanhope (Third Earl of
Stanhope), built and experimented with devices for solving simple problems inlogic and probability. (See Fig. 2.2.) One version of his “box” had slots on thesides into which a person could push colored slides. From a window on thetop, one could view slides that were appropriately positioned to represent a29
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2

Clues

specific problem. Today, we would say that Stanhope’s box was a kind of
analog computer.

Figure 2.2: The Stanhope Square Demonstrator, 1805. (Photograph courtesyof Science Museum/SSPL.)
The book Computing Before Computers gives an example of its
operation:4
To solve a numerical syllogism, for example:
Eight of ten A’s are B’s; Four of ten A’s are C’s;
Therefore, at least two B’s are C’s.
Stanhope would push the red slide (representing B) eight units
across the window (representing A) and the gray slide(representing C) four units from the opposite direction. The twounits that the slides overlapped represented the minimum numberof B’s that were also C’s.
···
In a similar way the Demonstrator could be used to solve a
traditional syllogism like:
No M is A; All B is M; Therefore, No B is A.
Stanhope was rather secretive about his device and didn’t want anyone toknow what he was up to. As mentioned in Computing Before Computers,
30
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2.1

From Philosophy and Logic“The few friends and relatives who received his privately distributed accountof the Demonstrator, The Science of Reasoning Clearly Explained Upon New
Principles (1800), were advised to remain silent lest ‘some bastard imitation’precede his intended publication on the subject.”
But no publication appeared until sixty years after Stanhope’s death.
Then, the Reverend Robert Harley gained access to Stanhope’s notes and oneof his boxes and published an article on what he called “The Stanhope
Demonstrator.”5Contrasted with Llull’s schemes and Leibniz’s hopes, Stanhope built thefirst logic machine that actually worked – albeit on small problems. Perhaps
his work raised confidence that logical reasoning could indeed be mechanized.
In 1854, the Englishman George Boole (1815–1864; Fig. 2.3) published abook with the title An Investigation of the Laws of Thought on Which Are
Founded the Mathematical Theories of Logic and Probabilities.6 Boole’s
purpose was (among other things) “to collect. . . some probable intimations
concerning the nature and constitution of the human mind.” Boole consideredvarious logical principles of human reasoning and represented them inmathematical form. For example, his “Proposition IV” states “. . . the principleof contradiction. . . affirms that it is impossible for any being to possess aquality, and at the same time not to possess it. . . .” Boole then wrote thisprinciple as an algebraic equation,x(1 − x) = 0,in which x represents “any class of objects,” (1 − x) represents the “contrary or
supplementary class of objects,” and 0 represents a class that “does not exist.”
In Boolean algebra, an outgrowth of Boole’s work, we would say that 0
represents falsehood, and 1 represents truth. Two of the fundamental
operations in logic, namely OR and AND, are represented in Boolean algebraby the operations + and ×, respectively. Thus, for example, to represent thestatement “either p or q or both,” we would write p + q. To represent thestatement “p and q,” we would write p × q. Each of p and q could be true or
false, so we would evaluate the value (truth or falsity) of p + q and p × q byusing definitions for how + and × are used, namely,
1 + 0 = 1,
1 × 0 = 0,
1 + 1 = 1,
1 × 1 = 1,
0 + 0 = 0,and0 × 0 = 0.
31
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Clues

Figure 2.3: George Boole.
Boolean algebra plays an important role in the design of telephoneswitching circuits and computers. Although Boole probably could not haveenvisioned computers, he did realize the importance of his work. In a letter
dated January 2, 1851, to George Thomson (later Lord Kelvin) he wrote7
I am now about to set seriously to work upon preparing for thepress an account of my theory of Logic and Probabilities which inits present state I look upon as the most valuable if not the only
valuable contribution that I have made or am likely to make to
Science and the thing by which I would desire if at all to beremembered hereafter. . .
Boole’s work showed that some kinds of logical reasoning could be
performed by manipulating equations representing logical propositions – avery important clue about the mechanization of reasoning. An essentiallyequivalent, but not algebraic, system for manipulating and evaluating
propositions is called the “propositional calculus” (often called “propositional
logic”), which, as we shall see, plays a very important role in artificial
intelligence. [Some claim that the Greek Stoic philospher Chrysippus (280–209bce) invented an early form of the propositional calculus.8 ]32
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2.2

From Life Itself

One shortcoming of Boole’s logical system, however, was that his
propositions p, q, and so on were “atomic.” They don’t reveal any entities
internal to propositions. For example, if we expressed the proposition “Jack ishuman” by p, and “Jack is mortal” by q, there is nothing in p or q to indicatethat the Jack who is human is the very same Jack who is mortal. For that, weneed, so to speak, “molecular expressions” that have internal elements.
Toward the end of the nineteenth century, the German mathematician,logician, and philosopher Friedrich Ludwig Gottlob Frege (1848–1925)
invented a system in which propositions, along with their internal components,
could be written down in a kind of graphical form. He called his language
Begriffsschrift, which can be translated as “concept writing.” For example, thestatement “All persons are mortal” would have been written in Begriffsschrift
something like the diagram in Fig. 2.4.9

Figure 2.4: Expressing “All persons are mortal” in Begriffsschrift.
Note that the illustration explicitly represents the x who is predicated to be a
person and that it is the same x who is then claimed to be mortal. It’s moreconvenient nowadays for us to represent this statement in the linear form(∀x)P (x)⊃M (x), whose English equivalent is “for all x, if x is a person, then x
is mortal.”Frege’s system was the forerunner of what we now call the “predicate
calculus,” another important system in artificial intelligence. It also
foreshadows another representational form used in present-day artificial
intelligence: semantic networks. Frege’s work provided yet more clues about
how to mechanize reasoning processes. At last, sentences expressing
information to be reasoned about could be written in unambiguous, symbolic
form.

2.2

From Life Itself

In Proverbs 6:6–8, King Solomon says “Go to the ant, thou sluggard; consider
her ways and be wise.” Although his advice was meant to warn against
slothfulness, it can just as appropriately enjoin us to seek clues from biology
about how to build or improve artifacts.
33
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Clues

Several aspects of “life” have, in fact, provided important clues about
intelligence. Because it is the brain of an animal that is responsible forconverting sensory information into action, it is to be expected that several
good ideas can be found in the work of neurophysiologists andneuroanatomists who study brains and their fundamental components,
neurons. Other ideas are provided by the work of psychologists who study (invarious ways) intelligent behavior as it is actually happening. And because,
after all, it is evolutionary processes that have produced intelligent life, those
processes too provide important hints about how to proceed.

2.2.1

Neurons and the Brain

In the late nineteenth and early twentieth centuries, the “neuron doctrine”
specified that living cells called “neurons” together with their interconnections
were fundamental to what the brain does. One of the people responsible forthis suggestion was the Spanish neuroanatomist Santiago Ramón y Cajal(1852–1934). Cajal (Fig. 2.5) and Camillo Golgi won the Nobel Prize inPhysiology or Medicine in 1906 for their work on the structure of the nervous
system.
A neuron is a living cell, and the human brain has about ten billion (1010 )of them. Although they come in different forms, typically they consist of acentral part called a soma or cell body, incoming fibers called dendrites, andone or more outgoing fibers called axons. The axon of one neuron has
projections called terminal buttons that come very close to one or more of thedendrites of other neurons. The gap between the terminal button of one
neuron and a dendrite of another is called a synapse. The size of the gap isabout 20 nanometers. Two neurons are illustrated schematically in Fig. 2.6.
Through electrochemical action, a neuron may send out a stream of pulses
down its axon. When a pulse arrives at the synapse adjacent to a dendrite ofanother neuron, it may act to excite or to inhibit electrochemical activity ofthe other neuron across the synapse. Whether or not this second neuron then“fires” and sends out pulses of its own depends on how many and what kindsof pulses (excitatory or inhibitory) arrive at the synapses of its various
incoming dendrites and on the efficiency of those synapses in transmitting
electrochemical activity. It is estimated that there are over half a trillion
synapses in the human brain. The neuron doctrine claims that the various
activities of the brain, including perception and thinking, are the result of allof this neural activity.
In 1943, the American neurophysiologist Warren McCulloch (1899–1969;
Fig. 2.7) and logician Walter Pitts (1923–1969) claimed that the neuron was,in essence, a “logic unit.” In a famous and important paper they proposed
simple models of neurons and showed that networks of these models could
perform all possible computational operations.10 The McCulloch–Pitts“neuron” was a mathematical abstraction with inputs and outputs
34
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2.2

From Life Itself

Figure 2.5: Ramón y Cajal.
(corresponding, roughly, to dendrites and axons, respectively). Each output
can have the value 1 or 0. (To avoid confusing a McCulloch–Pitts neuron witha real neuron, I’ll call the McCulloch–Pitts version, and others like it, a“neural element.”) The neural elements can be connected together into
networks such that the output of one neural element is an input to others andso on. Some neural elements are excitatory – their outputs contribute to“firing” any neural elements to which they are connected. Others are
inhibitory – their outputs contribute to inhibiting the firing of neural elements
to which they are connected. If the sum of the excitatory inputs less the sumof the inhibitory inputs impinging on a neural element is greater than acertain “threshold,” that neural element fires, sending its output of 1 to all of
the neural elements to which it is connected.
Some examples of networks proposed by McCullough and Pitts are shownin Fig. 2.8.
The Canadian neuropsychologist Donald O. Hebb (1904–1985) also
35
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Clues

Figure 2.6: Two neurons. (Adapted from Science, Vol. 316, p. 1416, 8 June
2007. Used with permission.)
believed that neurons in the brain were the basic units of thought. In an
influential book,11 Hebb suggested that “when an axon of cell A is nearenough to excite B and repeatedly or persistently takes part in firing it, some
growth process or metabolic change takes place in one or both cells such thatA’s efficiency, as one of the cells firing B, is increased.” Later, this so-called
Hebb rule of change in neural “synaptic strength” was actually observed inexperiments with living animals. (In 1965, the neurophysiologist Eric Kandel
published results showing that simple forms of learning were associated withsynaptic changes in the marine mollusk Aplysia californica. In 2000, Kandelshared the Nobel Prize in Physiology or Medicine “for their discoveries
concerning signal transduction in the nervous system.”)
Hebb also postulated that groups of neurons that tend to fire together
formed what he called cell assemblies. Hebb thought that the phenomenon of“firing together” tended to persist in the brain and was the brain’s way ofrepresenting the perceptual event that led to a cell-assembly’s formation. Hebb
said that “thinking” was the sequential activation of sets of cell assemblies.12

36
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2.2

From Life Itself

Figure 2.7: Warren McCulloch.

2.2.2

Psychology and Cognitive Science

Psychology is the science that studies mental processes and behavior. The
word is derived from the Greek words psyche, meaning breath, spirit, or soul,and logos, meaning word. One might expect that such a science ought to havemuch to say that would be of interest to those wanting to create intelligent
artifacts. However, until the late nineteenth century, most psychologicaltheorizing depended on the insights of philosophers, writers, and other astuteobservers of the human scene. (Shakespeare, Tolstoy, and other authors wereno slouches when it came to understanding human behavior.)
Most people regard serious scientific study to have begun with the37
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Clues

Figure 2.8: Networks of McCulloch–Pitts neural elements. (Adapted from Fig.
1 of Warren S. McCulloch and Walter Pitts, “A Logical Calculus of Ideas Immanent in Nervous Activity,” Bulletin of Mathematical Biophysics, Vol. 5, pp.
115–133, 1943.)
German Wilhelm Wundt (1832–1920) and the American William James(1842–1910).13 Both established psychology labs in 1875 – Wundt in Leipzig
and James at Harvard. According to C. George Boeree, who teaches thehistory of psychology at Shippensburg University in Pennsylvania, “Themethod that Wundt developed is a sort of experimental introspection: The
38
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2.2

From Life Itself

researcher was to carefully observe some simple event – one that could bemeasured as to quality, intensity, or duration – and record his responses tovariations of those events.” Although James is now regarded mainly as aphilosopher, he is famous for his two-volume book The Principles of
Psychology, published in 1873 and 1874.
Both Wundt and James attempted to say something about how the brain
worked instead of merely cataloging its input–output behavior. The
psychiatrist Sigmund Freud (1856–1939) went further, postulating internal
components of the brain, namely, the id, the ego, and the superego, and how
they interacted to affect behavior. He thought one could learn about thesecomponents through his unique style of guided introspection called
psychoanalysis.
Attempting to make psychology more scientific and less dependent on
subjective introspection, a number of psychologists, most famously B. F.
Skinner (1904–1990; Fig. 2.9), began to concentrate solely on what could beobjectively measured, namely, specific behavior in reaction to specific stimuli.
The behaviorists argued that psychology should be a science of behavior, notof the mind. They rejected the idea of trying to identify internal mental statessuch as beliefs, intentions, desires, and goals.

Figure 2.9: B. F. Skinner. (Photograph courtesy of the B. F. Skinner Foundation.)

39
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Clues

This development might at first be regarded as a step backward for people
wanting to get useful clues about the internal workings of the brain. In
criticizing the statistically oriented theories arising from “behaviorism,”
Marvin Minsky wrote “Originally intended to avoid the need for ‘meaning,’[these theories] manage finally only to avoid the possibility of explaining it.”14
Skinner’s work did, however, provide the idea of a reinforcing stimulus – onethat rewards recent behavior and tends to make it more likely to occur (undersimilar circumstances) in the future.
Reinforcement learning has become a popular strategy among AI
researchers, although it does depend on internal states. Russell Kirsch (circa
1930– ), a computer scientist at the U.S. National Bureau of Standards (nowthe National Institute for Standards and Technology, NIST), was one of thefirst to use it. He proposed how an “artificial animal” might use reinforcement
to learn good moves in a game. In some 1954 seminar notes he wrote thefollowing:15 “The animal model notes, for each stimulus, what move theopponent next makes, . . . Then, the next time that same stimulus occurs, theanimal duplicates the move of the opponent that followed the same stimulus
previously. The more the opponent repeats the same move after any given
stimulus, the more the animal model becomes ‘conditioned’ to that move.”
Skinner believed that reinforcement learning could even be used to
explain verbal behavior in humans. He set forth these ideas in his 1957 bookVerbal Behavior,16 claiming that the laboratory-based principles of selection
by consequences can be extended to account for what people say, write,
gesture, and think.
Arguing against Skinner’s ideas about language the linguist Noam
Chomsky (1928– ; Fig. 2.10), in a review17 of Skinner’s book, wrote that
careful study of this book (and of the research on which it draws)
reveals, however, that [Skinner’s] astonishing claims are far fromjustified. . . . the insights that have been achieved in thelaboratories of the reinforcement theorist, though quite genuine,
can be applied to complex human behavior only in the most grossand superficial way, and that speculative attempts to discuss
linguistic behavior in these terms alone omit from consideration
factors of fundamental importance. . .
How, Chomsky seems to ask, can a person produce a potentially infinite
variety of previously unheard and unspoken sentences having arbitrarily
complex structure (as indeed they can do) through experience alone? These“factors of fundamental importance” that Skinner omits are, according toChomsky, linguistic abilities that must be innate – not learned. He suggested
that “human beings are somehow specially created to do this, with
data-handling or ‘hypothesis-formulating’ ability of [as yet] unknown characterand complexity.” Chomsky claimed that all humans have at birth a “universal
40
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2.2

From Life Itself

Figure 2.10: Noam Chomsky. (Photograph by Don J. Usner.)grammar” (or developmental mechanisms for creating one) that accounts formuch of their ability to learn and use languages.18
Continuing the focus on internal mental processes and their limitations,the psychologist George A. Miller (1920– ) analyzed the work of several
experimenters and concluded that the “immediate memory” capacity ofhumans was approximately seven “chunks” of information.19 In the
introduction to his paper about this “magical number,” Miller humorously
notes “My problem is that I have been persecuted by an integer. For sevenyears this number has followed me around, has intruded in my most private
data, and has assaulted me from the pages of our most public journals. This
number assumes a variety of disguises, being sometimes a little larger andsometimes a little smaller than usual, but never changing so much as to be
unrecognizable. The persistence with which this number plagues me is far
more than a random accident.” Importantly, he also claimed that “the span of
immediate memory seems to be almost independent of the number of bits per
chunk.” That is, it doesn’t matter what a chunk represents, be it a single digit
in a phone number, a name of a person just mentioned, or a song title; we canapparently only hold seven of them (plus or minus two) in our immediate
41
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Clues

memory.
Miller’s paper on “The Magical Number Seven,” was given at a
Symposium on Information Theory held from September 10 to 12, 1956, at
MIT.20 Chomsky presented an important paper there too. It was entitled“Three Models for the Description of Language,” and in it he proposed afamily of rules of syntax he called phrase-structure grammars.21 It happensthat two pioneers in AI research (of whom we’ll hear a lot more later), Allen
Newell (1927–1992), then a scientist at the Rand Corporation, and Herbert
Simon (1916–2001), a professor at the Carnegie Institute of Technology (nowCarnegie Mellon University), gave a paper there also on a computer programthat could prove theorems in propositional logic. This symposium, bringing
together as it did scientists with these sorts of overlapping interests, is thought
to have contributed to the birth of cognitive science, a new discipline devoted
to the study of the mind. Indeed, George Miller wrote22
I went away from the Symposium with a strong conviction, moreintuitive than rational, that human experimental psychology,
theoretical linguistics, and computer simulation of cognitive
processes were all pieces of a larger whole, and that the futurewould see progressive elaboration and coordination of their sharedconcerns. . .
In 1960, Miller and colleagues wrote a book proposing a specific internalmechanism responsible for behavior, which they called the TOTE unit
(Test–Operate–Test–Exit).23 There is a TOTE unit corresponding to every
goal that an agent might have. Using its perceptual abilities, the unit firsttests whether or not its goal is satisfied. If so, the unit rests (exits). If not,some operation specific to achieving that goal is performed, and the test forgoal achievement is performed again, and so on repetitively until the goal
finally is achieved. As a simple example, consider the TOTE unit for driving anail with a hammer. So long as the nail is not completely driven in (the goal),
the hammer is used to strike it (the operation). Pounding stops (the exit)
when the goal is finally achieved. It’s difficult to say whether or not this bookinspired similar work by artificial intelligence researchers. The idea wasapparently “in the air,” because at about the same time, as we shall see later,some early work in AI used very similar ideas. [I can say that my work at SRI
with behavior (intermediate-level) programs for the robot, Shakey, and mylater work on what I called “teleo-reactive” programs were influenced byMiller’s ideas.]
Cognitive science attempted to explicate internal mental processes usingideas such as goals, memory, task queues, and strategies without (at least
during its beginning years) necessarily trying to ground these processes inneurophysiology.24 Cognitive science and artificial intelligence have beenclosely related ever since their beginnings. Cognitive science has provided
clues for AI researchers, and AI has helped cognitive science with newly
42
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2.2

From Life Itself

invented concepts useful for understanding the workings of the mind.

2.2.3

Evolution

That living things evolve gives us two more clues about how to build
intelligent artifacts. First, and most ambitiously, the processes of evolution
itself – namely, random generation and selective survival – might be simulatedon computers to produce the machines we dream about. Second, those paths
that evolution followed in producing increasingly intelligent animals can beused as a guide for creating increasingly intelligent artifacts. Start bysimulating animals with simple tropisms and proceed along these paths tosimulating more complex ones. Both of these strategies have been followedwith zest by AI researchers, as we shall see in the following chapters. Here, itwill suffice to name just a few initial efforts.
Early attempts to simulate evolution on a computer were undertaken at
Princeton’s Institute for Advanced Study by the viral geneticist Nils Aall
Barricelli (1912–1993). His 1954 paper described experiments in which
numbers migrated and reproduced in a grid.25
Motivated by the success of biological evolution in producing complex
organisms, some researchers began thinking about how programs could be
evolved rather than written. R. N. Friedberg and his IBM colleagues26
conducted experiments in which, beginning with a population of random
computer programs, they attempted to evolve ones that were more successfulat performing a simple logical task. In the summary of his 1958 paper,Friedberg wrote that “[m]achines would be more useful if they could learn toperform tasks for which they were not given precise methods. . . . It is
proposed that the program of a stored-program computer be gradually
improved by a learning procedure which tries many programs and chooses,
from the instructions that may occupy a given location, the one most often
associated with a successful result.” That is, Friedberg installed instructions
from “successful” programs into the programs of the next “generation,” muchas how the genes of individuals successful enough to have descendants are
installed in those descendants.
Unfortunately, Friedberg’s attempts to evolve programs were not very
successful. As Marvin Minsky pointed out,27
The machine [described in the first paper] did learn to solve someextremely simple problems. But it took of the order of 1000 times
longer than pure chance would expect. . . .
The second paper goes on to discuss a sequence ofmodifications. . . With these, and with some ‘priming’ (starting themachine off on the right track with some useful instructions), thesystem came to be only a little worse than chance.
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Clues

Minsky attributes the poor performance of Friedberg’s methods to thefact that each descendant machine differed very little from its parent, whereas
any helpful improvement would require a much larger step in the “space” ofpossible machines.
Other early work on artificial evolution was more successful. Lawrence
Fogel (1928–2007) and colleagues were able to evolve machines that could
make predictions of the next element in a sequence.28 Woodrow W. Bledsoe(1921–1995) at Panoramic Research and Hans J. Bremermann (1926–1969) atthe University of California, Berkeley, used simulated evolution to solve
optimization and mathematical problems, respectively.29 And Ingo
Rechenberg (according to one AI researcher) “pioneered the method of
artificial evolution to solve complex optimization tasks, such as the design of
optimal airplane wings or combustion chambers of rocket nozzles.”30
The first prominent work inspired by biological evolution was John
Holland’s development of “genetic algorithms” beginning in the early 1960s.
Holland (1929– ), a professor at the University of Michigan, used strings ofbinary symbols (0’s and 1’s), which he called “chromosomes” in analogy withthe genetic material of biological organisms. (Holland says he first came up
with the notion while browsing through the Michigan math library’s open
stacks in the early 1950s.)31 The encoding of 0’s and 1’s in a chromosome
could be interpreted as a solution to some given problem. The idea was toevolve chromosomes that were better and better at solving the problem.
Populations of chromosomes were subjected to an evolutionary process inwhich individual chromosomes underwent “mutations” (changing a component
1 to a 0 and vice versa), and pairs of the most successful chromosomes at eachstage of evolution were combined to make a new chromosome. Ultimately, theprocess would produce a population containing a chromosome (orchromosomes) that solved the problem.32
Researchers would ultimately come to recognize that all of theseevolutionary methods were elaborations of a very useful mathematical searchstrategy called “gradient ascent” or “hill climbing.” In these methods, one
searches for a local maximum of some function by taking the steepest possible
uphill steps. (When searching for a local minimum, the analogous method iscalled “gradient descent.”)
Rather than attempt to duplicate evolution itself, some researchers
preferred to build machines that followed along evolution’s paths toward
intelligent life. In the late 1940s and early 1950s, W. Grey Walter(1910–1977), a British neurophysiologist (born in Kansas City, Missouri), built
some machines that behaved like some of life’s most primitive creatures. They
were wheeled vehicles to which he gave the taxonomic name Machina
speculatrix (machine that looks; see Fig. 2.11).33 These tortoise-like machines
were controlled by “brains” consisting of very simple vacuum-tube circuitsthat sensed their environments with photocells and that controlled their wheel
motors. The circuits could be arranged so that a machine either moved toward
44
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2.2

From Life Itself

or away from a light mounted on a sister machine. Their behaviors seemedpurposive and often complex and unpredictable, so much so that Walter said
they “might be accepted as evidence of some degree of self-awareness.”
Machina speculatrix was the beginning of a long line of increasingly
sophisticated “behaving machines” developed by subsequent researchers.

Figure 2.11: Grey Walter (top left), his Machina speculatrix (top right), andits circuit diagram (bottom). (Grey Walter photograph from Hans Moravec,
ROBOT, Chapter 2: Caution! Robot Vehicle!, p. 18, Oxford: Oxford University
Press, 1998; “Turtle” photograph courtesy of National Museum of American
History, Smithsonian Institution; the circuit diagram is from W. Grey Walter,
The Living Brain, p. 200, London: Gerald Duckworth & Co., Ltd., 1953.)

2.2.4

Development and Maturation

Perhaps there are alternatives to rerunning evolution itself or to following its
paths toward increasing complexity from the most primitive animals. By
careful study of the behavior of young children, the Swiss psychologist Jean
Piaget proposed a set of stages in the maturation of their thinking abilities
45
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2

Clues

from infancy to adolescence.34 Might these stages provide a set of steps that
could guide designers of intelligent artifacts? Start with a machine that is able
to do what an infant can do, and then design machines that can mimic theabilities of children at each rung of the ladder. This strategy might be called“ontogenetic” to contrast it with the “phylogenetic” strategy of using
simlulated evolution.
Of course, it may be that an infant mind is far too complicated tosimulate and the processes of its maturation too difficult to follow. In any
case, this particular clue remains to be exploited.

2.2.5

Bionics

At a symposium in 1960, Major Jack E. Steele, of the Aerospace Division of
the United States Air Force, used the term “bionics” to describe the field thatlearns lessons from nature to apply to technology.35
Several bionics and bionics-related meetings were held during the 1960s.
At the 1963 Bionics Symposium, Leonard Butsch and Hans Oestreicher wrote“Bionics aims to take advantage of millions of years of evolution of living
systems during which they adapted themselves for optimum survival. One ofthe outstanding successes of evolution is the information processing capabilityof living systems [the study of which is] one of the principal areas of Bionics
research.”36
Today, the word “bionics” is concerned mainly with orthotic andprosthetic devices, such as artificial cochleas, retinas, and limbs. Nevertheless,as AI researchers continue their quest, the study of living things, theirevolution, and their development may continue to provide useful clues forbuilding intelligent artifacts.

2.3

From Engineering

2.3.1

Automata, Sensing, and Feedback

Machines that move by themselves and even do useful things by themselves
have been around for centuries. Perhaps the most common early examples are
the “verge-and-foliot” weight-driven clocks. (See Fig. 2.12.) These first
appeared in the late Middle Ages in the towers of large Italian cities. Theverge-and-foliot mechanism converted the energy of a falling weight into
stepped rotational motion, which could be used to move the clock hands.
Similar mechanisms were elaborated to control the actions of automata, suchas those of the Munich Glockenspiel.
One of the first automatic machines for producing goods was
Joseph-Marie Jacquard’s weaving loom, built in 1804. (See Fig. 2.13.) It
46
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Figure 2.12: A verge-and-foliot mechanism (left) and automata at the Munich
Glockenspiel (right).
followed a long history of looms and improved on the “punched card” design ofJacques de Vaucanson’s loom of 1745. (Vaucanson did more than build
mechanical ducks.) The punched cards of the Jacquard loom controlled theactions of the shuttles, allowing automatic production of fabric designs. Just a
few years after its invention, there were some 10,000 Jacquard looms weaving
away in France. The idea of using holes in paper or cards was later adopted byHerman Hollerith for tabulating the 1890 American census data and in player
pianos (using perforated rolls instead of cards). The very first factory “robots”of the so-called pick-and-place variety used only modest elaborations of thisidea.
It was only necessary to provide these early machines with an externalsource of energy (a falling weight, a wound-up spring, or humans pumping
pedals). Their behavior was otherwise fully automatic, requiring no humanguidance. But, they had an important limitation – they did not perceive
anything about their environments. (The punched cards that were “read” by
the Jacquard loom are considered part of the machine – not part of theenvironment.) Sensing the environment and then letting what is sensed
influence what a machine does is critical to intelligent behavior. Grey
Walters’s “tortoises,” for example, had photocells that could detect thepresence or absence of light in their environments and act accordingly. Thus,
they seem more intelligent than a Jacquard loom or clockwork automata.
One of the simplest ways to allow what is sensed to influence behavior
involves what is called “feedback control.” The word derives from feeding some
aspect of a machine’s behavior, say its speed of operation, back into the47
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Clues

Figure 2.13: Reconstruction of a Jacquard loom.
internals of the machine. If the aspect of behavior that is fed back acts todiminish or reverse that aspect, the process is called “negative feedback.” If,on the other hand, it acts to increase or accentuate that aspect of behavior, it
is called “positive feedback.” Both types of feedback play extremely importantroles in engineering.
48
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From Engineering

Negative feedback techniques have been used for centuries in mechanicaldevices. In 270 bce, a Greek inventor and barber, Ktesibios of Alexandria,
invented a float regulator to keep the water level in a tank feeding a water
clock at a constant depth by controlling the water flow into the tank.37 The
feedback device was a float valve consisting of a cork at the end of a rod. The
cork floated on the water in the tank. When the water level in the tank rose,
the cork would rise, causing the rod to turn off the water coming in. When thewater level fell, the cork would fall, causing the rod to turn on the water. The
water level in modern flush toilets is regulated in much the same way. In 250
bce, Philon of Byzantium used a similar float regulator to keep a constantlevel of oil in a lamp.38
The English clockmaker John Harrison (1693–1776) used a type ofnegative feedback control in his clocks. The ambient temperature of a clock
affects the length of its balance spring and thus its time-keeping accuracy.
Harrison used a bimetallic strip (sometimes a rod), whose curvature depends
on temperature. The strip was connected to the balance spring in such a waythat it produced offsetting changes in the length of the spring, thus making
the clock more independent of its temperature. The strip senses thetemperature and causes the clock to behave differently, and more accurately,
than it otherwise would. Today, such bimetallic strips see many uses, notably
in thermostats. (Dava Sobel’s 1995 book, Longitude: The True Story of aLone Genius Who Solved the Greatest Scientific Problem of His Time,
recounts the history of Harrison’s efforts to build a prize-winning clock for
accurate time-keeping at sea.)
Perhaps the most graphic use of feedback control is the centrifugal flyball
governor perfected in 1788 by James Watt for regulating the speed of his
steam engine. (See Fig. 2.14.) As the speed of the engine increases, the balls
fly outward, which causes a linking mechanism to decrease air flow, whichcauses the speed to decrease, which causes the balls to fall back inward, whichcauses the speed to increase, and so on, resulting in an equilibrium speed.
In the early 1940s, Norbert Wiener (1894–1964) and other scientists noted
similarities between the properties of feedback control systems in machines
and in animals. In particular, inappropriately applied feedback in control
circuits led to jerky movements of the system being controlled that weresimilar to pathological “tremor” in human patients. Arturo Rosenblueth,
Norbert Wiener, and Julian Bigelow coined the term “cybernetics” in a 1943
paper. Wiener’s book by that name was published in 1948. The word isrelated to the word “governor.” (In Latin gubernaculum means helm, andgubernator means helmsman. The Latin derives from the Greek kybernetike,which means the art of steersmanship.39 )Today, the prefix “cyber” is used to describe almost anything that deals
with computers, robots, the Internet, and advanced simulation. For example,
the author William Gibson coined the term “cyberspace” in his 1984 science
fiction novel Neuromancer. Technically, however, cybernetics continues to49
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Clues

Figure 2.14: Watt’s flyball governor.

describe activities related to feedback and control.40
The English psychiatrist W. Ross Ashby (1903–1972; Fig. 2.15)
contributed to the field of cybernetics by his study of “ultrastability” and“homeostasis.” According to Ashby, ultrastability is the capacity of a system
to reach a stable state under a wide variety of environmental conditions. To
illustrate the idea, he built an electromechanical device called the“homeostat.” It consisted of four pivoted magnets whose positions wererendered interdependent through feedback mechanisms. If the position of any
was disturbed, the effects on the others and then back on itself would result inall of them returning to an equilibrium condition. Ashby described this devicein Chapter 8 of his influential 1952 book Design For a Brain. His ideas had an
influence on several AI researchers. My “teleo-reactive programs,” to be

50
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From Engineering

described later, were motivated in part by the idea of homeostasis.

Figure 2.15: W. Ross Ashby, Warren McCulloch, Grey Walter, and Norbert
Wiener at a Meeting in Paris. (From P. de Latil, Thinking by Machine: A
Study of Cybernetics, Boston: Houghton, Mifflin, 1957.)
Another source of ideas, loosely associated with cybernetics and bionics,
came from studies of “self-organizing systems.” Many unorganizedcombinations of simple parts, including combinations of atoms and molecules,
respond to energetic “jostling” by falling into stable states in which the parts
are organized in more complex assemblies. An online dictionary devoted tocybernetics and systems theory has a nice example: “A chain made out of
paper clips suggests that someone has taken the trouble to link paper clips
together to make a chain. It is not in the nature of paper clips to makethemselves up into a chain. But, if you take a number of paper clips, open
them up slightly and then shake them all together in a cocktail shaker, youwill find at the end that the clips have organized themselves into short or long
chains. The chains are not so neat as chains put together by hand but,nevertheless, they are chains.”41
The term “self-organizing” seems to have been first introduced by Ashby
in 1947.42 Ashby emphasized that self-organization is not a property of anorganism itself, in response to its environment and experience, but a propertyof the organism and its environment taken together. Although self-organization
appears to be important in ideas about how life originated, it is unclear
whether or not it provides clues for building intelligent machines.
51
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Clues

2.3.2

Statistics and Probability

Because nearly all reasoning and decision making take place in the presence of
uncertainty, dealing with uncertainty plays an important role in theautomation of intelligence. Attempts to quantify uncertainty and “the laws ofchance” gave rise to statistics and probability theory. What would turn out tobe one of the most important results in probability theory, at least for artificial
intelligence, is Bayes’s rule, which I’ll define presently in the context of an
example. The rule is named for Reverend Thomas Bayes (1702–1761), an
English clergyman.43
One of the important applications of Bayes’s rule is in signal detection.
Let’s suppose a radio receiver is tuned to a station that after midnight
broadcasts (randomly) one of two tones, either tone A or tone B, and on aparticular night we want to decide which one is being broadcast. On any given
day, we do not know ahead of time which tone is to be broadcast that night,
but suppose we do know their probabilities. (For example, it might be thatboth tones are equally probable.) Can we find out which tone is being
broadcast by listening to the signal coming in to the receiver? Well, listening
can’t completely resolve the matter because the station is far away, andrandom noise partially obscures the tone. However, depending on the natureof the obscuring noise, we can often calculate the probability that the actual
tone that night is A (or that it is B). Let’s call the signal y and the actual
tone x (which can be either A or B). The probability that x = A, given theevidence for it contained in the incoming signal, y, is written as p(x = A | y)and read as “the probability that x is A, given that the signal is y.” Theprobability that x = B, given the same evidence is p(x = B | y).
A reasonable “decision rule” would be to decide in favor of tone A ifp(x = A | y) is larger than p(x = B | y). Otherwise, decide in favor of tone B.
(There is a straightforward adjustment to this rule that takes into accountdifferences in the “costs” of the two possible errors.) The problem in applying
this rule is that these two probabilities are not readily calculable, and that iswhere Bayes’s rule comes in. It allows us to calculate these probabilities interms of other probabilities that are more easily guessed or otherwise
obtainable. Specifically, Bayes’s rule isp(x | y) = p(y | x)p(x)/p(y).
Using Bayes’s rule, our decision rule can now be reformulated as
Decide in favor of tone A if p(y | x = A)p(x = A)/p(y) is greaterthan p(y | x = B)p(x = B)/p(y). Otherwise, decide in favor of tone
B.
Because p(y) occurs in both expressions and therefore does not affect which
one is larger, the rule simplifies to52
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Decide in favor of tone A if p(y | x = A)p(x = A) is greater thanp(y | x = B)p(x = B). Otherwise, decide in favor of tone B.

We assume that we know the a priori probabilities of the tones, namely,p(x = A) and p(x = B), so it remains only for us to calculate p(y | x) for
x = A and x = B. This expression is called the likelihood of y given x. Whenthe two probabilities, p(x = A) and p(x = B), are equal (that is, when bothtones are equally probable a priori), then we can decide in favor of which
likelihood is greater. Many decisions that are made in the presence of
uncertainty use this “maximum-likelihood” method . The calculation for theselikelihoods depends on how we represent the received signal, y, and on thestatistics of the interfering noise.
In my example, y is a radio signal, that is, a voltage varying in time. For
computational purposes, this time-varying voltage can be represented by asequence of samples of its values at appropriately chosen, uniformly spaced
time points, say y(t1 ), y(t2 ), . . . y(ti ), . . . , y(tN ). When noise alters thesevalues from what they would have been without noise, the probability of thesequence of them (given the cases when the tone is A and when the tone is B)
can be calculated by using the known statistical properties of the noise. I
won’t go into the details here except to say that, for many types of noise
statistics, these calculations are quite straightforward.
In the twentieth century, scientists and statisticians such as Karl Pearson(1857–1936), Sir Ronald A. Fisher (1890–1962), Abraham Wald (1902–1950),
and Jerzey Neyman (1894–1981) were among those who made important
contributions to the use of statistical and probabilistic methods in estimating
parameters and in making decisions. Their work set the foundation for some of
the first engineering applications of Bayes’s rule, such as the one I justillustrated, namely, deciding which, if any, of two or more electrical signals ispresent in situations where noise acts to obscure the signals. A paper by theAmerican engineers David Van Meter and David Middleton, which I read as abeginning graduate student in 1955, was my own introduction to theseapplications.44 For artificial intelligence, these uses of Bayes’s rule provided
clues about how to mechanize the perception of both speech sounds and visual
images. Beyond perception, Bayes’s rule lies at the center of much othermodern work in artificial intelligence.

2.3.3

The Computer

A. Early Computational Devices
Proposals such as those of Leibniz, Boole, and Frege can be thought of as earlyattempts to provide foundations for what would become the “software” ofartificial intelligence. But reasoning and all the other aspects of intelligent
behavior require, besides software, some sort of physical engine. In humans
53
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and other animals, that engine is the brain. The simple devices of Grey Walter
and Ross Ashby were, of course, physical manifestations of their ideas. And,
as we shall see, early networks of neuron-like units were realized in physicalform. However, to explore the ideas inherent in most of the clues from logic,
from neurophysiology, and from cognitive science, more powerful engines
would be required. While McCulloch, Wiener, Walter, Ashby, and others were
speculating about the machinery of intelligence, a very powerful and essentialmachine bloomed into existence – the general-purpose digital computer. This
single machine provided the engine for all of these ideas and more. It is by far
the dominant hardware engine for automating intelligence.
Building devices to compute has a long history. William Aspray hasedited an excellent book, Computing Before Computers, about computing’s
early days.45 The first machines were able to do arithmetic calculations, butthese were not programmable. Wilhelm Schickard (1592–1635; Fig. 2.16) built
one of the first of these in 1623. It is said to have been able to add andsubtract six-digit numbers for use in calculating astronomical tables. The
machine could “carry” from one digit to the next.
In 1642 Blaise Pascal (1623–1662; Fig. 2.16) created the first of about
fifty of his computing machines. It was an adding machine that could perform
automatic carries from one position to the next. “The device was contained ina box that was small enough to fit easily on top of a desk or small table. The
upper surface of the box. . . consisted of a number of toothed wheels, above
which were a series of small windows to show the results. In order to add anumber, say 3, to the result register, it was only necessary to insert a small
stylus into the toothed wheel at the position marked 3 and rotate the wheel
clockwise until the stylus encountered the fixed stop. . . ”46

Figure 2.16: Wilhelm Schickard (left) and Blaise Pascal (right).
54
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From Engineering

Inspired by Pascal’s machines, Gottfried Leibniz built a mechanicalmultiplier called the “Step Reckoner” in 1674. It could add, subtract, and do
multiplication (by repeated additions). “To multiply a number by 5, one
simply turned the crank five times.”47
Several other calculators were built in the ensuing centuries. A
particularly interesting one, which was too complicated to build in its day, wasdesigned in 1822 by Charles Babbage (1791–1871), an English mathematician
and inventor. (See Fig. 2.17.) Called the “Difference Engine,” it was to havecalculated mathematical tables (of the kind used in navigation at sea, forexample) using the method of finite differences. Babbage’s Difference Engine
No. 2 was actually constructed in 1991 (using Babbage’s designs andnineteenth-century mechanical tolerances) and is now on display at the
London Science Museum. The Museum arranged for another copy to be built
for Nathan Myhrvold, a former Microsoft Chief Technology Officer. (Adescription of the machine and a movie is available from a Computer History
Museum Web page at http://www.computerhistory.org/babbage/.)
Adding machines, however, can only add and subtract (and, by repetitionof these operations, also multiply and divide). These are important operations
but not the only ones needed. Between 1834 and 1837 Babbage worked on thedesign of a machine called the “Analytical Engine,” which embodied most of
the ideas needed for general computation. It could store intermediate results
in a “mill,” and it could be programmed. However, its proposed realization asa collection of steam-driven, interacting brass gears and cams ran into funding
difficulties and was never constructed.
Ada Lovelace (1815–1852), the daughter of Lord Byron, has been called
the “world’s first programmer” for her alleged role in devising programs for
the Analytical Engine. However, in the book Computing Before Computers
the following claim is made:48
This romantically appealing image is without foundation. All but
one of the programs cited in her notes [to her translation of an
account of a lecture Babbage gave in Turin, Italy] had been
prepared by Babbage from three to seven years earlier. The
exception was prepared by Babbage for her, although she diddetect a “bug” in it. Not only is there no evidence that Ada
Lovelace ever prepared a program for the Analytical Engine but
her correspondence with Babbage shows that she did not have theknowledge to do so.
For more information about the Analytical Engine and an emulator andprograms for it, see http://www.fourmilab.ch/babbage/.
Practical computers had to await the invention of electrical, rather thanbrass, devices. The first computers in the early 1940s used electromechanicalrelays. Vacuum tubes (thermionic valves, as they say in Britain) soon won out
55
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Clues

Figure 2.17: Charles Babbage (left) and a model of his Analytical Engine (right).
because they permitted faster and more reliable computation. Nowadays,
computers use billions of tiny transistors arrayed on silicon wafers. Who
knows what might someday replace them?
B. Computation Theory
Even before people actually started building computers, several logicians andmathematicians in the 1930s pondered the problem of just what could becomputed. Alonzo Church came up with a class of functions that could becomputed, ones he called “recursive.”49 The English logician andmathematician, Alan Turing (1912–1954; Fig. 2.18), proposed what is now
understood to be an equivalent class – ones that could be computed by animagined machine he called a “logical computing machine (LCM),” nowadays
called a “Turing machine.”50 (See Fig. 2.19.) The claim that these two notions
are equivalent is called the “Church–Turing Thesis.” (The claim has not been
proven, but it is strongly supported by logicians and no counterexample hasever been found.)51
The Turing machine is a hypothetical computational device that is quite
simple to understand. It consists of just a few parts. There is an infinite tape(which is one reason the device is just imagined and not actually built) divided
into cells and a tape drive. Each cell has printed on it either a 1 or a 0. The
machine also has a read–write head positioned over one cell of the tape. The
read function reads what is on the tape. There is also a logic unit that can
decide, depending on what is read and the state of the logic machine, to change
its own state, to command the write function to write either a 1 or a 0 on the56
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Figure 2.18: Alan Mathison Turing. (Photograph by Elliott & Fry c and usedwith permission of the National Portrait Gallery, London.)cell being read (possibly replacing what is already there), to move the tape one
cell to the left or to the right (at which time the new cell is read and so on), or
to terminate operation altogether. The input (the “problem” to be computed)
is written on the tape initially. (It turns out that any such input can be coded
into 1’s and 0’s.) When, and if, the machine terminates, the output (the coded“answer” to the input problem) ends up being printed on the tape.
Turing proved that one could always specify a particular logic unit (thepart that decides on the machine’s actions) for his machine such that themachine would compute any computable function. More importantly, heshowed that one could encode on the tape itself a prescription for any logic
unit specialized for a particular problem and then use a general-purpose logic
unit for all problems. The encoding for the special-purpose logic unit can bethought of as the “program” for the machine, which is stored on the tape (andthus subject to change by the very operation of the machine!) along with thedescription of the problem to be solved. In Turing’s words, “It can be shownthat a single special machine of that type can be made to do the work of all.
57
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Clues

Figure 2.19: A Turing machine.
It could in fact be made to work as a model of any other machine. The special
machine may be called the universal machine.”52
C. Digital Computers
Somewhat independently of Turing, engineers began thinking about how tobuild actual computing devices consisting of programs and logical circuitry for
performing the instructions contained in the programs. Some of the key ideas
for designing the logic circuits of computers were developed by the Americanmathematician and inventor Claude Shannon (1916–2001; Fig. 2.20).53 In his
1937 Yale University master’s thesis54 Shannon showed that Boolean algebra
and binary arithmetic could be used to simplify telephone switching circuits.
He also showed that switching circuits (which can be realized either by
combinations of relays, vacuum tubes, or whatever) could be used to
implement operations in Boolean logic, thus explaining their importance incomputer design.
It’s hard to know who first thought of the idea of storing a computer’s
program along with its data in the computer’s memory banks. Storing the
program allows changes in the program to be made easily, but more
58
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From Engineering

Figure 2.20: Claude Shannon. (Photograph courtesy of MIT Museum.)
importantly it allows the program to change itself by changing appropriate
parts of the memory where the program is stored. Among those who might
have thought of this idea first are the German engineer Konrad Zuse
(1910–1995) and the American computer pioneers J. Presper Eckert(1919–1995) and John W. Mauchly (1907–1980). (Of course Turing had
already proposed storing what amounted to a program on the tape of auniversal Turing machine.)
For an interesting history of Konrad Zuse’s contributions, see the familyof sites available fromhttp://irb.cs.tu-berlin.de/∼zuse/Konrad Zuse/en/index.html. One of thesementions that “it is undisputed that Konrad Zuse’s Z3 was the first fully
functional, program controlled (freely programmable) computer of the world.
. . . The Z3 was presented on May 12, 1941, to an audience of scientists in
Berlin.” Instead of vacuum tubes, it used 2,400 electromechanical relays. The
original Z3 was destroyed by an Allied air raid on December 21, 1943.55 A
59
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Clues

reconstructed version was built in the early 1960s and is now on display at theDeutsche Museum in Munich. Zuse also is said to have created the firstprogramming language, called the Plankalkül.
The American mathematician John von Neumann (1903–1957) wrote a“draft report” about the EDVAC, an early stored-program computer.56
Perhaps because of this report, we now say that these kinds of computers use
a “von Neumann architecture.” The ideal von Neumann architecture separates
the (task-specific) stored program from the (general-purpose) hardware
circuitry, which can execute (sequentially) the instructions of any programwhatsoever. (We usually call the program “software” to distinguish it from the“hardware” part of a computer. However, the distinction is blurred in mostmodern computers because they often have some of their programs built right
into their circuitry.)
Other computers with stored programs were designed and built in the
1940s in Germany, Great Britain, and the United States. They were large,
bulky machines. In Great Britain and the United States they were mainly
used for military purposes. Figure 2.21 shows one such machine.

Figure 2.21: The Cambridge University EDSAC computer (circa 1949). (Photograph used with permission of the Computer Laboratory, University of Cambridge c .)60
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From Engineering

We call computers “machines” even though today they can be madecompletely electrical with no moving parts whatsoever. Furthermore, when we
speak of computing machines we usually mean the combination of thecomputer and the program it is running. Sometimes we even call just the
program a machine. (As an example of this usage, I’ll talk later about a“checker-playing machine” and mean a program that plays checkers.)
The commanding importance of the stored-program digital computerderives from the fact that it can be used for any purpose whatsoever – that is,of course, any computational purpose. The modern digital computer is, for allpractical purposes, such a universal machine. The “all-practical-purposes”
qualifier is needed because not even modern computers have the infinite
storage capacity implied by Turing’s infinite tape. However, they do haveprodigious amounts of storage, and that makes them practically universal.
D. “Thinking” Computers
After some of the first computers were built, Turing reasoned that if they werepractically universal, they should be able to do anything. In 1948 he wrote,“The importance of the universal machine is clear. We do not need to have an
infinity of different machines doing different jobs. A single one will suffice. The
engineering problem of producing various machines for various jobs is replacedby the office work of ‘programming’ the universal machine to do these jobs.”57
Among the things that Turing thought could be done by computers was
mimicking human intelligence. One of Turing’s biographers, Andrew Hodges,
claims, “he decided the scope of the computable encompassed far more than
could be captured by explicit instruction notes, and quite enough to include allthat human brains did, however creative or original. Machines of sufficientcomplexity would have the capacity for evolving into behaviour that had neverbeen explicitly programmed.”58
The first modern article dealing with the possibility of mechanizing all of
human-style intelligence was published by Turing in 1950.59 This paper isfamous for several reasons. First, Turing thought that the question “Can amachine think?” was too ambiguous. Instead he proposed that the matter ofmachine intelligence be settled by what has come to be called “the Turing
test.”
Although there have been several reformulations (mostly simplifications)of the test, here is how Turing himself described it:
The new form of the problem [Can machines think?] can be
described in terms of a game which we call the “imitation game.”
It is played with three people, a man (A), a woman (B), and aninterrogator (C) who may be of either sex. The interrogator stays
in a room apart from the other two. The object of the game for the61
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2

Clues
interrogator is to determine which of the other two is the man andwhich is the woman. He knows them by labels X and Y, and at theend of the game he says either “X is A and Y is B” or “X is B and
Y is A.” The interrogator is allowed to put questions to A and B
thus:
C: Will X please tell me the length of his or her hair?
Now suppose X is actually A, then A must answer. It is A’s object
in the game to try and cause C to make the wrong identification.
His answer might therefore be“My hair is shingled, and the longest strands are about nine inches
long.”In order that tones of voice may not help the interrogator theanswers should be written, or better still, typewritten. The ideal
arrangement is to have a teleprinter communicating between thetwo rooms. Alternatively the question and answers can be repeatedby an intermediary. The object of the game for the third player(B) is to help the interrogator. The best strategy for her isprobably to give truthful answers. She can add such things as “I
am the woman, don’t listen to him!” to her answers, but it willavail nothing as the man can make similar remarks.
We now ask the question, “What will happen when a machine
takes the part of A in this game?” Will the interrogator decide
wrongly as often when the game is played like this as he does when
the game is played between a man and a woman? These questions
replace our original, “Can machines think?”

Most of the simplifications of Turing’s test involve attempting to have acomputer “in the other room” fool an interrogator into believing the computeris a human. To stimulate progress toward that goal, a prize has been
established – annual prizes in fact. In the words of the Web site announcing
the prizes, “In 1990 Hugh Loebner agreed with The Cambridge Center for
Behavioral Studies to underwrite a contest designed to implement the Turing
Test. Dr. Loebner pledged a Grand Prize of $100,000 and a Gold Medal forthe first computer whose responses were indistinguishable from a human’s.
Such a computer can be said ‘to think.’ Each year an annual prize of $2000
and a bronze medal is awarded to the most human-like computer. The winnerof the annual contest is the best entry relative to other entries that year,
irrespective of how good it is in an absolute sense.”60
However, several people have pointed to inadequacies of both thesimplifications and even of the original test.61 In fact, it has been alleged thateven Joseph Weizenbaum’s patently unintelligent 1966 conversational
program, ELIZA, has fooled people into thinking it is human.62
For example, here is part of a transcript of a conversation with ELIZA:
62
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From Engineering
ELIZA: How do you do. Please state your problem.

> I spend too much time with computers.
ELIZA: Why do you mention computers?

> You are one.
ELIZA: Does it please you to believe I am one?

> Yes.
ELIZA: You seem to be quite positive.

> I suppose I am.
ELIZA: How long have you been?

> 31 years.
ELIZA: I’m not sure I understand you fully.

> Join the club.
ELIZA: Please go on.

> You don’t make too much sense yourself.
ELIZA: We were discussing you – not me.

The first part of the conversation seems reasonable, but ELIZA bogs downin the middle because the program was expecting “I suppose I am” to befollowed by some word like “happy.” (There are several ELIZA simulations on
the Web. One that claims to reproduce faithfully the original ELIZA programis at http://www.chayden.net/eliza/Eliza.html. Try one out!)
A second important feature of Turing’s 1950 paper was his handling ofarguments that people might raise against the possibility of achieving
intelligent computers. I’ll quote the ones Turing mentions:(1) The Theological Objection: Thinking is a function of man’s
immortal soul. God has given an immortal soul to every man andwoman, but not to any other animal or to machines. Hence noanimal or machine can think.
(2) The ‘Heads in the Sand’ Objection: “The consequences ofmachines thinking would be too dreadful. Let us hope and believethat they cannot do so.”(3) The Mathematical Objection: There are a number of results ofmathematical logic that can be used to show that there arelimitations to the powers of discrete-state machines.
(4) The Argument from Consciousness: This argument is very well
expressed in Professor Jefferson’s Lister Oration for 1949, fromwhich I quote:“Not until a machine can write a sonnet or compose a concerto
because of thoughts and emotions felt, and not by the chance fall
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2

Clues
of symbols, could we agree that machine equals brain – that is, notonly write it but know that it had written it. No mechanism could
feel (and not merely artificially signal, an easy contrivance)
pleasure at its successes, grief when its valves fuse, be warmed byflattery, be made miserable by its mistakes, be charmed by sex, be
angry or depressed when it cannot get what it wants.”(5) Arguments from Various Disabilities: These arguments take theform, “I grant you that you can make machines do all the things
you have mentioned but you will never be able to make one to do
X.”(6) Lady Lovelace’s Objection: Our most detailed information ofBabbage’s Analytical Engine comes from a memoir by Lady
Lovelace. In it she states, “The Analytical Engine has nopretensions to originate anything. It can do whatever we know how
to order it to perform” (her italics).
(7) Argument from Continuity in the Nervous System: The
nervous system is certainly not a discrete-state machine. A small
error in the information about the size of a nervous impulse
impinging on a neuron may make a large difference to the size ofthe outgoing impulse. It may be argued that, this being so, one
cannot expect to be able to mimic the behavior of the nervous
system with a discrete-state system.
(8) The Argument from Informality of Behavior: It is not possible
to produce a set of rules purporting to describe what a man should
do in every conceivable set of circumstances.
(9) The Argument from Extra-Sensory Perception.

In his paper, Turing nicely (in my opinion) handles all of these points,
with the possible exception of the last one (because he apparently thoughtthat extra-sensory perception was plausible). I’ll leave it to you to readTuring’s 1950 paper to see his counterarguments.
The third important feature of Turing’s 1950 paper is his suggestion
about how we might go about producing programs with human-level
intellectual abilities. Toward the end of his paper, he suggests, “Instead oftrying to produce a programme to simulate the adult mind, why not rather try
to produce one which simulates the child’s? If this were then subjected to an
appropriate course of education one would obtain the adult brain.” Thissuggestion is really the source for the idea mentioned earlier about using an
ontogenetic strategy to develop intelligent machines.
Allen Newell and Herb Simon (see Fig. 2.22) were among those who had
no trouble believing that the digital computer’s universality meant that it
could be used to mechanize intelligence in all its manifestations – provided it
had the right software. In their 1975 ACM Turing Award lecture,63 they64
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2.3

From Engineering

described a hypothesis that they had undoubtedly come to believe muchearlier, the “Physical Symbol System Hypothesis.” It states that “a physicalsymbol system has the necessary and sufficient means for intelligent action.”
Therefore, according to the hypothesis, appropriately programmed digitalcomputers would be capable of intelligent action. Conversely, because humans
are capable of intelligent action, they must be, according to the hypothesis,
physical symbol systems. These are very strong claims that continue to bedebated.

Figure 2.22: Herbert Simon (seated) and Allen Newell (standing). (Courtesy ofCarnegie Mellon University Archives.)
Both the imagined Turing machine and the very real digital computer are
symbol systems in the sense Newell and Simon meant the phrase. How can aTuring machine, which uses a tape with 0’s and 1’s printed on it, be a “symbol
system”? Well, the 0’s and 1’s printed on the tape can be thought of as
symbols standing for their associated numbers. Other symbols, such as “A”
and “M,” can be encoded as sequences of primitive symbols, such as 0’s and1’s. Words can be encoded as sequences of letters, and so on. The fact that
one commonly thinks of a digital computer as a machine operating on 0’s and
1’s need not prevent us from thinking of it also as operating on more complex
symbols. After all, we are all used to using computers to do “word processing”
and to send e-mail.

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Newell and Simon admitted that their hypothesis could indeed be false:“Intelligent behavior is not so easy to produce that any system will exhibit itwilly-nilly. Indeed, there are people whose analyses lead them to conclude
either on philosophical or on scientific grounds that the hypothesis is false.
Scientifically, one can attack or defend it only by bringing forth empiricalevidence about the natural world.” They conclude the following:
The symbol system hypothesis implies that the symbolic behaviorof man arises because he has the characteristics of a physicalsymbol system. Hence, the results of efforts to model humanbehavior with symbol systems become an important part of theevidence for the hypothesis, and research in artificial intelligencegoes on in close collaboration with research in information
processing psychology, as it is usually called.
Although the hypothesis was not formally described until it appeared in
the 1976 article, it was certainly implicit in what Turing and other researchers
believed in the 1950s. After Allen Newell’s death, Herb Simon wrote, “Fromthe very beginning something like the physical symbol system hypothesis wasembedded in the research.”64
Inspired by the clues we have mentioned and armed with thegeneral-purpose digital computer, researchers began, during the 1950s, toexplore various paths toward mechanizing intelligence. With a firm belief inthe symbol system hypothesis, some people began programming computers toattempt to get them to perform some of the intellectual tasks that humans
could perform. Around the same time, other researchers began exploring
approaches that did not depend explicitly on symbol processing. They took
their inspiration mainly from the work of McCulloch and Pitts on networks ofneuron-like units and from statistical approaches to decision making. A split
between symbol-processing methods and what has come to be called“brain-style” and “nonsymbolic” methods still survives today.

Notes
1. Aristotle, Prior Analytics, Book I, written circa 350 bce, translated by A. J. Jenkinson,
Web addition published by eBooks@Adelaide, available online at
http://etext.library.adelaide.edu.au/a/aristotle/a8pra/. [27]2. Medieval students of logic gave names to the different syllogisms they studied. They
used the mnemonic Barbara for this one because each of the three statements begins with
“All,” whose first letter is “A.” The vowels in “Barbara” are three“a”s. [27]3. From Martin Davis, The Universal Computer: The Road from Leibniz to Turing, New
York: W. W. Norton & Co., 2000. For an excerpt from the paperback version containing
this quotation, see http://www.wwnorton.com/catalog/fall01/032229EXCERPT.htm. [28]4. Quotation from William Aspray (ed.), Computing Before Computers, Chapter 3, “Logic

66
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2.3

NOTES

Machines,” pp. 107–8, Ames, Iowa: Iowa State Press, 1990. (Also available fromhttp://ed-thelen.org/comp-hist/CBC.html.) [30]5. Robert Harley, “The Stanhope Demonstrator, Mind, Vol. IV, pp. 192–210, 1879. [31]6. George Boole, An Investigation of the Laws of Thought on Which are Founded theMathematical Theories of Logic and Probabilities, Dover Publications, 1854. [31]7. See D. McHale, George Boole: His Life and Work, Dublin, 1985. This excerpt was takenfrom http://www-groups.dcs.st-and.ac.uk/∼history/Mathematicians/Boole.html. [32]8. See, for example, Gerard O’Regan, A Brief History of Computing, p. 17, London:
Springer-Verlag, 2008. [32]9. I follow the pictorial version used in the online Stanford Encyclopedia of Philosophy(http://plato.stanford.edu/entries/frege/), which states that “. . . we are modifying Frege’s
notation a bit so as to simplify the presentation; we shall not use the special typeface(Gothic) that Frege used for variables in general statements, or observe some of the special
conventions that he adopted. . . .” [33]10. Warren S. McCulloch and Walter Pitts, “A Logical Calculus of Ideas Immanent in
Nervous Activity,” Bulletin of Mathematical Biophysics, Vol. 5, pp. 115–133, Chicago:
University of Chicago Press, 1943. (See Marvin Minsky, Computation: Finite and Infinite
Machines, Englewood Cliffs, NJ: Prentice-Hall, 1967, for a very readable treatment of thecomputational aspects of “McCulloch–Pitts neurons.”) [34]
11. Donald O. Hebb, The Organization of Behavior: A Neuropsychological Theory, New
York: John Wiley, Inc., 1949. [36]12. For more about Hebb, see http://www.cpa.ca/Psynopsis/special eng.html. [36]13. For a summary of the lives and work of both men, see a Web page entitled “Wilhelm
Wundt and William James” by Dr. C. George Boeree at
http://www.ship.edu/∼cgboeree/wundtjames.html. [38]14. M. Minsky (ed.), “Introduction,” Semantic Information Processing, p. 2, Cambridge,
MA: MIT Press, 1968. [40]15. Russell A. Kirsch, “Experiments with a Computer Learning Routine,” Computer
Seminar Notes, July 30, 1954. Available online at
http://www.nist.gov/msidlibrary/doc/kirsch 1954 artificial.pdf. [40]16. B. F. Skinner, Verbal Behavior, Engelwood Cliffs, NJ: Prentice Hall, 1957. [40]17. Noam Chomsky, “A Review of B. F. Skinner’s Verbal Behavior,” in Leon A. Jakobovits
and Murray S. Miron (eds.), Readings in the Psychology of Language, Engelwood Cliffs, NJ:
Prentice-Hall, 1967. Available online at http://www.chomsky.info/articles/1967----.htm.
[40]
18. See, for example, N. Chomsky, Aspects of the Theory of Syntax, Cambridge: MIT Press,
1965. [41]19. George A. Miller, “The Magical Number Seven, Plus or Minus Two: Some Limits on
Our Capacity for Processing Information,” The Psychological Review, Vol. 63, pp. 81–97,
1956. [41]20. IRE Transactions on Information Theory, Vol IT-2, 1956. [42]21. For a copy of his paper, see http://www.chomsky.info/articles/195609--.pdf. [42]22. George A. Miller, “A Very Personal History,” MIT Center for Cognitive Science
Occasional Paper No. 1, 1979. [42]23. George A. Miller, E. Galanter, and K. H. Pribram, Plans and the Structure of Behavior,
New York: Holt, Rinehart & Winston, 1960. [42]24. For a thorough history of cognitive science, see Margaret A. Boden, Mind As Machine:

67
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A History of Cognitive Science, vols. 1 and 2, Oxford: Clarendon Press, 2006. For an
earlier, one-volume treatment, see Howard E. Gardner, The Mind’s New Science: A Historyof the Cognitive Revolution, New York: Basic Books, 1985. [42]25. An English translation appeared later: N.A. Barricelli, “Symbiogenetic Evolution
Processes Realized by Artificial Methods,” Methodos, Vol. 9, Nos. 35–36, pp. 143–182, 1957.
For a summary of Barricelli’s experiments, see David B. Fogel, “Nils Barricelli – Artificial
Life, Coevolution, Self-Adaptation,” IEEE Computational Intelligence Magazine, Vol. 1, No.
1, pp. 41–45, February 2006. [43]26. R. M. Friedberg, “A Learning Machine: Part I,” IBM Journal of Research andDevelopment, Vol. 2, No. 1, pp. 2–13, 1958, and R. M. Friedberg, B. Dunham, and J. H.
North, “A Learning Machine: Part II,” IBM Journal of Research and Development, Vol. 3,
No. 3, pp. 282–287, 1959. The papers are available (for a fee) at
http://www.research.ibm.com/journal/rd/021/ibmrd0201B.pdf andhttp://www.research.ibm.com/journal/rd/033/ibmrd0303H.pdf. [43]27. Marvin L. Minsky, “Steps Toward Artificial Intelligence,” Proceedings of the Institute ofRadio Engineers, Vol. 49, pp. 8–30, 1961. Paper available at
http://web.media.mit.edu/∼minsky/papers/steps.html. [43]28. Lawrence J. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence throughSimulated Evolution, New York: Wiley, 1966. [44]29. Woodrow W. Bledsoe, “The Evolutionary Method in Hill Climbing: Convergence
Rates,” Technical Report, Panoramic Research, Inc., Palo Alto, CA, 1962.; Hans J.
Bremermann, “Optimization through Evolution and Recombination, M. C. Yovits, G. T.
Jacobi, and G. D. Goldstein (eds.), Self-Organizing Systems, pp. 93–106, Washington, DC:
Spartan Books, 1962. [44]30. Jürgen Schmidhuber, “2006: Celebrating 75 Years of AI – History and Outlook: The
Next 25 Years,” in Max Lungarella et al. (eds.), 50 Years of Artificial Intelligence: Essays
Dedicated to the 50th Anniversary of Artificial Intelligence, Berlin: Springer-Verlag, 2007.
Schmiduber cites Ingo Rechenberg, “Evolutionsstrategie – Optimierung Technischer Systeme
Nach Prinzipien der Biologischen Evolution,” Ph.D. dissertation, 1971 (reprinted by
Frommann-Holzboog Verlag, Stuttgart, 1973). [44]31. See http://www.aaai.org/AITopics/html/genalg.html. [44]32. John H. Holland, Adaptation in Natural and Artificial Systems, Ann Arbor: The
University of Michigan Press, 1975. Second edition, MIT Press, 1992. [44]33. W. Grey Walter, “An Imitation of Life,” Scientific American, pp. 42–45, May 1950. See
also W. Grey Walter, The Living Brain, London: Gerald Duckworth & Co. Ltd., 1953. [44]34. B. Inhelder and J. Piaget, The Growth of Logical Thinking from Childhood toAdolescence, New York: Basic Books, 1958. For a summary of these stages, see the following
Web pages: http://www.childdevelopmentinfo.com/development/piaget.shtml andhttp://www.ship.edu/∼cgboeree/piaget.html. [46]35. Proceedings of the Bionics Symposium: Living Prototypes – the Key to new Technology,
Technical Report 60-600, Wright Air Development Division, Dayton, Ohio, 1960. [46]36. Proceedings of the Third Bionics Symposium, Aerospace Medical Division, Air Force
Systems Command, United States Air Force, Wright-Patterson AFB, Ohio, 1963. [46]37. http://www.mlahanas.de/Greeks/Ctesibius1.htm. [49]38. http://www.asc-cybernetics.org/foundations/timeline.htm. [49]39. From http://www.nickgreen.pwp.blueyonder.co.uk/control.htm. [49]40. For a history of cybernetics, see a Web page of the American Society for Cybernetics at
http://www.asc-cybernetics.org/foundations/history.htm. [50]68
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2.3

NOTES

41. From http://pespmc1.vub.ac.be/ASC/SELF-ORGANI.html. [51]42. W. Ross Ashby, “Principles of the Self-Organizing Dynamic System,” Journal of
General Psychology, Vol. 37, pp. 125–128, 1947. See also the Web pages at
http://en.wikipedia.org/wiki/Self organization. [51]43. Bayes wrote an essay that is said to have contained a version of the rule. Later, theMarquis de Laplace (1749–1827) generalized (some say independently) what Bayes had
done. For a version of Bayes’s essay (posthumously written up by Richard Price), see
http://www.stat.ucla.edu/history/essay.pdf. [52]44. David Van Meter and David Middleton, “Modern Statistical Approaches to Reception in
Communication Theory,” Symposium on Information Theory, IRE Transactions on
Information Theory, PGIT-4, pp. 119–145, September 1954. [53]45. William Aspray (ed.), Computing Before Computers, Ames, Iowa: Iowa State University
Press, 1990. Available online at http://ed-thelen.org/comp-hist/CBC.html. [54]46. Ibid, Chapter 1. [54]47. Ibid. [55]48. Ibid, Chapter 2. [55]49. Alonzo Church, “An Unsolvable Problem of Elementary Number Theory,” American
Journal of Mathematics, Vol. 58, pp. 345–363, 1936. [56]50. Alan M. Turing, “On Computable Numbers, with an Application to the
Entscheidungsproblem,” Proceedings of the London Mathematical Society, Series 2, Vol. 42,
pp. 230–265, 1936–1937. [56]51. For more information about Turing, his life and works, see the Web pages maintained by
the Turing biographer, Andrew Hodges, at http://www.turing.org.uk/turing/. [56]52. The quotation is from Alan M. Turing, “Lecture to the London Mathematical Society,”p. 112, typescript in King’s College, Cambridge, published in Alan M. Turing’s ACE Reportof 1946 and Other Papers (edited by B. E. Carpenter and R. W. Doran, Cambridge, MA:
MIT Press, 1986), and in Volume 3 of The Collected Works of A. M. Turing (edited D. C.
Ince, Amsterdam: North-Holland 1992). [58]53. For a biographical sketch, see
http://www.research.att.com/∼njas/doc/shannonbio.html. [58]54. In his book The Mind’s New Science, Howard Gardner called this thesis “possibly themost important, and also the most famous, master’s thesis of the century.” [58]55. Various sources give different dates for the air raid, but a letter in the possession of
Zuse’s son, Horst Zuse, gives the 1943 date (according to an e-mail sent me on February 10,
2009, by Wolfgang Bibel, who has communicated with Horst Zuse). [59]56. A copy of the report, plus introductory commentary, can be found at
http://qss.stanford.edu/∼godfrey/. [60]57. Alan M. Turing, “Intelligent Machinery,” National Physical Laboratory Report, 1948.
Reprinted in B. Meltzer and D. Michie (eds), Machine Intelligence 5, Edinburgh: Edinburgh
University Press, 1969. A facsimile of the report is available online at
http://www.AlanTuring.net/intelligent machinery. [61]58. Andrew Hodges, Turing, London: Phoenix, 1997. [61]59. Alan M. Turing, “Computing Machinery and Intelligence,” Mind, Vol. LIX, No. 236, pp.
433–460, October 1950. (Available at http://www.abelard.org/turpap/turpap.htm.) [61]60. See the “Home Page of the Loebner Prize in Artificial Intelligence” at
http://www.loebner.net/Prizef/loebner-prize.html. [62]61. For discussion, see the Wikipedia article at http://en.wikipedia.org/wiki/Turing test.

69
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2

NOTES

[62]
62. Joseph Weizenbaum, “ELIZA—A Computer Program for the Study of Natural
Language Communication between Man and Machine,” Communications of the ACM, Vol.
9, No. 1, pp. 36–35, January 1966. Available online at
http://i5.nyu.edu/∼mm64/x52.9265/january1966.html. [62]63. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols
and Search,” Communications of the ACM, Vol. 19, No. 3, pp. 113–126, March 1976. [64]64. National Academy of Sciences, Biographical Memoirs, Vol. 71, 1997. Available online at
http://www.nap.edu/catalog.php?record id=5737. [66]70
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2.3

Part II

Early Explorations: 1950s
and 1960s

If machines are to become intelligent, they must, at the very least, be able
to do the thinking-related things that humans can do. The first steps then in
the quest for artificial intelligence involved identifying some specific tasks
thought to require intelligence and figuring out how to get machines to dothem. Solving puzzles, playing games such as chess and checkers, proving
theorems, answering simple questions, and classifying visual images wereamong some of the problems tackled by the early pioneers during the 1950s
and early 1960s. Although most of these were laboratory-style, sometimes
called “toy,” problems, some real-world problems of commercial importance,such as automatic reading of highly stylized magnetic characters on bankchecks and language translation, were also being attacked. (As far as I know,
Seymour Papert was the first to use the phrase “toy problem.” At a 1967 AI
workshop I attended in Athens, Georgia, he distinguished among tau or “toy”
problems, rho or real-world problems, and theta or “theory” problems in
artificial intelligence. This distinction still serves us well today.)In this part, I’ll describe some of the first real efforts to build intelligent
machines. Some of these were discussed or reported on at conferences andsymposia – making these meetings important milestones in the birth of AI. I’ll
also do my best to explain the underlying workings of some of these early AI
programs. The rather dramatic successes during this period helped toestablish a solid base for subsequent artificial intelligence research.
Some researchers became intrigued (one might even say captured) by themethods they were using, devoting themselves more to improving the powerand generality of their chosen techniques than to applying them to the tasks
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2thought to require them. Moreover, because some researchers were just as
interested in explaining how human brains solved problems as they were ingetting machines to do so, the methods being developed were often proposed
as contributions to theories about human mental processes. Thus, research incognitive psychology and research in artificial intelligence became highlyintertwined.

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3.1

Chapter 3

Gatherings
In September 1948, an interdisciplinary conference was held at the California
Institute of Technology (Caltech) in Pasadena, California, on the topics of how
the nervous system controls behavior and how the brain might be compared to
a computer. It was called the Hixon Symposium on Cerebral Mechanisms in
Behavior. Several luminaries attended and gave papers, among them Warren
McCulloch, John von Neumann, and Karl Lashley (1890–1958), a prominent
psychologist. Lashley gave what some thought was the most important talk at
the symposium. He faulted behaviorism for its static view of brain function
and claimed that to explain human abilities for planning and language,
psychologists would have to begin considering dynamic, hierarchicalstructures. Lashley’s talk laid out the foundations for what would become
cognitive science.1
The emergence of artificial intelligence as a full-fledged field of researchcoincided with (and was launched by) three important meetings – one in 1955,
one in 1956, and one in 1958. In 1955, a “Session on Learning Machines” washeld in conjunction with the 1955 Western Joint Computer Conference in Los
Angeles. In 1956 a “Summer Research Project on Artificial Intelligence” wasconvened at Dartmouth College. And in 1958 a symposium on the“Mechanization of Thought Processes,” was sponsored by the National
Physical Laboratory in the United Kingdom.

3.1

Session on Learning Machines

Four important papers were presented in Los Angeles in 1955. In his
chairman’s introduction to this session, Willis Ware wrote
These papers do not suggest that future learning machines should
be built in the pattern of the general-purpose digital computing73
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3

Gatherings
device; it is rather that the digital computing system offers aconvenient and highly flexible tool to probe the behavior of themodels. . . . This group of papers suggests directions ofimprovement for future machine builders whose intent is to utilize
digital computing machinery for this particular model technique.
Speed of operation must be increased manyfold; simultaneous
operation in many parallel modes is strongly indicated; the size ofrandom access storage must jump several orders of magnitude; newtypes of input–output equipment are needed. With such
advancements and the techniques discussed in these papers, there
is considerable promise that systems can be built in the relatively
near future which will imitate considerable portions of the activityof the brain and nervous system.

Fortunately, we have made substantial progress on the items on Ware’s listof “directions for improvement.” Speed of operation has increased manyfold,parallel operation is utilized in many AI systems, random access storage has
jumped several orders of magnitude, and many new types of input–output
equipment are available. Perhaps even further improvements will be necessary.
The session’s first paper, by Wesley Clark and Belmont Farley of MIT’s
Lincoln Laboratory, described some pattern-recognition experiments on
networks of neuron-like elements.2 Motivated by Hebb’s proposal that
assemblies of neurons could learn and adapt by adjusting the strengths of theirinterconnections, experimenters had been trying various schemes for adjusting
the strengths of connections within their networks, which were usuallysimulated on computers. Some just wanted to see what these networks might
do whereas others, such as Clark and Farley, were interested in specificapplications, such as pattern recognition. To the dismay of neurophysiologists,
who complained about oversimplification, these networks came to be calledneural networks. Clark and Farley concluded that “crude but usefulgeneralization properties are possessed even by randomly connected nets of thetype described.”3
The next pair of papers, one by Gerald P. Dinneen (1924– ) and one by
Oliver Selfridge (1926–2008; Fig. 3.1), both from MIT’s Lincoln Laboratory,
presented a different approach to pattern recognition. Dinneen’s paper4
described computational techniques for processing images. The images were
presented to the computer as a rectangular array of intensity values
corresponding to the various shades of gray in the image. Dinneen pioneered
the use of filtering methods to remove random bits of noise, thicken lines, andfind edges. He began his paper with the following:Over the past months in a series of after-hour and luncheon
meetings, a group of us at the laboratory have speculated on
problems in this area. Our feeling, pretty much unanimously, wasthat there is a real need to get practical, to pick a real live problem
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3.1

Session on Learning Machines
and go after it.

Selfridge’s paper5 was a companion piece to that of Dinneen. Operatingon “cleaned-up” images (as might be produced by Dinneen’s program, forexample), Selfridge described techniques for highlighting “features” in theseimages and then classifying them based on the features. For example, cornersof an image known to be either a square or a triangle are highlighted, and then
the number of corners is counted to determine whether the image is of asquare or of a triangle. Selfridge said that “eventually, we hope to be able torecognize other kinds of features, such as curvature, juxtaposition of singularpoints (that is, their relative bearings and distances), and so forth.”

Figure 3.1: Oliver Selfridge. (Photograph courtesy of Oliver Selfridge.)
The methods pioneered by Selfridge and Dinneen are fundamental tomost of the later work in enabling machines to “see.” Their work is all themore remarkable when one considers that it was done on a computer, theLincoln Laboratory “Memory Test Computer,” that today would be regarded
as extremely primitive. [The Memory Test Computer (MTC) was the first to
use the ferrite core random-access memory modules developed by Jay
Forrester. It was designed and built by Ken Olsen in 1953 at the Digital
Equipment Corporation (DEC). The MTC was the first computer to simulate
the operation of neural networks – those of Clark and Farley.]
The next paper6 was about programming a computer to play chess. It
was written by Allen Newell, then a researcher at the Rand Corporation in
Santa Monica. Thanks to a biographical sketch of Newell written by his
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3

Gatherings

colleague, Herb Simon of Carnegie Mellon University, we know something
about Newell’s motivation and how he came to be interested in this problem:7
In September 1954 Allen attended a seminar at RAND in which
Oliver Selfridge of Lincoln Laboratory described a runningcomputer program that learned to recognize letters and otherpatterns. While listening to Selfridge characterizing his rather
primitive but operative system, Allen experienced what he always
referred to as his “conversion experience.” It became instantly clear
to him “that intelligent adaptive systems could be built that were
far more complex than anything yet done.” To the knowledge Allen
already had about computers (including their symbolic
capabilities), about heuristics, about information processing inorganizations, about cybernetics, and proposals for chess programs
was now added a concrete demonstration of the feasibility ofcomputer simulation of complex processes. Right then hecommitted himself to understanding human learning and thinking
by simulating it.
Simon goes on to summarize Newell’s paper on chess:[It] outlined an imaginative design for a computer program to playchess in humanoid fashion, incorporating notions of goals,
aspiration levels for terminating search, satisfying with “goodenough” moves, multidimensional evaluation functions, thegeneration of subgoals to implement goals, and something like best
first search. Information about the board was to be expressed
symbolically in a language resembling the predicate calculus. The
design was never implemented, but ideas were later borrowed from
it for use in the NSS [Newell, Shaw, and Simon] chess program in1958.8Newell hinted that his aims extended beyond chess. In his paper he wrote“The aim of this effort, then, is to program a current computer to learn toplay good chess. This is the means to understanding more about the kinds ofcomputers, mechanisms, and programs that are necessary to handle
ultracomplicated problems.” Newell’s proposed techniques can be regarded as
his first attempt to produce evidence for what he and Simon later called the
Physical Symbol System Hypothesis.
Walter Pitts, a commentator for this session, concluded it by saying,“But, whereas Messrs. Farley, Clark, Selfridge, and Dinneen are imitating thenervous system, Mr. Newell prefers to imitate the hierarchy of final causes
traditionally called the mind. It will come to the same thing in the end, no
doubt. . . .” To “come to the same thing,” these two approaches, neuralmodeling and symbol processing, must be recognized simply as different levels
76
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3.2

The Dartmouth Summer Projectof description of what goes on in the brain. Different levels are appropriate fordescribing different kinds of mental phenomena. I’ll have more to say about
description levels later in the book.

3.2

The Dartmouth Summer ProjectIn 1954, John McCarthy (1927– ; Fig 3.2) joined Dartmouth College in
Hanover, New Hampshire, as an Assistant Professor of Mathematics.
McCarthy had been developing a continuing interest in what would come to becalled artificial intelligence. It was “triggered,” he says, “by attending the
September 1948 Hixon Symposium on Cerebral Mechanisms in Behavior heldat Caltech where I was starting graduate work in mathematics.”9 While atDartmouth he was invited by Nathaniel Rochester (1919–2001) to spend thesummer of 1955 in Rochester’s Information Research Department at IBM in
Poughkeepsie, New York. Rochester had been the designer of the IBM 701
computer and had also participated in research on neural networks.10
At IBM that summer, McCarthy and Rochester persuaded Claude
Shannon and Marvin Minsky (1927– ; Fig. 3.2), then a Harvard junior fellow inmathematics and neurology, to join them in proposing a workshop to be heldat Dartmouth during the following summer. Shannon, whom I have previously
mentioned, was a mathematician at Bell Telephone Laboratories and alreadyfamous for his work on switching theory and statistical information theory.
McCarthy took the lead in writing the proposal and in organizing what was tobe called a “Summer Research Project on Artificial Intelligence.” The proposal
was submitted to the Rockefeller Foundation in August 1955.
Extracts from the proposal read as follows:11
We propose that a 2 month, 10 man study of artificial intelligencebe carried out during the summer of 1956 at Dartmouth College in
Hanover, New Hampshire. The study is to proceed on the basis of
the conjecture that every aspect of learning or any other feature ofintelligence can in principle be so precisely described that amachine can be made to simulate it. An attempt will be made tofind how to make machines use language, form abstractions andconcepts, solve kinds of problems now reserved for humans, andimprove themselves. We think that a significant advance can bemade in one or more of these problems if a carefully selected groupof scientists work on it together for a summer.
...
For the present purpose the artificial intelligence problem is taken
to be that of making a machine behave in ways that would becalled intelligent if a human were so behaving.

77
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3

Gatherings

Figure 3.2: John McCarthy (left) and Marvin Minsky (right). (McCarthy photograph courtesy of John McCarthy. Minsky photograph courtesy MIT Museum.)

The Rockefeller Foundation did provide funding for the event, which took
place during six weeks of the summer of 1956. It turned out, however, to be
more of a rolling six-week workshop than a summer “study.” Among thepeople attending the workshop that summer, in addition to McCarthy,
Minsky, Rochester, and Shannon were Arthur Samuel (1901–1990), an
engineer at the IBM corporation who had already written a program to playcheckers, Oliver Selfridge, Ray Solomonoff of MIT, who was interested in
automating induction, Allen Newell, and Herbert Simon. Newell and Simon(together with another Rand scientist, Cliff Shaw) had produced a program forproving theorems in symbolic logic. Another attending IBM scientist was Alex
Bernstein, who was working on a chess-playing program.
McCarthy has given a couple of reasons for using the term “artificial
intelligence.” The first was to distinguish the subject matter proposed for theDartmouth workshop from that of a prior volume of solicited papers, titled
Automata Studies, co-edited by McCarthy and Shannon, which (toMcCarthy’s disappointment) largely concerned the esoteric and rather narrow
mathematical subject called “automata theory.” The second, according to
McCarthy, was “to escape association with ‘cybernetics.’ Its concentration on
analog feedback seemed misguided, and I wished to avoid having either toaccept Norbert Wiener as a guru or having to argue with him.”1278
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3.2

The Dartmouth Summer Project

There was (and still is) controversy surrounding the name. According toPamela McCorduck’s excellent history of the early days of artificial
intelligence, Art Samuel remarked, “The word artificial makes you think
there’s something kind of phony about this, or else it sounds like it’s all
artificial and there’s nothing real about this work at all.”13 McCorduck goeson to say that “[n]either Newell or Simon liked the phrase, and called theirown work complex information processing for years thereafter.” But most of
the people who signed on to do work in this new field (including myself) used
the name “artificial intelligence,” and that is what the field is called today.
(Later, Newell became reconciled to the name. In commenting about thecontent of the field, he concluded, “So cherish the name artificial intelligence.
It is a good name. Like all names of scientific fields, it will grow to become
exactly what its field comes to mean.”)14
The approaches and motivations of the people at the workshop differed.
Rochester came to the conference with a background in networks of neuron-likeelements. Newell and Simon had been pursuing (indeed had helped originate)
the symbol-processing approach. Among the topics Shannon wanted to think
about (according to the proposal) was the “application of information theory
concepts to computing machines and brain models.” (After the workshop,
however, Shannon turned his attention away from artificial intelligence.)
McCarthy wrote that he was interested in constructing “an artificial
language which a computer can be programmed to use on problems requiring
conjecture and self-reference. It should correspond to English in the sense thatshort English statements about the given subject matter should have shortcorrespondents in the language and so should short arguments or conjecturalarguments. I hope to try to formulate a language having these properties . . . ”
Although McCarthy later said that his ideas on this topic were still too “illformed” for presentation at the conference, it was not long before he madespecific proposals for using a logical language and its inference mechanisms forrepresenting and reasoning about knowledge.
Although Minsky’s Ph.D. dissertation15 and some of his subsequent work
concentrated on neural nets, around the time of the Dartmouth workshop hewas beginning to change direction. Now, he wrote, he wanted to consider amachine that “would tend to build up within itself an abstract model of theenvironment in which it is placed. If it were given a problem, it could first
explore solutions within the internal abstract model of the environment andthen attempt external experiments.” At the workshop, Minsky continued workon a draft that was later to be published as a foundational paper, “Steps
Toward Artificial Intelligence.”16
One of the most important technical contributions of the 1956 meeting
was work presented by Newell and Simon on their program, the “Logic
Theorist (LT),” for proving theorems in symbolic logic. LT was concrete
evidence that processing “symbol structures” and the use of what Newell andSimon called “heuristics” were fundamental to intelligent problem solving. I’ll
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3

Gatherings

describe some of these ideas in more detail in a subsequent chapter.
Newell and Simon had been working on ideas for LT for some months andbecame convinced in late 1955 that they could be embodied in a working
program. According to Edward Feigenbaum (1936– ), who was taking a course
from Herb Simon at Carnegie in early 1956, “It was just after Christmas
vacation – January 1956 – when Herb Simon came into the classroom andsaid, ‘Over Christmas Allen Newell and I invented a thinking machine.’”17
What was soon to be programmed as LT was the “thinking machine” Simon
was talking about. He called it such, no doubt, because he thought it usedsome of the same methods for solving problems that humans use. Simon later
wrote18 “On Thursday, Dec. 15. . . I succeeded in simulating by hand the first
proof. . . I have always celebrated Dec. 15, 1955, as the birthday of heuristic
problem solving by computer.” According to Simon’s autobiography Models of
My Life,19 LT began by hand simulation, using his children as the computing
elements, while writing on and holding up note cards as the registers that
contained the state variables of the program.20
Another topic discussed at Dartmouth was the problem of proving
theorems in geometry. (Perhaps some readers will recall their struggles withgeometry proofs in high school.) Minsky had already been thinking about aprogram to prove geometry theorems. McCorduck quotes him as saying thefollowing:21[P]robably the important event in my own development – and the
explanation of my perhaps surprisingly casual acceptance of the
Newell–Shaw–Simon work – was that I had sketched out theheuristic search procedure for [a] geometry machine and then beenable to hand-simulate it on paper in the course of an hour or so.
Under my hand the new proof of the isosceles-triangle theoremcame to life, a proof that was new and elegant to the participants –later, we found that proof was well-known. . .
In July 2006, another conference was held at Dartmouth celebrating thefiftieth anniversary of the original conference. (See Fig. 3.3.) Several of thefounders and other prominent AI researchers attended and surveyed what had
been achieved since 1956. McCarthy reminisced that the “main reason the1956 Dartmouth workshop did not live up to my expectations is that AI isharder than we thought.” In any case, the 1956 workshop is considered to be
the official beginning of serious work in artificial intelligence, and Minsky,
McCarthy, Newell, and Simon came to be regarded as the “fathers” of AI. A
plaque was dedicated and installed at the Baker Library at Dartmouthcommemorating the beginning of artificial intelligence as a scientific discipline.

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3.3

Mechanization of Thought Processes

Figure 3.3: Some of AI’s founders at the July 2006 Dartmouth fiftieth anniversary meeting. From the left are Trenchard More, John McCarthy, Marvin Minsky, Oliver Selfridge, and Ray Solomonoff. (Photograph courtesy of photographer Joe Mehling and the Dartmouth College Artificial Intelligence Conference:
The Next Fifty Years.)

3.3

Mechanization of Thought Processes

In November 1958, a symposium on the “Mechanisation of Thought
Processes” was held at the National Physical Laboratory in Teddington,
Middlesex, England. According to the preface of the conference proceedings,
the symposium was held “to bring together scientists studying artificial
thinking, character and pattern recognition, learning, mechanical languagetranslation, biology, automatic programming, industrial planning and clericalmechanization.”
Among the people who presented papers at this symposium were many
whom I have already mentioned in this story. They include Minsky (by then astaff member at Lincoln Laboratory and on his way to becoming an assistantprofessor of Mathematics at MIT), McCarthy (by then an assistant professorof Communication Sciences at MIT), Ashby, Selfridge, and McCulloch. (John81
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3

Gatherings

Backus, one of the developers of the computer programming language
FORTRAN, and Grace Murray Hopper, a pioneer in “automatic programming,”
also gave papers.)
The proceedings of this conference22 contains some papers that became
quite influential in the history of artificial intelligence. Among these, I’ll
mention ones by Minsky, McCarthy, and Selfridge.
Minsky’s paper, “Some Methods of Artificial Intelligence and Heuristic
Programming,” was the latest version of a piece he had been working on since
just before the Dartmouth workshop. The paper described various methods
that were (and could be) used in heuristic programming. It also covered
methods for pattern recognition, learning, and planning. The final version,
which was soon to be published as “Steps Toward Artificial Intelligence,” wasto become required reading for new recruits to the field (including me).
I have already mentioned McCarthy’s hope to develop an artificial
language for AI. He summarized his conference paper, “Programs with
Common Sense,” as follows:This paper will discuss programs to manipulate in a suitable formal
language (most likely a part of the predicate calculus) common
instrumental statements. The basic program will draw immediateconclusions from a list of premises. These conclusions will be either
declarative or imperative sentences. When an imperative sentence
is deduced, the program takes a corresponding action.
In his paper, McCarthy suggested that facts needed by an AI program,which he called the “advice taker,” might be represented as expressions in amathematical (and computer-friendly) language called “first-order logic.” Forexample, the facts “I am at my desk” and “My desk is at home” would be
represented as the expressions at(I, desk) and at(desk, home). These,
together with similarly represented information about how to achieve a change
in location (by walking and driving for example), could then be used by the
proposed (but not yet programmed) advice taker to figure out how to achievesome goal, such as being at the airport. The advice taker’s reasoning process
would produce imperative logical expressions involving walking to the car anddriving to the airport.
Representing facts in a logical language has several advantages. As
McCarthy later put it,23
Expressing information in declarative sentences is far more
modular than expressing it in segments of computer program or intables. Sentences can be true in much wider contexts than specificprograms can be useful. The supplier of a fact does not have tounderstand much about how the receiver functions, or how orwhether the receiver will use it. The same fact can be used for82
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3.3

Mechanization of Thought Processes
many purposes, because the logical consequences of collections offacts can be available.

McCarthy later expanded on these ideas in a companion memorandum.24
As I’ll mention later, some of McCarthy’s advice-taker proposals were finallyimplemented by a Stanford graduate student, C. Cordell Green.
I have already mentioned the 1955 pattern-recognition work of Oliver
Selfridge. At the 1958 Teddington Symposium, Selfridge presented a paper ona new model for pattern recognition (and possibly for other cognitive tasks
also).25 He called it “Pandemonium,” meaning the place of all the demons.
His model is especially interesting because its components, which Selfridge
called “demons,” can either be instantiated as performing lower levelnerve-cell-type functions or higher level cognitive functions (of thesymbol-processing variety). Thus, Pandemonium can take the form of a neuralnetwork, a hierarchically organized set of symbol processors – all working inparallel, or some combination of these forms. If the latter, the model is a
provocative proposal for joining these two disparate approaches to AI.
In the introduction to his paper, Selfridge emphasized the importance ofcomputations performed in parallel:
The basic motif behind our model is the notion of parallel
processing. This is suggested on two grounds: first, it is often easier
to handle data in a parallel manner, and, indeed, it is usually themore “natural” manner to handle it in; and, secondly, it is easier tomodify an assembly of quasi-independent modules than a machine
all of whose parts interact immediately and in a complex way.
Selfridge made several suggestions about how Pandemonium could learn.
It’s worth describing some of these because they foreshadow later work inmachine learning. But first I must say a bit more about the structure ofPandemonium.
Pandemonium’s structure is something like that of a business organization
chart. At the bottom level are workers, whom Selfridge called the “datademons.” These are computational processes that “look at” the input data,
say an image of a printed letter or number. Each demon looks for something
specific in the image, perhaps a horizontal bar; another might look for a
vertical bar; another for an arc of a circle; and so on. Each demon “shouts” itsfindings to a set of demons higher in the organization. (Think of these higher
level demons as middle-level managers.) The loudness of a demon’s shout
depends on how certain it is that it is seeing what it is looking for. Of course,
Selfridge is speaking metaphorically when he uses terms such as “looking for”
and “shouting.” Suffice it to say that it is not too difficult to program
computers to “look for” certain features in an image. (Selfridge had alreadyshown how that could be done in his 1955 paper that I mentioned earlier.)
And a “shout” is really the strength of the output of a computational process.
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Gatherings

Each of the next level of demons specializes in listening for a particular
combination of shouts from the data demons. For example, one of the demonsat this level might be tuned to listen for shouts from data demon 3, data
demon 11, and data demon 22. If it finds that these particular demons areshouting loudly, it responds with a shout of its own to the demons one level up
in the hierarchy, and so on.
Just below the top level of the organization are what Selfridge called the“cognitive demons.” As at the other levels, these listen for particular
combinations of shouts from the demons at the level below, and they respondwith shouts of their own to a final “decision demon” at the top – the overallboss. Depending on what it hears from its “staff,” the decision demon finally
announces what it thinks is the identity of the image – perhaps the letter “A”
or the letter “R” or whatever.
Actual demon design depends on what task Pandemonium is supposed to
be doing. But even without specifying what each demon was to do, Selfridge
made very interesting proposals about how Pandemonium could learn toperform better at whatever it was supposed to be doing. One of his proposals
involved equipping each demon with what amounted to a “megaphone”
through which it delivered its shout. The volume level of the megaphone could
be adjusted. (Selfridge’s Pandemonium is just a bit more complicated than theversion I am describing. His version has each demon using different channels
for communicating with each of the different demons above it. The volume ofthe shout going up each channel is individually adjusted by the learning
mechanism.) The demons were not allowed to set their own volume levels,
however. All volume levels were to be set through an outside learning processattempting to improve the performance of the whole assembly. Imagine thatthe volume levels are initially set either at random or at whatever a designer
thinks would be appropriate. The device is then tested on some sample of
input data and its performance score is noted. Say, it gets a score of 81%.
Then, small adjustments are made to the volume levels in all possible ways
until a set of adjustments is found that improves the score the most, say to83%. This particular set of small adjustments is then made and the process isrepeated over and over (possibly on additional data) until no further
improvement can be made.
(Because there might be a lot of megaphones in the organization, it mightseem impractical to make adjustments in all possible ways and to test each ofthese ways to find its score. The process might indeed take some time, but
computers are fast – even more so today. Later in the book, I’ll show how one
can calculate, rather than find by experiment, the best adjustments to make inneural networks organized like Pandemonium.)
If we think of the score as the height of some landscape and theadjustments as movements over the landscape, the process can be likened toclimbing a hill by always taking steps in the direction of steepest ascent.
Gradient ascent (or hill-climbing methods, as they are sometimes called) are84
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3.3

NOTES

well known in mathematics. Selfridge had this to say about some of thepitfalls of their use:
This may be described as one of the problems of training, namely,
to encourage the machine or organism to get enough on thefoot-hills so that small changes. . . will produce noticeable
improvement in his altitude or score. One can describe learning
situations where most of the difficulty of the task lies in finding any
way of improving one’s score, such as learning to ride a unicycle,
where it takes longer to stay on for a second than it does toimprove that one second to a minute; and others where it is easy to
do a little well and very hard to do very well, such as learning toplay chess. It’s also true that often the main peak is a plateau
rather than an isolated spike.
Selfridge described another method for learning in Pandemonium. Thismethod might be likened to replacing managers in an organization who do not
perform well. As Selfridge puts it,
At the conception of our demoniac assembly we collected somewhat
arbitrarily a large number of subdemons which we guessed would
be useful. . . but we have no assurance at all that the particularsubdemons we selected are good ones. Subdemon selection
generates new subdemons for trial and eliminates inefficient ones,
that is, ones that do not much help improve the score.
The demon selection process begins after the volume-adjusting learning
mechanism has run for a while with no further improvements in the score.
Then the “worth” of each demon is evaluated by using, as Selfridge suggests, amethod based on the learned volume levels of their shouting. Demons having
high volume levels have a large effect on the final score, and so they can bethought to have high worth. First, the demons with low volume levels are
eliminated entirely. (That step can’t hurt the score very much.) Next, some ofthe demons undergo random “mutations” and are put back in service. Next,
some pairs of worthy demons are selected and, as Selfridge says, “conjugated”
into offspring demons. The precise method Selfridge proposed for conjugationneed not concern us here, but the spirit of the process is to produce offspringthat share, one hopes, useful properties of the parents. The offspring are thenput into service. Now the whole process of adjusting volume levels of thesurviving and “evolved” demons can begin again to see whether the score of
the new assembly can be further improved.

Notes

85
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3

NOTES

1. The proceedings of the symposium were published in L. A. Jeffries (ed.), Cerebral
Mechanisms in Behavior: The Hixon Symposium, New York: Wiley, 1951. An excellent
review of Lashley’s points are contained in Chapter 2 of The Mind’s New Science: A Historyof the Cognitive Revolution, by Howard E Gardner, New York: Basic Books, 1985. [73]2. W. A. Clark and B. G. Farley, “Generalization of Pattern Recognition in a
Self-Organizing System,” Proceedings of the 1955 Western Joint Computer Conference,
Institute of Radio Engineers, New York, pp. 86–91, 1955. Clark and Farley’s experiments
continued some work they had reported on earlier in B. G. Farley and W. A. Clark,“Simulation of Self-Organizing Systems by Digital Computer, IRE Transactions on
Information Theory, Vol. 4, pp. 76–84, 1954. (In 1962 Clark built the first personal
computer, the LINC.) [74]3. Alan Wilkes and Nicholas Wade credit Scottish psychologist Alexander Bain
(1818–1903) with the invention of the first neural network, which Bain described in his 1873
book Mind and Body: The Theories of Their Relation.” (See Alan L. Wilkes and Nicholas J.
Wade, “Bain on Neural Networks,” Brain and Cognition, Vol. 33, pp. 295–305, 1997.) [74]4. Gerald P. Dinneen, “Programming Pattern Recognition,” Proceedings of the 1955
Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 94–100,
1955. [74]5. Oliver Selfridge, “Pattern Recognition and Modern Computers,” Proceedings of the 1955
Western Joint Computer Conference, Institute of Radio Engineers, New York, pp. 91–93,
1955. [75]6. Allen Newell, “The Chess Machine: An Example of Dealing with a Complex Task by
Adaptation,” Proceedings of the 1955 Western Joint Computer Conference, Institute ofRadio Engineers, New York, pp. 101–108, 1955. (Also issued as RAND Technical Report
P-620.) [75]7. National Academy of Sciences, Biographical Memoirs, Vol. 71, 1997. Available online at
http://www.nap.edu/catalog.php?record id=5737. [76]8. Allen Newell, J. C. Shaw, and Herbert A. Simon, “Chess-Playing Programs and the
Problem of Complexity,” IBM Journal of Research and Development, Vol. 2, pp. 320–335,
1958. The paper is available online at http://domino.watson.ibm.com/tchjr/journalindex.
nsf/0/237cfeded3be103585256bfa00683d4d?OpenDocument. [76]9. From John McCarthy’s informal comments at the 2006 Dartmouth celebration. [77]10. Nathan Rochester et al., “Tests on a Cell Assembly Theory of the Action of the Brain
Using a Large Digital Computer,” IRE Transaction of Information Theory, Vol. IT-2, pp.
80-93, 1956. [77]11. From http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html.
Portions of the proposal have been reprinted in John McCarthy, Marvin L. Minsky,
Nathaniel Rochester, and Claude E. Shannon, “A Proposal for the Dartmouth Summer
Research Project on Artificial Intelligence,” AI Magazine, Vol. 27, No. 4, p. 12, Winter2006. [77]12. From http://www-formal.stanford.edu/jmc/reviews/bloomfield/bloomfield.html. [78]13. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History andProspects of Artificial Intelligence, p. 97, San Francisco: W. H. Freeman and Co., 1979. [79]14. See Allen Newell, “The First AAAI President’s Message,” AI Magazine, Vol. 26, No. 4,
pp. 24–29, Winter 2005. [79]15. M. L. Minsky, Theory of Neural-Analog Reinforcement Systems and Its Application to
the Brain-Model Problem, Ph.D. thesis, Princeton University, 1954. [79]16. Marvin L. Minsky, “Steps Toward Artificial Intelligence,” Proceedings of the IRE, Vol.
49, No. 1, pp. 8–30, January 1961. Also appears in Edward A. Feigenbaum, and Julian86
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3.3

NOTES

Feldman (eds.), Computers and Thought, New York: McGraw Hill, 1963. (Available online
at http://web.media.mit.edu/∼minsky/papers/steps.html.) [79]17. Pamela McCorduck, op. cit., p. 116. [80]18. Herbert A. Simon, Models of My Life, Cambridge, MA: MIT Press, 1996. The quote isfrom http://www.post-gazette.com/pg/06002/631149.stm. [80]19. Ibid. [80]20. http://www.post-gazette.com/downloads/20060102simon notes.pdf contains sketches ofSimon’s simulation of an LT proof. [80]21. Pamela McCorduck, op. cit., p. 106. [80]22. D. V. Blake and A. M. Uttley (eds.), Proceedings of the Symposium on Mechanisationof Thought Processes, Vols. 1 and 2, London: Her Majesty’s Stationary Office, 1959. [82]23. John McCarthy, “Artificial Intelligence, Logic and Formalizing Common Sense,” inPhilosophical Logic and Artificial Intelligence, Richmond Thomason (ed.), Dordrecht:
Kluwer Academic, 1989. [82]24. J. McCarthy, “Situations, Actions and Causal Laws, Stanford Artificial Intelligence
Project,” Memo 2, 1963. The two pieces are reprinted together in M. Minsky (ed.),Semantic Information Processing, pp. 410–417, Cambridge, MA: MIT Press, 1968. Related
topics are explored in J. McCarthy and Patrick Hayes, “Some Philosophical Ideas From theStandpoint of Artificial Intelligence,” MI-4, 1969. [83]25. Oliver G. Selfridge, “Pandemonium: A Paradigm for Learning,” in D. V. Blake and A.
M. Uttley (eds.), Proceedings of the Symposium on Mechanisation of Thought Processes, pp,
511–529, London: Her Majesty’s Stationary Office, 1959. [83]87
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3

NOTES

88
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4.1

Chapter 4

Pattern Recognition
Most of the attendees of the Dartmouth summer project were interested in
mimicking the higher levels of human thought. Their work benefitted from acertain amount of introspection about how humans solve problems. Yet, manyof our mental abilities are beyond our power of introspection. We don’t know
how we recognize speech sounds, read cursive script, distinguish a cup from aplate, or identify faces. We just do these things automatically without
thinking about them. Lacking clues from introspection, early researchersinterested in automating some of our perceptual abilities based their work
instead on intuitive ideas about how to proceed, on networks of simple modelsof neurons, and on statistical techniques. Later, workers gained additional
insights from neurophysiological studies of animal vision.
In this chapter, I’ll describe work during the 1950s and 1960s on what iscalled “pattern recognition.” This phrase refers to the process of analyzing an
input image, a segment of speech, an electronic signal, or any other sample ofdata and classifying it into one of several categories. For character recognition,for example, the categories would correspond to the several dozen or soalphanumeric characters.
Most of the pattern-recognition work in this period dealt with
two-dimensional material such as printed pages or photographs. It was alreadypossible to scan images to convert them into arrays of numbers (later called“pixels”), which could then be processed by computer programs such as thoseof Dinneen and Selfridge. Russell Kirsch and colleagues at the National
Bureau of Standards (now the National Institute for Standards andTechnology) were also among the early pioneers in image processing. In 1957
Kirsch built and used a drum scanner to scan a photograph of his
three-month-old son, Walden. Said to be the first scanned photograph, itmeasured 176 pixels on a side and is depicted in Fig. 4.1.1 Using his scanner,
he and colleagues experimented with picture-procesing programs running on
their SEAC (Standards Eastern Automatic Computer) computer.2
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Pattern Recognition

Figure 4.1: An early scanned photograph. (Photograph used with permissionof NIST.)

4.1

Character Recognition

Early efforts at the perception of visual images concentrated on recognizing
alphanumeric characters on documents. This field came to be known as“optical character recognition.” A symposium devoted to reporting on
progress on this topic was held in Washington, DC, in January 1962.3 In
summary, devices existed at that time for reasonably accurate recognition of
fixed-font (typewritten or printed) characters on paper. Perhaps the state ofthings then was best expressed by one of the participants of the symposium, J.
Rabinow of Rabinow Engineering, who said “We think, in our company, that
we can read anything that is printed, and we can even read some things thatare written. The only catch is, ‘how many bucks do you have to spend?’”4
A notable success during the 1950s was the magnetic ink character
recognition (MICR) system developed by researchers at SRI International(then called the Stanford Research Institute) for reading stylized magnetic ink
characters at the bottom of checks. (See Fig. 4.2.) MICR was part of SRI’s
ERMA (Electronic Recording Method of Accounting) system for automating
check processing and checking account management and posting.
According to an SRI Web site, “In April 1956, the Bank of America
announced that General Electric Corporation had been selected to
manufacture production models. . . . In 1959, General Electric delivered thefirst 32 ERMA computing systems to the Bank of America. ERMA served as the
Bank’s accounting computer and check handling system until 1970.”5

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4.1

Character Recognition

Figure 4.2: The MICR font set.
Most of the recognition methods at that time depended on matching acharacter (after it was isolated on the page and converted to an array of 0’s
and 1’s) against prototypical versions of the character called “templates” (alsostored as arrays in the computer). If a character matched the template for an“A,” say, sufficiently better than it matched any other templates, the input
was declared to be an “A.” Recognition accuracy degraded if the input
characters were not presented in standard orientation, were not of the same
font as the template, or had imperfections.
The 1955 papers by Selfridge and Dinneen (which I have alreadymentioned on p. 74) proposed some ideas for moving beyond templatematching. A 1960 paper by Oliver Selfridge and Ulrich Neisser carried thiswork further.6 That paper is important because it was a successful, earlyattempt to use image processing, feature extraction, and learned probability
values in hand-printed character recognition. The characters were scanned andrepresented on a 32 × 32 “retina” or array of 0’s and 1’s. They were thenprocessed by various refining operations (similar to those I mentioned inconnection with the 1955 Dinneen paper) for removing random bits of noise,
filling gaps, thickening lines, and enhancing edges. The “cleaned-up” images
were then inspected for the occurrence of “features” (similar to the features I
mentioned in connection with the 1955 Selfridge paper.) In all, 28 features
were used – features such as the maximum number of times a horizontal line
intersected the image, the relative lengths of different edges, and whether or
not the image had a “concavity facing south.”
Recalling Selfridge’s Pandemonium system, we can think of thefeature-detection process as being performed by “demons.” At one level higher
in the hierarchy than the feature demons were the “recognition demons” – onefor each letter. (The version of this system tested by Worthie Doyle of Lincoln
Laboratory was designed to recognize ten different hand-printed characters,
namely, A, E, I, L, M, N, O, R, S, and T.) Each recognition demon received
inputs from each of the feature-detecting demons. But first, the inputs to each
recognition demon were multiplied by a weight that took into account theimportance of the contribution of the corresponding feature to the decision.
For example, if feature 17 were more important than feature 22 in decidingthat the input character was an “A,” then the input to the “A” recognizer

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Pattern Recognition

from feature 17 would be weighted more heavily than would be the input fromfeature 22. After each recognition demon added up the total of its weighted
inputs, a final “decision demon” decided in favor of that character having thelargest sum.
The values of the weights were determined by a learning process during
which 330 “training” images were analyzed. Counts were tabulated for how
many times each feature was detected for each different letter in the training
set. These statistical data were used to make estimates of the probabilities
that a given feature would be detected for each of the letters. These
probability estimates were then used to weight the features summed by therecognizing demons.
After training, the system was tested on samples of hand-printed
characters that it had not yet seen. According to Selfridge and Neisser, “Thisprogram makes only about 10 percent fewer correct identifications than humanreaders make – a respectable performance, to be sure.”

4.2

Neural Networks

4.2.1

Perceptrons

In 1957, Frank Rosenblatt (1928–1969; Fig. 4.3), a psychologist at the Cornell
Aeronautical Laboratory in Buffalo, New York, began work on neuralnetworks under a project called PARA (Perceiving and Recognizing
Automaton). He was motivated by the earlier work of McCulloch and Pitts
and of Hebb and was interested in these networks, which he called perceptrons,
as potential models of human learning, cognition, and memory.7
Continuing during the early 1960s as a professor at Cornell University in
Ithaca, New York, he experimented with a number of different kinds ofperceptrons. His work, more than that of Clark and Farley and of the otherneural network pioneers, was responsible for initiating one of the principal
alternatives to symbol-processing methods in AI, namely, neural networks.
Rosenblatt’s perceptrons consisted of McCulloch–Pitts-style neuralelements, like the one shown in Fig. 4.4. Each element had inputs (coming infrom the left in the figure), “weights” (shown by bulges on the input lines),and one output (going out to the right). The inputs had values of either 1 or
0, and each input was multiplied by its associated weight value. The neuralelement computed the sum of these weighted values. So, for example, if all of
the inputs to the neural element in Fig. 4.4 were equal to 1, the sum would be
13. If the sum were greater than (or just equal to) a “threshold value,” say 7,
associated with the element, then the output of the neural element would be 1,
which it would be in this example. Otherwise the output would be 0.
A perceptron consists of a network of these neural elements, in which the92
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4.2

Neural Networks

Figure 4.3: Frank Rosenblatt (left) working (with Charles Wrightman) on a
prototype A-unit. (Courtesy of the Division of Rare and Manuscript Collections,
Cornell University Library.)

Figure 4.4: Rosenblatt’s neural element with weights.
outputs of one element are inputs to others. (There is an analogy here with
Selfridge’s Pandemonium in which mid-level demons receive “shouts” fromlower level demons. The weights on a neural element’s input lines can bethought of as analogous to the strength-enhancing or strength-diminishing“volume controls” in Pandemonium.) A sample perceptron is illustrated inFig. 4.5. [Rosenblatt drew his perceptron diagrams in a horizontal format (theelectrical engineering style), with inputs to the left and output to the right.
Here I use the vertical style generally preferred by computer scientists for
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4

Pattern Recognition

hierarchies, with the lowest level at the bottom and the highest at the top. To
simplify the diagram, weight bulges are not shown.] Although the perceptron
illustrated, with only one output unit, is capable of only two different outputs(1 or 0), multiple outputs (sets of 1’s and 0’s) could be achieved by arranging
for several output units.

Figure 4.5: A perceptron.
The input layer, shown at the bottom of Fig. 4.5, was typically arectangular array of 1’s and 0’s corresponding to cells called “pixels” of a
black-and-white image. One of the applications Rosenblatt was interested inwas, like Selfridge, character recognition.
I’ll use some simple algebra and geometry to show how the neuralelements in perceptron networks can be “trained” to produce desired outputs.
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4.2

Neural Networks

Let’s consider, for example, a single neural element whose inputs are thevalues x1 , x2 , and x3 and whose associated weight values are w1 , w2 , and w3 .
When the sum computed by this element is exactly equal to its thresholdvalue, say t, we have the equation
w1 x1 + w2 x2 + w3 x3 = t.
In algebra, such an equation is called a “linear equation.” It defines a linear
boundary, that is, a plane, in a three-dimensional space. The plane separates
those input values that would cause the neural element to have an output of 1
from those that would cause it to have an output of 0. I show a typical planarboundary in Fig. 4.6.

Figure 4.6: A separating plane in a three-dimensional space.
An input to the neural element can be depicted as a point (that is, avector) in this three-dimensional space. Its coordinates are the values of x1 ,x2 , and x3 , each of which can be either 1 or 0. The figure shows six such
points, three of them (the small circles, say) causing the element to have an
output of 1 and three (the small squares, say) causing it to have an output of
0. Changing the value of the threshold causes the plane to move sideways in a95
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4

Pattern Recognition

direction parallel to itself. Changing the values of the weights causes the plane
to rotate. Thus, by changing the weight values, points that used to be on one
side of the plane might end up on the other side. “Training” takes place byperforming such changes. I’ll have more to say about training procedures
presently.
In dimensions higher than three (which is usually the case), a linear
boundary is called a “hyperplane.” Although it is not possible to visualize
what is going on in spaces of high dimensions, mathematicians still speak of
input points in these spaces and rotations and movements of hyperplanes in
response to changes in the values of weights and thresholds.
Rosenblatt defined several types of perceptrons. He called the one shownin the diagram a “series-coupled, four-layer perceptron.” (Rosenblatt counted
the inputs as the first layer.) It was termed “series-coupled” because theoutput of each neural element fed forward to neural elements in a subsequent
layer. In more recent terminology, the phrase “feed-forward” is used instead of“series-coupled.” In contrast, a “cross-coupled” perceptron could have theoutputs of neural elements in one layer be inputs to neural elements in thesame layer. A “back-coupled” perceptron could have the outputs of neuralelements in one layer be inputs to neural elements in lower numbered layers.
Rosenblatt thought of his perceptrons as being models of the wiring ofparts of the brain. For this reason, he called the neural elements in all layers
but the output layer “association units” (“A-units”) because he intended themto model associations performed by networks of neurons in the brain.
Of particular interest in Rosenblatt’s research was what he called an“alpha-perceptron.” It consisted of a three-layer, feed-forward network with an
input layer, an association layer, and one or more output units. In most of his
experiments, the inputs had values of 0 or 1, corresponding to black or white
pixels in a visual image presented on what he called a “retina.” Each A-unit
received inputs (which were not multiplied by weight values) from somerandomly selected subset of the pixels and sent its output, through sets ofadjustable weights, to the final output units, whose binary values could beinterpreted as a code for the category of the input image.
Various “training procedures” were tried for adjusting the weights of theoutput units of an alpha-perceptron. In the most successful of these (forpattern-recognition purposes), the weights leading in to the output units were
adjusted only when those units made an error in classifying an input. The
adjustments were such as to force the output to make the correct classification
for that particular input. This technique, which soon became a standard, wascalled the “error-correction procedure.” Rosenblatt used it successfully in a
number of experiments for training perceptrons to classify visual inputs, suchas alphanumeric characters, or acoustic inputs, such as speech sounds.
Professor H. David Block, a Cornell mathematician working with Rosenblatt,
was able to prove that the error-correction procedure was guaranteed to find a96
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4.2

Neural Networks

hyperplane that perfectly separated a set of training inputs when such a
hyperplane existed.8 (Other mathematicians, such as Albert B. Novikoff at
SRI, later developed more elegant proofs.9 I give a version of this proof in mybook Learning Machines.10 )Although some feasibility and design work was done using computer
simulations, Rosenblatt preferred building hardware versions of his
perceptrons. (Simulations were slow on early computers, thus explaining theinterest in building special-purpose perceptron hardware.) The MARK I wasan alpha-perceptron built at the Cornell Aeronautical Laboratory under thesponsorship of the Information Systems Branch of the Office of Naval
Research and the Rome Air Development Center. It was first publicly
demonstrated on 23 June 1960. The MARK I used volume controls (called“potentiometers” by electrical engineers) for weights. These had small motors
attached to them for making adjustments to increase or decrease the weightvalues.
In 1959, Frank Rosenblatt moved his perceptron work from the Cornell
Aeronautical Laboratory in Buffalo, New York, to Cornell University, where hebecame a professor of psychology. Together with Block and several students,
Rosenblatt continued experimental and theoretical work on perceptrons. His
book Principles of Neurodynamics provides a detailed treatment of his
theoretical ideas and experimental results.11 Rosenblatt’s last system, called
Tobermory, was built as a speech-recognition device.12 [Tobermory was thename of a cat that learned to speak in The Chronicles of Clovis, a group of
short stories by Saki (H. H. Munro).] Several Ph.D. students, including George
Nagy, Carl Kessler, R. D. Joseph, and others, completed perceptron projects
under Rosenblatt at Cornell.
In his last years at Cornell, Rosenblatt moved on to study chemical
memory transfer in flatworms and other animals – a topic quite removed fromhis perceptron work. Tragically, Rosenblatt perished in a sailing accident in
Chesapeake Bay in 1969.
Around the same time as Rosenblatt’s alpha-perceptron, Woodrow W.
(Woody) Bledsoe (1921–1995) and Iben Browning (1918–1991), twomathematicians at Sandia Laboratories in Albuquerque, New Mexico, werealso pursuing research on character recognition that used random samplings of
input images. They experimented with a system that projected images ofalphanumeric characters on a 10 × 15 mosaic of photocells and sampled thestates of 75 randomly chosen pairs of photocells. Pointing out that the idea
could be extended to sampling larger groups of pixels, say N of them, theycalled their method the “N-tuple” method. They used the results of thissampling to make a decision about the category of an input letter.13

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4

Pattern Recognition

4.2.2

ADALINES and MADALINES

Independently of Rosenblatt, a group headed by Stanford ElectricalEngineering Professor Bernard Widrow (1929– ) was also working onneural-network systems during the late 1950s and early 1960s. Widrow had
recently joined Stanford after completing a Ph.D. in control theory at MIT.
He wanted to use neural-net systems for what he called “adaptive control.”One of the devices Widrow built was called an “ADALINE” (for adaptive
linear network). It was a single neural element whose adjustable weights were
implemented by switchable (thus adjustable) circuits of resistors. Widrow andone of his students, Marcian E. “Ted” Hoff Jr. (who later invented the firstmicroprocessor at Intel), developed an adjustable weight they called a“memistor.” It consisted of a graphite rod on which a layer of copper could be
plated and unplated – thus varying its electrical resistance. Widrow and Hoffdeveloped a training procedure for their ADALINE neural element that cameto be called the Widrow–Hoff least-mean-squares adaptive algorithm. Most of
Widrow’s experimental work was done using simulations on an IBM1620
computer. Their most complex network design was called a “MADALINE”(for many ADALINEs). A training procedure was developed for it by Stanford
Ph.D. student William Ridgway.14

4.2.3

The MINOS Systems at SRI

Rosenblatt’s success with perceptrons on pattern-recognition problems led to a
flurry of research efforts by others to duplicate and extend his results. During
the 1960s, perhaps the most significant pattern-recognition work using neuralnetworks was done at the Stanford Research Institute in Menlo Park,
California. There, Charles A. Rosen (1917–2002) headed a laboratory that was
attempting to etch microscopic vacuum tubes onto a solid-state substrate.
Rosen speculated that circuits containing these tubes might ultimately be“wired-up” to perform useful tasks using some of the training procedures
being explored by Frank Rosenblatt. SRI employed Rosenblatt as a consultant
to help in the design of an exploratory neural network.
When I interviewed for a position at SRI in 1960, a team in Rosen’s lab,
under the leadership of Alfred E. (Ted) Brain (1923–2004), had just aboutcompleted the construction of a small neural network called MINOS (Fig.
4.7). (In Greek mythology, Minos was a king of Crete and the son of Zeus andEuropa. After his death, Minos was one of the three judges in the underworld.)
Brain felt that computer simulations of neural networks were too slow forpractical applications, thus leading to his decision to build rather than toprogram. (The IBM 1620 computer being used at the same time by Widrow’sgroup at Stanford for simulating neural networks had a basic machine cycle of
21 microseconds and a maximum of 60,000 “digits” of random-access memory.)
For adjustable weights, MINOS used magnetic devices designed by Brain.

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4.2

Neural Networks

Rosenblatt stayed in close contact with SRI because he was interested in using
these magnetic devices as replacements for his motor-driven potentiometers.

Figure 4.7: MINOS. Note the input switches and corresponding indicator lights
in the second-from-the-left rack of equipment. The magnetic weights are at the
top of the third rack. (Photograph used with permission of SRI International.)
Rosen’s enthusiasm and optimism about the potential for neural networks
helped convince me to join SRI. Upon my arrival in July 1961, I was given adraft of Rosenblatt’s book to read. Brain’s team was just beginning work on
the construction of a large neural network, called MINOS II, a follow-on
system to the smaller MINOS. (See Fig. 4.8.)
Work on the MINOS systems was supported primarily by the U.S. Army
Signal Corps during the period 1958 to 1967. The objective of the MINOS
work was “to conduct a research study and experimental investigation oftechniques and equipment characteristics suitable for practical application tographical data processing for military requirements.” The main focus of the
project was the automatic recognition of symbols on military maps. Other
applications – such as the recognition of military vehicles, such as tanks, on
aerial photographs and the recognition of hand-printed characters – were also
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Pattern Recognition

attempted.15
In the first stage of processing by MINOS II, the input image was
replicated 100 times by a 10 × 10 array of plastic lenses. Each of theseidentical images was then sent through its own optical feature-detecting mask,
and the light through the mask was detected by a photocell and comparedwith a threshold. The result was a set of 100 binary (off–on) values. These
values were the inputs to a set of 63 neural elements (“A-units” inRosenblatt’s terminology), each with 100 variable magnetic weights. The 63
binary outputs from these neural elements were then translated into one of 64
decisions about the category of the original input image. (We constructed 64
equally distant “points” in the sixty-three-dimensional space and trained theneural network so that each input image produced a point closer to its own
prototype point than to any other. Each of these prototype points was one of
the 64 “maximal-length shift-register sequences” of 63 dimensions.)16

Figure 4.8: MINOS II: operator’s display board (left), an individual weightframe (middle), and weight frames with logic circuitry (right). (Photographs
used with permission of SRI International.)
During the 1960s, the SRI neural network group, by then called the
Learning Machines Group, explored many different network organizations andtraining procedures. As computers became both more available and more
powerful, we increasingly used simulations (at various computer centers) on
the Burroughs 220 and 5000 and on the IBM 709 and 7090. In the mid-1960s,
we obtained our own dedicated computer, an SDS 910. (The SDS 910,
developed at Scientific Data Systems, was the first computer to use silicontransistors.) We used that computer in conjunction with the latest version of
our neural network hardware (now using an array of 1,024 preprocessing
lenses), a combination we called MINOS III.

100
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4.2

Neural Networks

One of the most successful results with the MINOS III system was theautomatic recognition of hand-printed characters on FORTRAN coding sheets.
(In the 1960s, computer programs were typically written by hand and thenconverted to punched cards by key-punch operators.) This work was led byJohn Munson (1939–1972; Fig. 4.9), Peter Hart (1941– ; Fig. 4.9), andRichard Duda (1936– ; Fig. 4.9). The neural net part of MINOS III was used
to produce a ranking of the possible classifications for each character with aconfidence measure for each. For example, the first character encountered in astring of characters might be recognized by the neural net as a “D” with aconfidence of 90 and as an “O” with a confidence of 10. But accepting themost confident decision for each character might not result in a string that is alegal statement in the FORTRAN language – indicating that one or more of thedecisions was erroneous (where it is assumed that whoever wrote statementson the coding sheet wrote legal statements). Accepting the second or third
most confident choices for some of the characters might be required to produce
a legal string.

Figure 4.9: John Munson (left), Peter Hart (middle), and Richard Duda (right).
(Photographs courtesy of Faith Munson, of Peter Hart, and of Richard Duda.)
The overall confidence of a complete string of characters was calculated
by adding the confidences of the individual characters in the string. Then,
what was needed was a way of ranking these overall confidence numbers foreach of the possible strings resulting from all of the different choices for eachcharacter. Among this ranking of all possible strings, the system then selected
the most confident legal string.
As Richard Duda wrote, however, “The problem of finding the 1st, 2nd,
3rd,. . . most confident string of characters is by no means a trivial problem.”
The key to computing the ranking in an efficient manner was to use a method
called dynamic programming.17 (In a later chapter, we’ll see dynamic
programming used again in speech recognition systems.)
An illustration of a sample of the original source and the final output is101
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4

Pattern Recognition

shown in Fig. 4.10.

Figure 4.10: Recognition of FORTRAN characters. Input is above and output(with only two errors) is below. (Illustration used with permission of SRI International.)
After the neural net part of the system was trained, the overall system(which decided on the most confident legal string) was able to achieve a
recognition accuracy of just over 98% on a large sample of material that was
not part of what the system was trained on. Recognizing handwritten
characters with this level of accuracy was a significant achievement in the
1960s.18
Expanding its interests beyond neural networks, the Learning Machines
Group ultimately became the SRI Artificial Intelligence Center, which
continues today as a leading AI research enterprise.

4.3

Statistical Methods

During the 1950s and 1960s there were several applications of statisticalmethods to pattern-recognition problems. Many of these methods bore a close
resemblance to some of the neural network techniques. Recall that earlier I
explained how to decide which of two tones was present in a noisy radio signal.
A similar technique could be used for pattern recognition. For classifying
images (or other perceptual inputs), it was usual to represent the input by a102
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4.3

Statistical Methods

list of distinguishing “features” such as those used by Selfridge and hiscolleagues. In alphanumeric character recognition for example, one firstextracted features from the image of the character to be classified. Usually thefeatures had numerical values, such as the number of times lines of different
angles intersected the character or the length of the perimeter of the smallest
circle that completely enclosed the character. Selecting appropriate features
was often more art than science, but it was critical to good performance.
We’ll need a bit of elementary mathematical notation to help describe
these statistically oriented pattern-recognition methods. Suppose the list offeatures extracted from a character is {f1 , f2 , . . . , fi , . . . , fN }. I’ll abbreviate
this list by the bold-face symbol X. Suppose there are k categories,
C1 , C2 , . . . , Ci , . . . , Ck to which the character described by X might belong.
Using Bayes’s rule in a manner similar to that described earlier, the decision
rule is the following:
Decide in favor of that category for which p(X | Ci )p(Ci ) is largest,
where p(Ci ) is the a priori probability of category Ci and p(X | Ci )is the likelihood of X given Ci . These likelihoods can be inferredby collecting statistical data from a large sample of characters.
As I mentioned earlier, researchers in pattern recognition often describe thedecision process in terms of geometry. They imagine that the values of thefeatures obtained from an image sample can be represented as a point in a
multidimensional space. If we have several samples for each of, say, two knowncategories of data, we can represent these samples as scatterings of points inthe space. In character recognition, scattering can occur not only because theimage of the character might be noisy but also because characters in the same
category might be drawn slightly differently. I show a two-dimensional
example, with features f1 and f2 , in Fig. 4.11. From the scattering of points
in each category we can compute an estimate of the probabilities needed forcomputing likelihoods. Then, we can use the likelihoods and the priorprobabilities to make decisions.
I show in this figure the boundary, computed from the likelihoods and theprior probabilities, that divides the space into two regions. In one region, we
decide in favor of category 1; in the other, we decide in favor of category 2. I
also show a new feature point, X, to be classified. In this case, the position of
X relative to the boundary dictates that we classify X as a member ofcategory 1.
There are other methods also for classifying feature points. An interesting
example is the “nearest-neighbor” method. In that scheme, invented by E.
Fix and J. L. Hodges in 1951,19 a new feature point is assigned to the same
category as that sample feature point to which it is closest. In Fig. 4.11, thenew point X would be classified as belonging to category 2 using thenearest-neighbor method.
103
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Figure 4.11: A two-dimensional space of feature points and a separating boundary.
An important elaboration of the nearest-neighbor method assigns a new
point to the same category as the majority of the k closest points. Such a
decision rule seems plausible (in the case in which there are many, many
sample points of each category) because there being more sample points of
category Ci closer to an unknown point, X, than sample points of category Cj
is evidence that p(X | Ci )p(Ci ) is greater than p(X | Cj )p(Cj ). Expanding onthat general observation, Thomas Cover and Peter Hart rigorously analyzed
the performance of nearest-neighbor methods.20
Any technique for pattern recognition, even those using neural networks
or nearest neighbors, can be thought of as constructing separating boundaries
in a multidimensional space of features. Another method for constructing
boundaries using “potential functions” was suggested by the Russian scientists
M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer in the 1960s.21
Some important early books on the use of statistical methods in patternrecognition are ones by George Sebestyen,22 myself,23 and Richard Duda andPeter Hart.24 My book also describes some of the relationships between
statistical methods and those based on neural networks. The technology ofpattern recognition as of the late 1960s is nicely reviewed by George Nagy
(who had earlier been one of Frank Rosenblatt’s graduate students).25

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4.4

Applications of Pattern Recognition to Aerial Reconnaissance

4.4

Applications of Pattern Recognition to
Aerial Reconnaissance

The neural network and statistical methods for pattern recognition attracted
much attention in many aerospace and avionics companies during the late
1950s and early 1960s. These companies had ample research and development
budgets stemming from their contracts with the U.S. Department of Defense.
Many of them were particularly interested in the problem of aerial
reconnaissance, that is, locating and identifying “targets” in aerial
photographs. Among the companies having substantial research programs
devoted to this and related problems were the Aeronutronic Division of theFord Motor Co.,26 Douglas Aircraft Company (as it was known at that time),
General Dynamics, Lockheed Missiles and Space Division, and the Philco
Corporation. (Philco was later acquired by Ford in late 1961.)
I’ll mention some of the work at Philco as representative. There, Laveen
N. Kanal (1931– ), Neil C. Randall (1930– ), and Thomas Harley (1929– )worked on both the theory and applications of statistical pattern-recognition
methods. The systems they developed were for screening aerial photographs
for interesting military targets such as tanks. A schematic illustration of oneof their systems is shown in Fig. 4.12.27
Philco’s apparatus scanned material from 9-inch film negatives gathered
by a U2 reconnaissance airplane during U.S. Army tank maneuvers at Fort
Drum, New York. A small section of the scanned photograph, possibly
containing an M-48 tank (in standard position and size), was first processed toenhance edges, and the result was presented to the target detection system as
an array of 1’s and 0’s. The first of their systems used a 22 × 12 array; laterones used a 32 × 32 array as shown in Fig. 4.12. The array was thensegmented into 24 overlapping 8 × 8 “feature blocks.” The data in each feature
block were then subjected to a statistical test to decide whether or not thesmall area of the picture represented by this block contained part of a tank.
The statistical tests were based on a “training sample” of 50 images
containing tanks and 50 samples of terrain not containing tanks. For each8 × 8 feature block, statistical parameters were compiled from these samples todetermine a (linear) boundary in the sixty-four-dimensional space that best
discriminated the tank samples from the nontank samples.
Using these boundaries, the system was then tested on a different set of
50 images containing tanks and 50 images not containing tanks. For each test
image, the number of feature blocks deciding “tank present” was tallied to
produce a final numerical “score” (such as 21 out of the 24 blocks decided atank was present). This score could then be used to decide whether or not theimage contained a tank.
The authors stated that “the experimental performance of the statisticalclassification procedure exceeded all expectations.” Almost half of the test
105
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Pattern Recognition

Figure 4.12: A Philco tank-recognition system. (Adapted from Laveen N. Kanal
and Neal C. Randall, “Target Detection in Aerial Photography,” paper 8.3, Proceedings of the 1964 Western Electronics Show and Convention (WESCON), Los
Angeles, CA, Institute of Radio Engineers (now IEEE), August 25–28, 1964.)
samples had perfect scores (that is, all 24 feature blocks correctly
discriminated between tank and nontank). Furthermore, all of the test
samples containing tanks had a score greater than or equal to 11, and all of
the test samples not containing tanks had a score less than or equal to 7.
An early tank-detecting system at Philco was built with analog circuitry –not programmed on a computer. As Thomas Harley, the project leader for thissystem, later elaborated,28
It is important to remember the technological context of the era inwhich this work was done. The system we implemented had nobuilt-in computational capabilities. The weights in the linear
discriminant function were resistors that controlled the current
coming from the (binary) voltage source in the shift registerelements. Those currents were added together, and each feature
was recognized or not depending whether on the sum of those
106
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4.4

Applications of Pattern Recognition to Aerial Reconnaissancecurrents exceeded a threshold value. Those binary feature decisions
were then summed, again in an analog electrical circuit, not in acomputer, and again a decision [tank or no tank] was madedepending on whether the sum exceeded a threshold value.

In another system, the statistical classification was implemented by aprogram, called MULTINORM, running on the Philco 2000 computer.29 In
other experiments Philco used additional statistical tests to weight some of thefeature blocks more heavily than others in computing the final score. Kanal
told me that these experiments with weighting the outputs of the feature
blocks “anticipated the support vector machine (SVM) classification
idea. . . [by] using the first layer to identify the training samples close to theboundary between tanks and non-tanks.”30 (I’ll describe the important SVM
method in a later chapter.)
Of course, these systems had a rather easy task. All of the tanks were instandard position and were already isolated in the photograph. (The authors
mention, however, how the system could be adapted to deal with tanks
occurring in any position or orientation in the image.) The photograph in Fig.
4.13 shows a typical tank image. (The nontank images are similar, except
without the tank.)
I find the system interesting not only because of its performance but also
because it is a layered system (similar to Pandemonium and to thealpha-perceptron) and because it is an example in which the original image isdivided into overlapping subimages, each of which is independently processed.
As I’ll mention later, overlapping subimages play a prominent role in somecomputational models of the neocortex.
Unfortunately, the Philco reports giving details of this work aren’t readily
available.31 Furthermore, Philco and some of the other groups engaged in thiswork have disappeared. Here is what Tom Harley wrote me about the Philco
reports and about Philco itself:32
Most of the pattern recognition work done at Philco in the 1960s
was sponsored by the DoD [Department of Defense], and thereports were not available for public distribution. Since then, thecompany itself has really vanished into thin air. Philco was bought
by Ford Motor Company in 1961, and by 1966, they had
eliminated the Philco research labs where Laveen [Kanal] and I
were working. Ford tried to move our small pattern recognitiongroup to Newport Beach, CA [the location of Ford’s Aeronutronic
Division, whose pattern recognition group folded later also], andwhen we all decided not to go, they transferred us to theirCommunications Division, and told us to close out our patternrecognition projects. Laveen eventually went off to the Universityof Maryland, and in 1975, I transferred to the Ford Aerospace
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Pattern Recognition

Figure 4.13: A typical tank image. (Photograph courtesy of Thomas Harley.)Western Development Labs (WDL) in Palo Alto, where I worked
on large systems for the intelligence community. In later years,
what had been Philco was sold to Loral, and most of that was latersold to Lockheed Martin. I retired from Lockheed in 2001.
Approaches to AI problems involving neural networks and statisticaltechniques came to be called “nonsymbolic” to contrast them with the“symbol-processing” work being pursued by those interested in proving
theorems, playing games, and problem solving. These nonsymbolic approaches
found application mainly in pattern recognition, speech processing, andcomputer vision. Workshops and conferences devoted especially to those topics
began to be held in the 1960s. A subgroup of the IEEE Computer Society (thePattern Recognition Subcommittee of the Data Acquisition and108
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4.4

NOTES

Transformation Committee) organized the first “Pattern Recognition
Workshop,” which was held in Puerto Rico in October 1966.33 A second one(which I attended) was held in Delft, The Netherlands, in August 1968. In
1966, this subgroup became the IEEE Computer Society Pattern Analysis andMachine Intelligence (PAMI) Technical Committee, which continued toorganize conferences and workshops.34
Meanwhile, during the late 1950s and early 1960s, the symbol-processing
people did their work mainly at MIT, at Carnegie Mellon University, at IBM,and at Stanford University. I’ll turn next to describing some of what they did.

Notes
1. See http://www.nist.gov/public affairs/techbeat/tb2007 0524.htm. [89]2. Russell A. Kirsch et al., “Experiments in Processing Pictorial Information with a Digital
Computer,” Proceedings of the Eastern Joint Computer Conference, pp. 221–229, Instituteof Radio Engineering and Association for Computing Machinery, December 1957. [89]3. The proceedings of the conference were published in George L. Fischer Jr. et al., Optical
Character Recognition, Washington, DC: Spartan Books, 1962. [90]4. From J. Rabinow, “Developments in Character Recognition Machines at Rabinow
Engineering Company,” in George L. Fischer Jr. et al., op. cit., p. 27. [90]5. From http://www.sri.com/about/timeline/erma-micr.html. [90]6. Oliver G. Selfridge and Ulrich Neisser, “Pattern Recognition by Machine,” Scientific
American, Vol. 203, pp. 60–68, 1960. (Reprinted in Edward A. Feigenbaum and Julian
Feldman (eds.), Computers and Thought, pp. 237ff, New York: McGraw Hill, 1963.) [91]7. An early reference is Frank Rosenblatt, “The Perceptron: A Probabilistic Model for
Information Storage and Organization in the Brain,” Psychological Review, Vol. 65, pp.
386ff, 1958. [92]8. H. David Block, “The Perceptron: A Model for Brain Functioning,” Reviews of Modern
Physics, Vol. 34, No. 1, pp. 123–135, January 1962. [97]9. Albert B. J. Novikoff, “On Convergence Proofs for Perceptrons,” in Proceedings of the
Symposium on Mathematical Theory of Automata, pp. 615–622, Brooklyn, NY: Polytechnic
Press of Polytechnic Inst. of Brooklyn, 1963. [97]10. Nils J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying
Systems, New York: McGraw-Hill Book Co., 1965; republished as The Mathematical
Foundations of Learning Machines, San Francisco: Morgan Kaufmann Publishers, 1990. [97]11. Frank Rosenblatt, Principles of Neurodynamics, Washington, DC: Spartan Books, 1962.
[97]
12. Frank Rosenblatt, “A Description of the Tobermory Perceptron,” Collected TechnicalPapers, Vol. 2, Cognitive Systems Research Program, Cornell University, 1963. [97]13. Woodrow W. Bledsoe and Iben Browning, “Pattern Recognition and Reading by
Machine,” Proceedings of the Eastern Joint Computer Conference, pp. 225–232, New York:
Association for Computing Machinery, 1959. [97]14. William C. Ridgway, “An Adaptive Logic System with Generalizing Properties,”
Stanford Electronics Laboratories Technical Report 1556-1, Stanford University, Stanford,
CA, 1962. [98]109
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4

NOTES

15. For a description of MINOS II, see Alfred E. Brain, George Forsen, David Hall, andCharles Rosen, “A Large, Self-Contained Learning Machine,” Proceedings of the WesternElectronic Show and Convention, 1963. The paper was reprinted as Appendix C of an SRI
proposal and is available online at
http://www.ai.sri.com/pubs/files/rosen65-esu65-1tech.pdf. [100]16. For a discussion of shift-register codes and other codes, see W. Peterson,
Error-Correcting Codes, New York: John Wiley & Sons, 1961. Our technique was reported
in A. E. Brain and N. J. Nilsson, “Graphical Data Processing Research Study andExperimental Investigation,” Quarterly Progress Report No. 8, p. 11, SRI Report, June
1962; available online at http://www.ai.sri.com/pubs/files/1329.pdf. [100]17. Robert E. Larsen of SRI suggested using this method. The online encyclopedia
Wikipedia has a clear description of dynamic programming. See
http://en.wikipedia.org/wiki/Dynamic programming.
[101]
18. The technical details of the complete system are described in two papers: John Munson,“Experiments in the Recognition of Hand-Printed Text: Part I – Character Recognition,”and Richard O. Duda and Peter E. Hart, “Experiments in the Recognition of Hand-Printed
Text: Part II – Context Analysis,” AFIPS Conference Proceedings, (of the 1968 Fall Joint
Computer Conference), Vol. 33, pp. 1125–1149, Washington, DC: Thompson Book Co.,
1968. Additional information can be found in SRI AI Center Technical reports, available
online at http://www.ai.sri.com/pubs/files/1343.pdf andhttp://www.ai.sri.com/pubs/files/1344.pdf. [102]19. E. Fix and J. L. Hodges Jr., “Discriminatory analysis, nonparametric discrimination,”
USAF School of Aviation Medicine, Randolph Field, Texas, Project 21-49-004, Report 4,
Contract AF41(128)-31, February 1951. See also B. V. Dasarathy (ed.), Nearest Neighbor(NN) Norms: NN Pattern Classification Techniques, Los Alamitos, CA: IEEE Computer
Society Press, which is a reprint of 1951 unpublished work of Fix and Hodges. [103]20. Thomas M. Cover and Peter E. Hart, “Nearest Neighbor Pattern Classification,” IEEE
Transactions on Information Theory, pp. 21–27, January 1967. Available online at
http://ieeexplore.ieee.org/iel5/18/22633/01053964.pdf. [104]21. See M. A. Aizerman, E. M. Braverman, and L. I. Rozonoer, “Theoretical Foundations of
the Potential Function Method in Pattern Recognition Learning,” Automation and Remote
Control, Vol. 25, pp. 917–936, 1964, and A. G. Arkadev and E. M. Braverman, Computers
and Pattern Recognition, (translated from the Russian by W. Turski and J. D. Cowan),
Washington, DC: Thompson Book Co., Inc., 1967. [104]22. George S. Sebestyen, Decision-Making Processes in Pattern Recognition, Indianapolis,
IN: Macmillan Publishing Co., Inc., 1962. [104]23. Nils J. Nilsson, op. cit. [104]24. Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, New
York: John Wiley & Sons, 1973; updated version: Richard O. Duda, Peter E. Hart, andDavid G. Stork, Pattern Classification, 2nd Edition, New York: John Wiley & Sons, 2000.
[104]
25. George Nagy, “State of the Art in Pattern Recognition,” Proceedings of the IEEE, Vol.
56, No. 5, pp. 836–857, May 1968. [104]26. See, for example, Joseph K. Hawkins and C. J. Munsey, “An Adaptive System with
Direct Optical Input,” Proceedings of the IEEE, Vol. 55, No. 6, pp. 1084–1085, June 1967.
Available online for IEEE members at http://ieeexplore.ieee.org/iel5/5/31078/01446273.
pdf?tp=&arnumber=1446273&isnumber=31078. [105]27. Laveen N. Kanal and Neal C. Randall, “Target Detection in Aerial Photography,” paper
8.3, Proceedings of the 1964 Western Electronics Show and Convention (WESCON), Los
Angeles, CA, Institute of Radio Engineers (now IEEE), August 25–28, 1964. (Several other110
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4.4

NOTES

papers on pattern recognition were presented at this conference and are contained in theproceedings.) [105]28. Thomas Harley, personal e-mail communication, July 15, 2007. [106]29. Laveen N. Kanal and Neal C. Randall, op. cit.. [107]30. Laveen Kanal, personal e-mail communication, July 13, 2007. [107]31. Laveen N. Kanal, “Statistical Methods for Pattern Classification,” Philco Report, 1963;
originally appeared in T. Harley et al., “Semi-Automatic Imagery Screening Research Studyand Experimental Investigation,” Philco Reports VO43-2 and VO43-3, Vol. I, Sec. 6, andAppendix H, prepared for U.S. Army Electronics Research and Development Laboratory
under Contract DA-36-039-SC- 90742, March 29, 1963. [107]32. Thomas Harley, personal e-mail communication, July 11, 2007. [107]33. Laveen N. Kanal (ed.), Pattern Recognition, Proceedings of the IEEE Workshop on
Pattern Recognition, held at Dorado, Puerto Rico, Washington, DC: Thompson Book Co.,
1968. [109]34. See the Web page at http://tab.computer.org/pamitc/. [109]111
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NOTES

112
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5.1

Chapter 5

Early Heuristic Programs
5.1

The Logic Theorist and Heuristic Search

Just prior to the Dartmouth workshop, Newell, Shaw, and Simon had
programmed a version of LT on a computer at the RAND Corporation called
the JOHNNIAC (named in honor of John von Neumann). Later papers1
described how it proved some of the theorems in symbolic logic that wereproved by Russell and Whitehead in Volume I of their classic work, Principia
Mathematica.2 LT worked by performing transformations on Russell andWhitehead’s five axioms of propositional logic, represented for the computerby “symbol structures,” until a structure was produced that corresponded tothe theorem to be proved. Because there are so many different transformations
that could be performed, finding the appropriate ones for proving the given
theorem involves what computer science people call a “search process.”
To describe how LT and other symbolic AI programs work, I need to
explain first what is meant by a “symbol structure” and what is meant by“transforming” them. In a computer, symbols can be combined in lists, suchas (A, 7, Q). Symbols and lists of symbols are the simplest kinds of symbol
structures. More complex structures are composed of lists of lists of symbols,
such as ((B, 3), (A, 7, Q)), and lists of lists of lists of symbols, and so on.
Because such lists of lists, etc. can be quite complex, they are called“structures.” Computer programs can be written that transform symbol
structures into other symbol structures. For example, with a suitable program
the structure “(the sum of seven and five)” could be transformed into thestructure “(7 + 5),” which could further be transformed into the symbol “12.”Transforming structures of symbols and searching for an appropriate
problem-solving sequence of transformations lies at the heart of Newell andSimon’s ideas about mechanizing intelligence. In a later paper (the one theygave on the occasion of their receiving the prestigious Turing Award), they113
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Early Heuristic Programs

summarized the process as follows:3
The solutions to problems are represented as symbol structures. A
physical symbol system exercises its intelligence in problem solving
by search – that is, by generating and progressively modifying
symbol structures until it produces a solution structure.
...
To state a problem is to designate (1) a test for a class of symbol
structures (solutions of the problem), and (2) a generator ofsymbol structures (potential solutions). To solve a problem is togenerate a structure, using (2), that satisfies the test of (1).
Understanding in detail how LT itself used symbol structures and theirtransformations to prove theorems would require some mathematical andlogical background. The process is easier to explain by using one of AI’s
favorite “toy problems” – the “fifteen-puzzle.” (See Fig. 5.1.) The
fifteen-puzzle is one of several types of sliding-block puzzles. The problem is totransform an array of tiles from an initial configuration into a “goal”
configuration by a succession of moves of a tile into an adjacent empty cell.

Figure 5.1: Start (left) and goal (right) configurations of a fifteen-puzzle problem.
I’ll use a simpler version of the puzzle – one that uses a 3 × 3 array ofeight sliding tiles instead of the 4 × 4 array. (AI researchers haveexperimented with programs for solving larger versions of the puzzle also, suchas 5 × 5 and 6 × 6.)
Suppose we wanted to move the tiles from their configuration on the left
to the one on the right as illustrated in Fig. 5.2.
Following the Newell and Simon approach, we must first represent tile
positions for the computer by symbol structures that the computer can deal
114
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5.1

The Logic Theorist and Heuristic Search

Figure 5.2: The eight-puzzle.
with. I will represent the starting position by the following structure, which isa list of three sublists:((2, 8, 3), (1, 6, 4), (7, B, 5)).
The first sublist, namely, (2, 8, 3), names the occupants of the first row of the
puzzle array, and so on. B stands for the empty cell in the middle of the thirdrow.
In the same fashion, the goal configuration is represented by the following
structure:((1, 2, 3), (8, B, 4), (7, 6, 5)).
Next, we have to show how a computer can transform structures of thekind we have set up in a way that corresponds to the allowed moves of the
puzzle. Note that when a tile is moved, it swaps places with the blank cell;that is, the blank cell moves too. The blank cell can either move within its row
or can change rows.
Corresponding to these moves of the blank cell, when a tile moves within
its row, B swaps places with the number either to its left in its list (if there isone) or to its right (if there is one). A computer can easily make either ofthese transformations. When the blank cell moves up or down, B swaps places
with the number in the corresponding position in the list to the left (if there isone) or in the list to the right (if there is one). These transformations can also
be made quite easily by a computer program.
Using the Newell and Simon approach, we start with the symbol structure
representing the starting configuration of the eight-puzzle and apply allowedtransformations until a goal is reached. There are three transformations of thestarting symbol structure. These produce the following structures:((2, 8, 3), (1, 6, 4), (B, 7, 5)),((2, 8, 3), (1, 6, 4), (7, 5, B)),and((2, 8, 3), (1, B, 4), (7, 6, 5)).
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5

Early Heuristic Programs

None of these represents the goal configuration, so we continue to apply
transformations to each of these and so on until a structure representing thegoal is reached. We (and the computer) can keep track of the transformations
made by arranging them in a treelike structure such as shown in Fig. 5.3.
(The arrowheads on both ends of the lines representing the transformations
indicate that each transformation is reversible.)

Figure 5.3: A search tree.
This version of the eight-puzzle is relatively simple, so not manytransformations have to be tried before the goal is reached. Typically though(especially in larger versions of the puzzle), the computer would be swampedby all of the possible transformations – so much so that it would nevergenerate a goal expression. To constrain what was later called “thecombinatorial explosion” of transformations, Newell and Simon suggested
using “heuristics” to generate only those transformations guessed as likely to
be on the path to a solution.
In one of their papers about LT, they wrote “A process that may solve a
problem, but offers no guarantees of doing so, is called a heuristic for thatproblem.” Rather than blindly striking out in all directions in a search for a
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5.1

The Logic Theorist and Heuristic Search

proof, LT used search guided by heuristics, or “heuristic search.” Usually, as
was the case with LT, there is no guarantee that heuristic search will be
successful, but when it is successful (and that is quite often) it eliminates
much otherwise fruitless search effort.
The search for a solution to an eight-puzzle problem involves growing thetree of symbol structures by applying transformations to the “leaves” of thetree and thus extending it. To limit the growth of the tree, we should use
heuristics to apply transformations only to those leaves thought to be on theway to a solution. One such heuristic might be to apply a transformation tothat leaf with the smallest number of tiles out of position compared to the goal
configuration. Because sliding tile problems have been thoroughly studied,
there are a number of heuristics that have proved useful – ones much better
than the simple number-of-tiles-out-of-position one I have just suggested.
Using heuristics keyed to the problem being solved became a major theme
in artificial intelligence, giving rise to what is called “heuristic programming.”
Perhaps the idea of heuristic search was already “in the air” around the timeof the Dartmouth workshop. It was implicit in earlier work by Claude
Shannon. In March 1950, Shannon, an avid chess player, published a paper
proposing ideas for programming a computer to play chess.4 In his paper,
Shannon distinguished between what he called “type A” and “type B”
strategies. Type A strategies examine every possible combination of moves,whereas type B strategies use specialized knowledge of chess to focus on linesof play thought to be the most productive. The type B strategies depended onwhat Newell and Simon later called heuristics. And Minsky is quoted assaying “. . . I had already considered the idea of heuristic search obvious andnatural, so that the Logic Theorist was not impressive to me.”5
It was recognized quite early in AI that the way a problem is set up, its“representation,” is critical to its solution. One example of how arepresentation affects problem solving is due to John McCarthy and is called
the “mutilated checkerboard” problem.6 Here’s the problem: “Two diagonally
opposite corner squares are removed from a checkerboard. Is it possible tocover the remaining squares with dominoes?” (A domino is a rectangular tilethat covers two adjacent squares.) A naive way of searching for a solution
would be to try to place dominoes in all possible ways over the checkerboard.
But, if one uses the information that a checkerboard consists of 32 squares of
one color and 32 of another color, and that the opposite corner squares are of
the same color, then one realizes that the mutilated board consists of 30
squares of one color and 32 of another. Because a domino covers two squaresof opposite colors, there is no way that a set of them can cover the remainingcolors. McCarthy was interested in whether or not people could come up with“creative” ways to formulate the puzzle so that it could be solved bycomputers using methods based on logical deduction.
Another classic puzzle that has been used to study the effects of different
representations is the “missionary and cannibals” problem: Three cannibals
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5

Early Heuristic Programsand three missionaries must cross a river. Their boat can only hold two
people. If the cannibals outnumber the missionaries, on either side of the river,the missionaries on that side perish. Each missionary and each cannibal can
row the boat. How can all six get across the river safely? Most people have notrouble formulating this puzzle as a search problem, and the solution isrelatively easy. But it does require making one rather nonintuitive step. The
computer scientist and AI researcher Saul Amarel (1928–2002) wrote amuch-referenced paper analyzing this puzzle and various extended versions of
it in which there can be various numbers of missionaries and cannibals.7 (Theextended versions don’t appear to be so easy.) After moving from one
representation to another, Amarel finally developed a representation for ageneralized version of the problem whose solution required virtually no search.
AI researchers are still studying how best to represent problems and, mostimportantly, how to get AI systems to come up with their own representations.

5.2

Proving Theorems in Geometry

Nathan Rochester returned to IBM after the Dartmouth workshop excitedabout discussions he had had with Marvin Minsky about Minsky’s ideas for a
possible computer program for proving theorems in geometry. He described
these ideas to a new IBM employee, Herb Gelernter (1929– ). Gelernter soon
began a research project to develop a geometry-theorem-proving machine. He
presented a paper on the first version of his program at a conference in Parisin June 1959,8 acknowledging that[t]he research project itself is a consequence of the Dartmouth
Summer Research Project on Artificial Intelligence held in 1956,
during which M. L. Minsky pointed out the potential utility of the
diagram to a geometry theorem-proving machine.
Gelernter’s program exploited two important ideas. One was the explicit
use of subgoals (sometimes called “reasoning backward” or “divide andconquer”), and the other was the use of a diagram to close off futile searchpaths.
The strategy taught in high school for proving a theorem in geometry
involves finding some subsidiary geometric facts from which, if true, thetheorem would follow immediately. For example, to prove that two angles areequal, it suffices to show that they are corresponding angles of two“congruent” triangles. (A triangle is congruent to another if it can betranslated and rotated, possibly even flipped over, in such a way that itmatches the other exactly.) So now, the original problem is transformed into
the problem of showing that two triangles are congruent. One way (among
others) to show that two triangles are congruent is to show that twocorresponding sides and the enclosed angle of the two triangles all have the118
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5.2

Proving Theorems in Geometry

same sizes. This backward reasoning process ends when what remains to beshown is among the premises of the theorem.
Readers familiar with geometry will be able to follow the illustrative
example shown in Fig. 5.4. There, on the left-hand side, we are given triangle
ABC with side AB equal to side AC and must prove that angle ABC is equal toangle ACB. The triangle on the right side is a flipped-over version of triangle
ABC.

Figure 5.4: A triangle with two equal sides (left) and its flipped-over version(right).
Here is how the proof goes: If we could prove that triangle ABC iscongruent to triangle ACB, then the theorem would follow because the twoangles are corresponding angles of the two triangles. These two triangles canbe proved congruent if we could establish that side AB (of triangle ABC) isequal to side AC (of triangle ACB) and that side AC (of triangle ABC) is equal
to side AB (of triangle ACB) and that angle A (of triangle ABC) is equal toangle A (of triangle ACB). But the premises state that side AB is equal to sideAC, and these lengths don’t change in the flipped-over triangle. Similarly,
angle A is equal to its flipped-over version – so we have our proof.
Before continuing my description of Gelernter’s program, a short historicaldigression is in order. The geometry theorem just proved is famous – being thefifth proposition in Book I of Euclid’s Elements. Because Euclid’s proof of the
proposition was a difficult problem for beginners it became known as the ponsasinorum or “fools bridge.” The proof given here is simpler than Euclid’s – aversion of it was given by Pappus of Alexandria (circa 290–350 ce).
Minsky’s “hand simulation” of a program for proving theorems ingeometry, discussed at Dartmouth, came up with this very proof (omitting
what I think is the helpful step of flipping the triangle over). Minsky wrote9
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5

Early Heuristic Programs
In 1956 I wrote two memos about a hand-simulated program forproving theorems in geometry. In the first memo, the procedurefound the simple proof that if a triangle has two equal sides thenthe corresponding angles are equal. It did this by noticing that
triangle ABC was congruent to triangle CBA because of“side-angle-side.” What was interesting is that this was found aftera very short search – because, after all, there weren’t many things
to do. You might say the program was too stupid to do what aperson might do, that is, think, “Oh, those are both the same
triangle. Surely no good could come from giving it two different
names.” (The program has a collection of heuristic methods forproving Euclid-Like theorems, and one was that “if you want toprove two angles are equal, show that they’re corresponding partsof congruent triangles.” Then it also had several ways todemonstrate congruence. There wasn’t much more in that firstsimulation.) But I can’t find that memo anywhere.

As Minsky said, this is a very easy problem for a computer. Gelernter’s
program proved much more difficult theorems, and for these his use of adiagram was essential. The program did not literally draw and look at a
diagram. Instead, as Gelernter wrote,[The program is] supplied with the diagram in the form of a list of
possible coordinates for the points named in the theorem. This
point list is accompanied by another list specifying the points
joined by segments. Coordinates are chosen to reflect the greatest
possible generality in the figures.
So, for example, the points named in the problem about proving two
angles equal are the vertices of the triangle ABC, namely, points A and B and
C. Coordinates for each of these points are chosen, and care is taken to makesure that these coordinates do not happen to satisfy any special unnamed
properties.
Gelernter’s program worked by setting up subgoals and subsubgoals suchas those I used in the example just given. It then searched for a chain of theseending in subgoals that could be established directly from the premises.
Before any subgoal was selected by the program to be worked on however, it
was first tested to see whether it held in the diagram. If it did hold, it might
possibly be provable and could therefore be considered as a possible route to a
proof. But, if it did not hold in the diagram, it could not possibly be true.
Thus, it could be eliminated from further consideration, thereby “pruning” thesearch tree and saving what would certainly be fruitless effort. Later work in
AI would also exploit “semantic” information of this sort.
We can see similarities between the strategies used in the geometry
program and those used by humans when we solve problems. It is common for120
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5.3

The General Problem Solver

us to work backward – transforming a hard problem into subproblems andthose into subsubproblems and so on until finally the problems are trivial.
When a subproblem has many parts, we know that we must solve all of them.
We also recognize when a proposed subproblem is patently impossible andthus can reject it. The next program I describe was based explicitly on what
its authors thought were human problem-solving strategies.

5.3

The General Problem Solver

At the same 1959 Paris conference where Gelernter presented his program,
Allen Newell, J. C. Shaw, and Herb Simon gave a paper describing their recent
work on mechanizing problem solving.10 Their program, which they called the“General Problem Solver (GPS),” was an embodiment of their ideas about howhumans solve problems. Indeed, they claimed that the program itself was atheory of human problem-solving behavior. Newell and Simon were among
those who were just as interested (perhaps even more interested) in explaining
the intelligent behavior of humans as they were in building intelligent
machines. They wrote “It is often argued that a careful line must be drawn
between the attempt to accomplish with machines the same tasks that humans
perform, and the attempt to simulate the processes humans actually use toaccomplish these tasks. . . . GPS maximally confuses the two approaches – withmutual benefit.”11
GPS was an outgrowth of their earlier work on the Logic Theorist in that
it was based on manipulating symbol structures (which they believed humans
did also). But GPS had an important additional mechanism among its
symbol-manipulating strategies. Like Gelernter’s geometry program, GPStransformed problems into subproblems, and so on. GPS’s innovation was tocompute a “difference” between a problem to be solved (represented as asymbol structure) and what was already known or given (also represented as asymbol structure). The program then attempted to reduce this difference byapplying some symbol-manipulating “operator” (known to be relevant to thisdifference) to the initial symbol structure. Newell and Simon called thisstrategy “means–ends analysis.” (Note the similarity to feedback control
systems, which continuously attempt to reduce the difference between acurrent setting and a desired setting.) To do so, it would have to show thatthe operator’s applicability condition was satisfied – a subproblem. The
program then started up another version of itself to work on this subproblem,
looking for a difference and so on.

For example, suppose the goal is to have Sammy at school when Sammy is
known to be at home.12 GPS computes a “difference,” namely, Sammy is in
the wrong place, and it finds an operator relevant to reducing this difference,
namely, driving Sammy to school. To drive Sammy to school requires that thecar be in working order. To make the problem interesting, we’ll suppose that

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5

Early Heuristic Programs

the car’s battery is dead, so GPS can’t apply the drive-car operator becausethat operator requires a working battery. Getting a working battery is a
subproblem to which GPS can apply a version of itself. This “lower” version of
GPS computes a difference, namely, the need for a working battery, and it finds
an operator, namely, calling a mechanic to come and install a new battery. To
call a mechanic requires having a phone number (and let us suppose we haveit), so GPS applies the call-mechanic operator, resulting in the mechaniccoming to install a new battery. The lower version of GPS has successfully
solved its problem, so the superordinate GPS can now resume – noting that thecondition for drive-car, namely, having a working battery, is satisfied. So GPS
applies this operator, Sammy gets to school, and the original problem issolved. (This example illustrates the general workings of GPS. A real one using
actual symbol structures, differences, and operators with their conditions andso on would be cumbersome but not more revealing.)
When GPS works on subproblems by starting up a new version of itself, it
uses a very important idea in computer science (and in mathematics) called“recursion.” You might be familiar with the idea that computer programmers
organize complex programs hierarchically. That is, main programs fire up
subprograms, which might fire-up subsubprograms, and so on. When a main
program “calls” a subprogram, the main program suspends itself until the
subprogram completes what it is supposed to do (possibly handing back data
to the main program), and then the main program resumes work. In AI (andin other applications also), it is common to have a main program call a versionof itself – taking care that the new version works on a simpler problem so as to
avoid endless repetition and “looping.” Having a program call itself is called“recursion.”
Do people use subprograms and recursion in their own thinking? Quite
possibly, but their ability to recall how to resume what some higher level
thought process was doing when that process starts up a chain of lower levelprocesses is certainly limited. I don’t believe that GPS attempted to mimic
this limitation of human thinking.
Newell and Simon believed that the methods used by GPS could be used
to solve a wide variety of different problems, thus giving rise to the term
“general.” To apply it to a specific problem, a “table of differences” for thatproblem would have to be supplied. The table would list all the possible
differences that might arise and match them to operators, which, for thatproblem, would reduce the corresponding differences. GPS was, in fact, applied
to a number of different logical problems and puzzles13 and inspired later workin both artificial intelligence and in cognitive science. Its longevity as aproblem-solving program itself and as a theory of human problem solving was
short, however, and lives on only through its various descendants (about whichmore will be discussed later).
Heuristic search procedures were used in a number of AI programs
developed in the early 1960s. For example, another one of Minsky’s Ph.D.
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5.4

Game-Playing Programs

students, James Slagle, programmed a system called SAINT that could solve
calculus problems, suitably represented as symbol structures. It solved 52 of
54 problems taken from MIT freshman calculus final examinations.14 Much
use of heuristics was used in programs that could play board games, a subject
to which I now turn.

5.4

Game-Playing Programs

I have already mentioned some of the early work of Shannon and of Newell,Shaw, and Simon on programs for playing chess. Playing excellent chessrequires intelligence. In fact, Newell, Shaw, and Simon wrote that if “onecould devise a successful chess machine, one would seem to have penetrated tothe core of human intellectual endeavor.”15
Thinking about programs to play chess goes back at least to Babbage.
According to Murray Campbell, an IBM researcher who helped design aworld-champion chess-playing program (which I’ll mention later), Babbage’s
1845 book, The Life of a Philosopher, contains the first documented discussionof programming a computer to play chess.16 Konrad Zuse, the German
designer and builder of the Z1 and Z3 computers, used his programming
language called Plankalkül to design a chess-playing program in the early
1940s.
In 1946 Turing mentioned the idea of a computer showing “intelligence,”with chess-playing as a paradigm.17 In 1948, Turing and his former
undergraduate colleague, D. G. Champernowne, began writing a chessprogram. In 1952, lacking a computer powerful enough to execute theprogram, Turing played a game in which he simulated the computer, taking
about half an hour per move. (The game was recorded. You can see it at
http://www.chessgames.com/perl/chessgame?gid=1356927.) The program lost
to a colleague of Turing, Alick Glennie; however, it is said that the program
won a game against Champernowne’s wife.18
After these early programs, work on computer chess programs continued,
with off-again–on-again effort, throughout the next several decades. According
to John McCarthy, Alexander Kronrod, a Russian AI researcher, said “Chessis the Drosophila of AI” – meaning that it serves, better than more
open-ended intellectual tasks do, as a useful laboratory specimen for research.
As Minsky said, “It is not that the games and mathematical problems arechosen because they are clear and simple; rather it is that they give us, for thesmallest initial structures, the greatest complexity, so that one can engage
some really formidable situations after a relatively minimal diversion into
programming.”19 Chess presents very difficult problems for AI, and it was not
until the mid-1960s that the first competent chess programs appeared. I’ll
return to discuss these in a subsequent chapter.

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5

Early Heuristic ProgramsMore dramatic early success, however, was achieved on the simpler gameof checkers (or draughts as the game is known in British English). Arthur
Samuel (Fig. 5.5) began thinking about programming a computer to playcheckers in the late 1940s at the University of Illinois where he was a Professorof Electrical Engineering. In 1949, he joined IBM’s Poughkeepsie Laboratory
and completed his first operating checkers program in 1952 on IBM’s 701
computer. The program was recoded for the IBM 704 in 1954. According toJohn McCarthy,20 “Thomas J. Watson Sr., the founder and President of IBM,
remarked that the demonstration [of Samuel’s program] would raise the priceof IBM stock 15 points. It did.”[Apparently, Samuel was not the first to write a checkers-playing program.
According to the Encyclopedia Brittanica, Online, “The earliest successful AI
program was written in 1951 by Christopher Strachey, later director of theProgramming Research Group at the University of Oxford. Strachey’s
checkers (draughts) program ran on the Ferranti Mark I computer at the
University of Manchester, England. By the summer of 1952 this program
could play a complete game of checkers at a reasonable speed.”]21
Samuel’s main interest in programming a computer to play checkers was
to explore how to get a computer to learn. Recognizing the “time consuming
and costly procedure[s]” involved in programming, Samuel wrote“Programming computers to learn from experience should eventually eliminate
the need for much of this detailed programming effort.”22 Samuel’s efforts
were among the first in what was to become a very important part of artificial
intelligence, namely, “machine learning.” His first program that incorporated
learning was completed in 1955 and demonstrated on television on February
24, 1956.
Before describing his learning methods, I’ll describe in general how
Samuel’s program chose moves. The technique is quite similar to how moves
were chosen in the eight-puzzle I described earlier. Except now, provision must
be made for the fact that the opponent chooses moves also. Again, a tree ofsymbolic expressions, representing board positions, is constructed. Startingwith the initial configuration, all possible moves by the program (under theassumption that the program moves first) are considered. The result is all thepossible resulting board configurations branching out from the starting
configuration. Then, from each of these, all possible moves of the opponent areconsidered – resulting in more branches, and so on.
If such a tree could be constructed for an entire game, a winning movecould be computed by examination of the tree. Unfortunately, it has been
estimated that there are about 5 × 1020 possible checkers positions. A leading
expert in programming computers to play games, Jonathan Schaeffer, was able
to “solve” checkers (showing that optimal play by both players results in adraw) by time-consuming analysis of around 1014 positions. He wrote me that
“This was the result of numerous enhancements aimed at focussing the searchat the parts of the search space where we were most likely to find what we
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5.4

Game-Playing Programs

Figure 5.5: Arthur Samuel. (Photograph courtesy of Donna Hussain, Samuel’s
daughter.)needed.”23 I’ll describe his work in more detail later.
Samuel’s program then could necessarily construct only a part of the tree
– that is, it could look only a few moves ahead. How far ahead it looked, along
various of its branches, depended on a number of factors that need not concern
us here. (They involved such matters as whether or not an immediate capture
was possible.) Looking ahead about three moves was typical, although somebranches might be explored (sparsely) to a depth of as many as ten moves. A
diagram from Samuel’s paper, shown in Fig. 5.6, gives the general idea.
Samuel said that the “actual branchings are much more numerous.”
So, how is the program to choose a move from such an incomplete tree?
This problem is faced by all game-playing programs, and they all use methods
that involve computing a score for the positions at the tips, or “leaves,” of thetree (that is, the leaves of the incomplete tree generated by the program) andthen “migrating” this score back up to the positions resulting from moves fromthe current position. First, I will describe how to compute the score, then how
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5

Early Heuristic Programs

Figure 5.6: An illustrative checkers game tree. (From p. 74 of Edward A.
Feigenbaum and Julian Feldman (eds.), Computers and Thought, New York:
McGraw Hill, 1963.)
to migrate it back, and then how Samuel used learning methods to improve
performance.
Samuel’s program first computed the points to be awarded to positions at
the leaves of the tree based on their overall “goodness” from the point of viewof the program. Among the features contributing points were the relative pieceadvantage (with kings being worth more than ordinary pieces), the overall“mobility” (freedom to move) of the program’s pieces, and center control.
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5.4

Game-Playing Programs(The program had access to 38 such features but only used the 16 best of theseat any one time.) The points contributed by each feature were then multiplied
by a “weight” (reflecting the relative importance of its corresponding feature),
and the result was summed to give an overall score for a position.
Starting with a position immediately above those at the tip of the tree, if
it is a position for which it is the program’s turn to move, we can assume thatthe program would want to move to that position with the highest score, sothat highest score is migrated back to this “immediately above” position. If,
however, it is a position from which it is the opponent’s turn to move, we
assume that the opponent would want to move to that position with the lowest
score. In that case, the lowest score is migrated back to this immediately
above position. This alternately “highest–lowest” migration strategy iscontinued back all the way up the tree and is called the “minimax” strategy.
[A simple modification of this strategy, called the “alpha–beta” procedure,is used to infer (correctly) from already-migrated scores that certain branches
need not be examined at all – thus allowing other branches to be explored
more deeply. Opinions differ about who first thought of this important
modification. McCarthy and Newell and Simon all claim credit. Samuel told
me he used it but that it was too obvious to write about.]
If one assumes that it is the program’s turn to move from the current
position, and that scores have already been migrated back to the positions justbelow it, the program would make its move to that position with the highest
score. And then the game would continue with the opponent making a move,another stage of tree growth, score computation and migration, and so on
until one side wins or loses.
One of the learning methods in Samuel’s program adjusted the values of
the weights used by the scoring system. (Recall that weight adjustments in
Pandemonium and in neural networks were ways in which those systems
learned.) The weights were adjusted so that the score of a board position (ascomputed by the sum of the weighted feature scores) moved closer to the valueof its migrated score after finishing a search. For example, if the score of an
initial position was computed (using the weights before adjustment) to be 22,
and the migrated score of that position after search was 30, then the weights
used to compute the score of the initial position were adjusted in a manner sothat the new score (using the adjusted value of the weights) was made closer
to 30, say 27. (This technique foreshadowed a very important learning method
later articulated by Richard Sutton called “temporal-difference learning.”) The
idea here was that the migrated score, depending as it did on looking ahead in
the game, was presumed to be a better estimate than the original score. The
estimating procedure was thereby improved so that it produced values moreconsistent with the “look-ahead” score.
Samuel also used another method called “rote learning” in which theprogram saved various board positions and their migrated scores encountered

127
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5

NOTES

during actual play. Then, at the end of a search, if a leaf position encountered
was the same as one of these stored positions, its score was already known(and would not have to be computed using the weights and features.) The
known score, based as it was on a previous search, would presumably be abetter indicator of position value than would be the computed score.
Samuel’s program also benefitted from the use of “book games,” which
are records of the games of master checkers players. In commenting about
Samuel’s work, John McCarthy wrote that “checker players have many
volumes of annotated games with the good moves distinguished from the bad
ones. Samuel’s learning program used Lee’s Guide to Checkers24 to adjust its
criteria for choosing moves so that the program would choose those thought
good by checker experts as often as possible.”
Samuel’s program played very good checkers and, in the summer of 1962,
beat Robert Nealey, a blind checkers master from Connecticut. (You can see agame played between Mr. Nealey and Samuel’s program at
http://www.fierz.ch/samuel.htm.) But, according to Jonathan Schaeffer andRobert Lake, “In 1965, the program played four games each against Walter
Hellman and Derek Oldbury (then playing a match for the WorldChampionship), and lost all eight games.”25

Notes
1. A. Newell and H. A. Simon, “The Logic Theory Machine: A Complex Information
Processing System,” Proceedings IRE Transactions on Information Theory, Vol. IT-2, pp.
61–79, September 1956, and A. Newell, J. C. Shaw, and H. A. Simon, “Empirical
Explorations of the Logic Theory Machine: A Case Study in Heuristics,” Proceedings of the1957 Western Joint Computer Conference, Institute of Radio Engineers, pp. 218–230, 1957.
[113]
2. Alfred North Whitehead and Bertrand Russell, Principia Mathematica, Vol. 1,
Cambridge: Cambridge University Press, 1910. [113]3. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols
and Search,” Communications of the ACM, Vol. 19, No. 3, pp. 113–126, March 1976. [114]
4. Claude E. Shannon, “Programming a Computer for Playing Chess,” Philosophical
Magazine, Ser. 7, Vol. 41, No. 314, March 1950. Text available online at
http://www.pi.infn.it/∼carosi/chess/shannon.txt. (The paper was first presented in March
1950 at the National Institute for Radio Engineers Convention in New York.) [117]5. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History andProspects of Artificial Intelligence, p. 106, San Francisco: W. H. Freeman and Co., 1979.
[117]
6. John McCarthy, “A Tough Nut for Theorem Provers,” Stanford Artificial Intelligence
Project Memo No. 16, July 17, 1964; available online at
http://www-formal.stanford.edu/jmc/toughnut.pdf. [117]7. Saul Amarel, “On Representations of Problems of Reasoning About Actions,” in Donald
Michie (ed.), Machine Intelligence 3, pp. 131–171, Edinburgh: Edinburgh University Press,
1968. [118]128
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5.4

NOTES

8. Herbert Gelernter, “Realization of a Geometry-Theorem Proving Machine,” Proceedingsof the International Conference on Information Processing”, pp. 273–282, Paris: UNESCO
House, Munich: R. Oldenbourg, and London: Butterworths, 1960. Also in Edward A.
Feigenbaum and Julian Feldman (eds.), Computers and Thought, pp. 134–152, New York:
McGraw Hill, 1963. [118]9. From http://www.math.niu.edu/∼rusin/known-math/99/minsky. [119]10. Allen Newell, J. C. Shaw, and Herbert A. Simon, “Report on a General Problem-Solving
Program,” Proceedings of the International Conference on Information Processing”, pp.
256–264, Paris: UNESCO House, Munich: R. Oldenbourg, and London: Butterworths, 1960.
[121]
11. For more about GPS as a theory and explanation for human problem solving, see Allen
Newell and Herbert Simon, “GPS, a Program That Simulates Human Thought,” in H.
Billings (ed.), Lernende Automaten, pp. 109–124, Munich: R. Oldenbourg KG, 1961.
Reprinted in Computers and Thought, pp. 279–293. [121]12. I adapt an example fromhttp://www.math.grinnell.edu/∼stone/events/scheme-workshop/gps.html. [121]13. See George Ernst and Allen Newell, GPS: A Case Study in Generality and Problem
Solving, New York: Academic Press, 1969. [122]14. James R. Slagle, “A Heuristic Program That Solves Symbolic Integration Problems in
Freshman Calculus,” Ph.D. dissertation, MIT, May 1961. For an article about SAINT, seeJames R. Slagle, “A Heuristic Program That Solves Symbolic Integration Problems in
Freshman Calculus,” Journal of the ACM, Vol. 10, No. 4, pp. 507–520, October 1963. [123]15. Allen Newell, J. Shaw, and Herbert Simon, “Chess-Playing Programs and the Problem
of Complexity,” IBM Journal of Research and Development, Vol. 2, pp. 320–335, October
1958. [123]16. Chapter 5 of Hal’s Legacy: 2001’s Computer as Dream and Reality, David G. Stork(ed.), Cambridge, MA: MIT Press, 1996. See the Web site at
http://mitpress.mit.edu/e-books/Hal/chap5/five3.html. [123]17. Andrew Hodges, “Alan Turing and the Turing Test,” in Parsing the Turing Test:
Philosophical and Methodological Issues in the Quest for the Thinking Computer, Robert
Epstein, Gary Roberts, and Grace Beber (ed.), Dordrecht, The Netherlands: Kluwer, 2009.
See A. M. Turing, “Proposed Electronic Calculator,” report for National PhysicalLaboratory, 1946, in A. M. Turing’s ACE Report of 1946 and Other Papers, B. E.
Carpenter and R. W. Doran (eds.), Cambridge, MA: MIT Press, 1986. [123]18. http://en.wikipedia.org/wiki/Alan Turing. [123]19. Marvin Minsky (ed.), “Introduction,” Semantic Information Processing, p. 12,
Cambridge, MA: MIT Press, 1968. [123]20. From a Web retrospective at
http://www-db.stanford.edu/pub/voy/museum/samuel.html. [124]21. See Christopher Strachey, “Logical or Non-mathematical Programmes,” Proceedings ofthe 1952 ACM National Meeting (Toronto), pp. 46–49, 1952. [124]
22. Arthur L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,”
IBM Journal of Research and Development, Vol. 3, No. 3, pp. 210–229, 1959. Reprinted in
Edward A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, p. 71, New
York: McGraw Hill, 1963. [124]23. E-mail of February 14, 2009. [125]24. John W. Dawson, Lee’s Guide to the Game of Draughts, Revised Edition, London: E.
Marlborough, 1947. [128]129
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5

NOTES

25. Jonathan Schaeffer and Robert Lake, “Solving the Game of Checkers,” Games of No
Chance, pp. 119–133, MSRI Publications, Vol. 29, 1996. (Available online at
http://www.msri.org/communications/books/Book29/files/schaeffer.pdf.) [128]130
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6.1

Chapter 6

Semantic Representations
The computer programs I have described so far performed transformations on
relatively simple symbol structures, which were all that were required for themathematical problems, puzzles, and games that these programs dealt with.
The main effort was in coming up with and using problem-specific heuristics(such as features to be used in computing the value of a checkers position, forexample) to limit the number of transformations of these structures insearches for solutions. As Minsky put it, “The most central idea of thepre-1962 period was that of finding heuristic devices to control the breadth ofa trial-and-error search.”1 In the early 1960s, several Ph.D. research projects,
some performed under Minsky’s direction at MIT, began to employ more
complex symbol structures in programs for performing various intellectualtasks. Because of their rich, articulated content of information about theirproblem topic, these structures were called semantic representations.2 As
Minsky wrote, “Within the small domain in which each program operates, theperformance [of these programs] is not too bad compared with some humanactivities. . . . But much more important than what these particularexperiments achieve are the methods they use to achieve what they do, foreach is a first trial of previously untested ideas.”3 I’ll describe some examplesof these sorts of projects and the new methods that they employed.

6.1

Solving Geometric Analogy Problems

Thomas G. Evans (1934– ) programmed a system that was able to perform
well on some standard geometric analogy tests. It was apparently the largest
program written up to that time in John McCarthy’s new programming
language, LISP (which I’ll describe later). I quote from an article based on
Evans’s 1963 dissertation, which presented this work:4131
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Semantic Representations
We shall be considering the solution by machine of so-called“geometric-analogy” intelligence-test questions. Each member ofthis class of problems consists of a set of labeled line drawings. The
task to be performed can be described by the question: “Figure A
is to Figure B as Figure C is to which of the following figures?” Forexample [in Fig. 6.1] it seems safe to say that most people would
agree with the program we are about to describe, in choosing[number 4] as the desired answer.

Figure 6.1: An analogy problem.
He further noted that “problems of this type are widely regarded as
requiring a high degree of intelligence for their solution and in fact are used asa touchstone of intelligence in some general intelligence tests used for college
admission and other purposes.” So, again, AI research concentrated onmechanizing tasks requiring human intelligence.
Evans’s program first transformed the diagrams presented to it so thatthey revealed how they were composed out of parts. He called these“articular” representations. Of the possibly several decompositions possible,
the one chosen by the program depended on its “context.” (This choice is one
example of a heuristic used by the program.) For example, the diagram

could either be decomposed into132
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6.1

Solving Geometric Analogy Problems

andor intoand

But if the analogy problem contained another diagram (part of the context):

then the first decomposition would be chosen.
Evans represented diagrams and their parts as complex symbol structuresconsisting of rather elaborate combinations of lists and lists of lists whose
elements indicated which parts were inside or outside (or above or below)
which other parts, and so on. Those details need not concern us here, but they
did allow Evans to specify “rules” for his program that could be used to showhow one diagram could be transformed into another. The program was able toinfer which combinations of these rules transformed Figure A of a given
problem into Figure B. Then it could apply this transformation to Figure C. If
one of the multiple-choice answers resulted, it would give that one as its
answer. Otherwise, the program “weakened” the transformation just enoughso that one of the answers was produced, and that would be the program’sanswer.
Evans summarized his results as follows:
Allowing ourselves only [the parts of the program actually
implemented], our estimate would be that of the 30
geometric-analogy problems on a typical edition of the ACE tests,[the program] can successfully solve at least 15 and possibly as
133
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6

Semantic Representations
many as 20 problems.

He notes that this level of performance compares favorably with theaverage high school student.

6.2

Storing Information and Answering
Questions

Another of Minsky’s Ph.D. students during the early 1960s, Bertram Raphael(1936– ), focused on the problem of “machine understanding.” In his
dissertation,5 Raphael explained thata computer should be considered able to “understand” if it canconverse intelligently, i.e., if it can remember what it is told,
answer questions, and make responses which a human observer
considers reasonable.
Raphael wanted to be able to tell things to a computer and then ask itquestions whose answers could be deduced from the things it had been told.
(The telling and asking were to be accomplished by typing sentences andqueries.) Here are some examples of the kinds of things he wanted to tell it:
Every boy is a person.
A finger is part of a hand.
There are two hands on each person.
John is a boy.
Every hand has five fingers.
Given this information, Raphael would want his system to be able todeduce the answer to the question “How many fingers does John have?”
Because Raphael wanted his system to communicate with people, hewanted its input and output languages to be “reasonably close to natural
English.” He recognized that “the linguistic problem of transforming natural
language input into a usable form will have to be solved before we obtain ageneral semantic information retrieval system.” This “linguistic problem” isquite difficult and still not “solved” even though much progress has been madesince the 1960s. Raphael used various “devices” (as he called them and which
are not germane to our present discussion) to “bypass [the general problem of
dealing with natural language] while still utilizing understandable English-like
input and output.”

134
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6.2

Storing Information and Answering Questions

The main problem that Raphael attacked was how to organize facts in thecomputer’s memory so that the relevant deductions could be made. As
Raphael put it, “The most important prerequisite for the ability to‘understand’ is a suitable internal representation, or model, for stored
information. The model should be structured so that information relevant forquestion-answering is easily accessible.”6
Raphael called his system SIR, for Semantic Information Retrieval, (which
he programmed in LISP). He used the word “semantic” because SIR modeledsentences in a way dependent on their meanings. The sentences that SIR could
deal with involved “entities” (such as John, boy, hand, finger, and so on) andrelations among these entities (such as “set-membership,” “part–whole,”“ownership,” “above,” “beside,” and other spatial relationships.) The model,
then, had to have ways for representing entities and the relationships among
them.
Entities such as John and boy were represented by the LISP computer
words JOHN and BOY, respectively. (Of course, the computer had no way ofknowing that the computer word JOHN had anything to do with the person
John. Raphael could have just as well represented John in the computer by
X13F27 so long as he used that representation consistently for John. Using thecomputer word JOHN was a mnemonic convenience for the programmer – notfor the computer!) When representing the fact that John is a boy, SIR would“link” a computer expression (SUPER-SET JOHN BOY) to the expression JOHN
and link a computer expression (SUB-SET BOY JOHN) to the expression BOY.
Thus, if SIR were asked to name a boy, it could reply “JOHN” by referring to
BOY in its model, looking at its SUB-SET link and retrieving JOHN. (I have
simplified the representations somewhat to get the main ideas across; SIR’s
actual representations were a bit more complicated.)
SIR could deal with dozens of different entities and relations among them.
Every time it was told new information, it would add new entities and links asneeded. It also had several mechanisms for making logical deductions and for
doing simple arithmetic. The very structure of the model facilitated many of
its deductions because, as Minsky pointed out in his discussion of Raphael’s
thesis, “the direct predicate-links. . . almost physically chain together theimmediate logical consequences of the given information.”7
SIR was also the first AI system to use the “exception principle” inreasoning. This principle is best explained by quoting directly from Raphael’s
thesis:General information about “all the elements” of a set is considered
to apply to particular elements only in the absence of more specificinformation about those elements. Thus it is not necessarily
contradictory to learn that “mammals are land animals” and yet “awhale is a mammal which always lives in water.” In the program,
this idea is implemented by always referring for desired information
135
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6

Semantic Representations
to the property-list [that is, links] of the individual concerned before
looking at the descriptions of sets to which the individual belongs.
The justification for this departure from the no-exception
principles of Aristotelian logic is that this precedence of specificfacts over background knowledge seems to be the way people
operate, and I wish the computer to communicate with people asnaturally as possible.
The present program does not experience the uncomfortable feelingpeople frequently get when they must face facts like “a whale is a
mammal which lives in water although mammals as a rule live on
land.”The exception principle was studied by AI researchers in much moredetail later and led to what is called default reasoning and nonmonotonic
logics, as we shall see.

6.3

Semantic Networks

It is instructive to think of SIR’s representational scheme in terms of a
network. The entities (such as JOHN and BOY) are the “nodes” of the network,and the relational links (such as SUB-SET) are the connections between nodes.
SIR was an early version of what would become an important representational
idea in artificial intelligence, namely, semantic networks. It was not the first,
however. John Sowa, who has written extensively about semantic networks,
claims that the “oldest known semantic network was drawn in the 3rd century
AD by the Greek philosopher Porphyry in his commentary on Aristotle’s
categories.”8 In 1961 Margaret Masterman (1910–1986), Director of theCambridge Language Research Unit, used a semantic network in a translation
system in which concepts were ordered in a hierarchy.9
M. Ross Quillian, a student of Herb Simon’s at the Carnegie Institute ofTechnology, was interested, along with Newell and Simon, in computationalmodels of human mental processes, specifically memory organization. He
developed a memory model consisting of a semantic network of nodes
representing English words. The nodes were interconnected by what he called“associative links.” In Quillian’s words, “In the memory model, ingredientsused to build up a concept are represented by the token nodes naming otherconcepts, while the configurational meaning of the concept is represented by
the particular structure of interlinkages connecting those token nodes to eachother.”
Quillian goes on to write that “[t]he central question asked in thisresearch has been: What constitutes a reasonable view of how semantic
information is organized within a person’s memory? In other words: What136
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6.3

Semantic Networks

sort of representational format can permit the ‘meanings’ of words to be
stored, so that humanlike use of these meanings is possible?”10
I can illustrate how Quillian’s network format represents meaning by using
one of his examples. Consider the different meanings of the word “plant.” Onesuch meaning is given by linking the node PLANT to other nodes, such as LIVE,LEAF, FOOD, AIR, WATER, and EARTH, through connections that represent that aplant (according to this meaning of the word) is alive, has leaves, and gets its
food from air, water, and earth. Another meaning of “plant” links PLANT toother nodes, such as PEOPLE, PROCESS, and INDUSTRY, through connections
that represent that a plant (according to this other meaning of the word) is anapparatus that uses people for engaging in a process used in industry.
According to Quillian, the meaning of a term is represented by its place inthe network and how it is connected to other terms. This same idea is used in
dictionaries where the meaning of a word is given by mentioning therelationship of this word to other words. The meanings of those other words
are, in turn, given by their relationships to yet other words. So we can think ofa dictionary as being like a large semantic network of words linked to otherwords.
By using this view, the full meaning of a concept can be quite extensive.
As Quillian puts it,
Suppose that a person were asked to state everything he knows
about the concept “machine.” . . . This information will start off
with the more “compelling” facts about machines, such as thatthey are usually man-made, involve moving parts, and so on, andwill proceed “down” to less and less inclusive facts, such as the factthat typewriters are machines, and then eventually will get tomuch more remote information about machines, such as the factthat a typewriter has a stop which prevents its carriage from flying
off each time it is returned. We are suggesting that thisinformation can all usefully be viewed as part of the subject’s
concept of “machine.”
In what way is Quillian’s network a model of human memoryorganization? Quillian explored two capabilities of human memory modeled byhis network. One was comparing and contrasting two different words. Quillian
proposed that this be done by a process that came to be called “spreading
activation.” Conceptually, one starts at the nodes representing the two words
and gradually traverses the links emanating from them, “activating” the nodes
along the way. This process continues until the two “waves” of activationintersect, thus producing a “path” between the two original nodes. Quillian
proposed that the total “distance” along this path between the two words
could be used as a measure of their similarity. The path can be used to
produce an account comparing the two words. (Quillian’s program hadmechanisms for expressing this account in simple English.)137
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NOTES

To use one of Quillian’s examples, suppose we wanted to compare thewords “cry” and “comfort.” The spreading activations would intersect at theword “sad,” and the English account would express something like “to cry is
to make a sad sound, and to comfort is to make something less sad.”
Quillian was also interested in how the network could be used to“disambiguate” two possible uses of the same word. Consider, for example, thesentence “After the strike, the president sent him away.” The network can
encode different meanings of the word “strike.” One such might involve a labor
dispute, another might involve baseball, and yet another involve a raid bymilitary aircraft. Which of these meanings is intended by the sentence?
Presumably, activation proceeding outward from the word “president” would
eventually reach concepts having to do with labor disputes before reaching
concepts having to do with baseball or the military. Thus, the “labor dispute”
meaning would be preferred because it is “closer,” given that the word“president” is in the sentence. In contrast, a different conclusion would bereached for the sentence “After the strike, the umpire sent him away.”
Quillian’s model differs from some later semantic networks in that it does
not have a predetermined hierarchy of superclasses and subclasses. As Quillian
puts it, “every word is the patriarch of its own separate hierarchy when somesearch process starts with it. Similarly, every word lies at various places down
within the hierarchies of (i.e., is an ingredient in) a great many other word
concepts, when processing starts with them.”

Notes
1. Marvin Minsky (ed.), “Introduction,” Semantic Information Processing, p. 9,
Cambridge, MA: MIT Press, 1968. [131]2. It might be argued that the diagram used by Gelernter’s geometry program was an
earlier use of a semantic representation. [131]3. Marvin Minsky, op. cit., p. 1. [131]4. Thomas G. Evans, “A Program for the Solution of a Class of Geometric-Analogy
Intelligence-Test Questions,” in Marvin L. Minsky, op. cit., p. 271. [131]5. Bertram Raphael, “SIR: Semantic Information Retrieval,” in Marvin Minsky, op. cit.,
pp. 33–145. (This is a partial reprint of his 1964 Ph.D. dissertation.) [134]6. Marvin Minsky, op. cit., p. 35. [135]7. Marvin Minsky, op. cit., p. 17. [135]8. From an article by John F. Sowa at http://www.jfsowa.com/pubs/semnet.htm. (This isa revised and extended version of one that was originally written for the Encyclopedia ofArtificial Intelligence, edited by Stuart C. Shapiro, Wiley, 1987, second edition, 1992.) [136]9. Margaret Masterman, “Semantic Message Detection for Machine Translation, Using an
Interlingua,” in Proceedings of the 1961 International Conference on Machine Translationof Languages and Applied Language Analysis, pp. 438–475, London: Her Majesty’s
Stationery Office, 1962. [136]138
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6.3

NOTES

10. M. Ross Quillian, “Semantic Memory,” Ph.D. dissertation, Carnegie Institute ofTechnology (now Carnegie Mellon University), October 1966. (This work also appears as
Report AFCRL-66-189 and is partially reprinted in M. Minsky (ed.), Semantic Information
Processing, pp. 216–270, Cambridge, MA: MIT Press, 1968.) [137]139
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6

NOTES

140
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7.1

Chapter 7

Natural Language
Processing
Beyond pattern recognition of individual alphanumeric characters, whether
they be of fixed font or handwritten, lies the problem of understanding stringsof characters that form words, sentences, or larger assemblages of text in a“natural” language, such as English. To distinguish languages such as English
from the languages used by computers, the former are usually called “natural
languages.” In artificial intelligence, “understanding” natural language input
usually means either converting it to some kind of memory model (such as theone used by Raphael in his SIR system or the semantic network used by
Quillian) or the evocation of some action appropriate to the input.
Natural languages are spoken as well as written. And, because speechsounds are not as well segmented as are the characters printed on a page,
speech understanding presents additional difficulties, which I’ll describe in alater chapter.
The inverse of understanding natural language input is generating natural
language output – both written and spoken. Translating from one language toanother involves both understanding and generation. So does carrying on aconversation. All of these problems – understanding, generation, translation,and conversing – fall under the general heading of “natural language
processing” (sometimes abbreviated as NLP).

7.1

Linguistic Levels

Linguists and others who study language recognize several levels at which
language can be analyzed. These levels can be arranged in a sort of hierarchy,
starting with those dealing with the most basic components of language
141
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7

Natural Language Processing(sounds and word parts) and proceeding upward to levels dealing withsequences of sentences. If speech is being dealt with, there are the levels ofphonetics (language sounds) and phonology (organization of sounds intowords). For both speech and text, morphology deals with how whole words are
put together from smaller parts. For example, “walking” consists of “walk”
plus “-ing.”
Next, syntax is concerned with sentence structure and grammar. It
attempts to describe rules by which a string of words in a certain language canbe labeled either grammatical or not. For example, the string “John hit theball” is grammatical but the string “ball the hit John” is not. Together withthe dictionary definitions of words, syntax comes next in importance forunderstanding the meaning of a sentence. For example, the sentence “Johnsaw the man with a telescope” has two different meanings depending on its
syntactic structure (that is, depending on whether “with a telescope” refers to“the man” who had a telescope or to “saw.”)
But grammaticality alone is insufficient for determining meaning. For
example, the sentence “Colorless green ideas sleep furiously” might beconsidered grammatical, but it is nonsensical. The semantics level helps todetermine the meaning (or the meaninglessness) of a sentence by employing
logical analyses. For example, through semantic analysis, an “idea” can’t beboth “colorless” and “green.”
Next comes the pragmatics level, which considers the context of asentence to pin down meaning. For example, “John went to the bank” would
have a different meaning in a sentence about stream fishing than it would in a
sentence about commerce. Pragmatics deals with meanings in the context ofspecific situations.
One of these levels in particular, namely, syntax, was the subject of muchearly study and continues to be an important aspect of NLP. In 1957, theAmerican linguist Noam Chomsky published a ground-breaking book titled
Syntactic Structures in which he proposed sets of grammatical rules that could
be used for generating the “legal” sentences of a language.1 The same rules
could also be used to analyze a string of words to determine whether or notthey formed a legal sentence of the language. I’ll illustrate how this analysis isdone using what Chomsky called a phrase-structure grammar (PSG).2 The
process is very similar to how we all “diagrammed” sentences back in grade
school.
Grammars are defined by stating rules for replacing words in the string bysymbols corresponding to syntactic categories, such as noun or verb or
adjective. Grammars also have rules for replacing strings of these syntactic
symbols by additional symbols. To illustrate these ideas, I’ll use a very simple
grammar adapted from one of Chomsky’s examples. This grammar has only
three syntactic categories: determiner, noun, and verb. Those three aresufficient for analysing strings such as “the man hit the ball.”142
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7.1

Linguistic Levels

One of the rules in this illustrative grammar states that we can replaceeither of the words “the” or “a” by the symbol “DET” (for determiner).
Linguists write this rule as follows:
the | a → DET(The symbol | is used to indicate that either of the words that surroundit can be replaced by the syntactic symbol to the right of the arrow.)
Here are some other rules, written in the same format:
man | ball | john → N(The words “man,” “ball,” and “john” can be replaced by the symbol“N” for noun.)hit | took | threw → V(The words “hit,” “took,” and “threw” can be replaced by the symbol“V” for verb.)
DET N → NP
(The string of symbols “DET” and “N” can be replaced by the symbol“NP” for noun phrase.)
V NP → VP(The string of symbols “V” and “NP” can be replaced by the symbol“VP” for verb phrase.)
NP VP → S
(The string of symbols “NP” and “VP” can be replaced by the symbol“S” for sentence.)
Symbols such as “S,” “DET,” “NP,” and so on are called the “nonterminal”
symbols of the language defined by the grammar, whereas vocabulary words
such as “ball,” “john,” and “threw” are the “terminal” symbols of thelanguage.
We can apply these rules to the string “the man hit the ball” to transform
it into “S.” Any string that can be changed into “S” in this way is said to begrammatical – a legal sentence in the language defined by this very simple
grammar. One way to illustrate the rule applications, called a parse tree, isshown in Fig. 7.1.3
This example was based on a small set of syntactic categories andreplacement rules just to illustrate the main ideas about syntactic analysis. To
make the grammar slightly more realistic, we would need to include symbols
and replacement rules for adjectives, adverbs, prepositions, and so on. And, ofcourse, we would have to include many more vocabulary words.
Grammars are called context-free grammars (CFGs) if all of their rules
have just a single nonterminal symbol on the right side of the arrow. They are
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Natural Language Processing

Figure 7.1: A parse tree for analyzing a sentence.
called that because when the rules are used in reverse (to generate rather thanto analyze grammatical sentences), the way in which a nonterminal symbol isreplaced does not depend on the presence of any other symbols. PSGs arecontext free.
The diagram in Fig. 7.2 shows how the rules of our simple grammar canbe used to generate sentences. In this case, it starts with the symbol forsentence, namely, “S,” and generates the sentence “John threw the ball.”This simple grammar certainly can’t generate all of the sentences wewould claim to be legal or acceptable. It also generates sentences that wewould not ordinarily want to accept, such as “the john threw the ball.”
Chomsky’s book presents much more complex grammars, and later work has

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7.1

Linguistic Levels

Figure 7.2: A parse tree for generating a sentence.
produced quite elaborate ones. By the early 1960s, several grammars had been
encoded in computer programs that could parse samples of English text.4 I’ll
be mentioning several different grammars, some more complex than CFGs in
succeeding chapters. Nevertheless, even the most complex grammars can’t
cleanly distinguish between sentences we would accept as grammaticallycorrect and those we would not. I will return to this difficulty and one way todeal with it in a later chapter.
The way a sentence is parsed by a grammar can determine its meaning, so

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7

Natural Language Processing

an important part of natural language processing involves using the grammar
rules to find acceptable parse trees for sentences. Finding a parse tree involves
search – either for the several different ways that the nonterminal symbols,
beginning with “S,” can be replaced using grammar rules in an attempt tomatch a target sentence or for the several different ways the words in a target
sentence can be replaced by nonterminal symbols in an attempt to produce thesymbol “S.” The first of these kinds of searches is called “top-down” (from “S”
to a sentence); the second is called “bottom-up” (from a sentence to “S”).
It is often (if not usually) the case that, given a grammar, sentences canhave more than one parse tree, each with a different meaning. For example,“the man hit the ball in the park” could have a parse tree in which “in thepark” is part of a verb phrase along with “hit” or a parse tree in which “in thepark” is part of a noun phrase along with “ball.” Moreover, as I have alreadymentioned, some parsings of sentences might be meaningless. For example,
according to my simple grammar, “the ball threw the man” is a legal but
probably meaningless sentence. Deciding which parse tree is appropriate ispart of the process of deciding on meaning and is a job for the semantics (andpossibly even the pragmatics) level. During the late 1950s and throughout
most of the 1960s and beyond, syntactic analysis was more highly developed
than was semantics.
Semantic analysis usually involves using the parse tree to guide thetransformation of the input sentence into an expression in some well-defined“meaning representation language” or into a program that responds in theappropriate way to the input sentence. For example, “the man threw the ball”
might be transformed into a logical expression such as(∃x, y, z)[Past(z) ∧ Man(x, z) ∧ Ball(y, z) ∧ Event(z) ∧ Throws(x, y, z)],which can be interpreted as “there are x, y, and z, such that z is an event that
occurred in the past, x is a man in that event, y is a ball in that event, and x
throws y in that event.”
Semantic analysis might also transform the sentence “the man threw theball” into a program that, in some way, simulates a man throwing the ball in
the past.

7.2

Machine Translation

Some of the first attempts to use computers for more than the usual numericalcalculations were in automatic translation of sentences in one language into
sentences of another. Word dictionaries could be stored in computer memory(either on tapes or on punched cards), and these could be used to find English
equivalents for foreign words. It was thought that selecting an appropriate
equivalent for each foreign word in a sentence, together with a modest amount
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7.2

Machine Translationof syntactic analysis, could be used to translate a sentence in a foreign
language (Russian, for example) into English.
Reporting about a new computer5 being developed by a team led byHarry D. Huskey at the National Bureau of Standards (now called the
National Institute of Standards and Technology), the New York Times
reported the following on May 31, 1949:6
A new type of “electric brain” calculating machine capable not
only of performing complex mathematical problems but even oftranslating foreign languages, is under construction here at theUnited States Bureau of Standards Laboratory at the University ofCalifornia’s Institute of Numerical Analysis. While the exact scope
the machine will have in the translating field has not been decided,
the scientists working on it say it would be quite possible to makeit encompass the 60,000 words of the Webster Collegiate
Dictionary with equivalents for each word in as many as three
foreign languages.
Explaining how the machine might do translation, the Times reporter
wrote
When a foreign word for translation is fed into the machine, in theform of an electro-mathematical symbol on a tape or card, themachine will run through its “memory” and if it finds that symbol
as record, will automatically emit a predetermined equivalent – theEnglish word.
...
This admittedly will amount to a crude word-for-word translation,
lacking syntax, but will nevertheless be extremely valuable, thedesigners say, for such purposes as scientists’ translations of foreigntechnical papers in which vocabulary is far more of a problem thansyntax.
The machine had not actually performed any translations – the idea of
doing so was still just a possibility envisioned by Huskey. But evennonscientists could imagine the difficulties. An editorial in the New YorkTimes the next day put the problem well:
We have our misgivings about the accuracy of every translation.
How is the machine to decide if the French word “pont” is to betranslated as “bridge” or “deck” or to know that “operation” inGerman means a surgical operation? All the machine can do is tosimplify the task of looking up words in a dictionary and setting
down their English equivalents on a tape, so that the translator
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7

Natural Language Processing
still has to frame the proper sentences and give the words theircontextual meaning.

In a 1947 letter to Norbert Wiener, Warren Weaver, a mathematician andscience administrator, mentioned the possibility of using digital computers totranslate documents between natural human languages. Wiener was doubtful
about this possibility. In his reply to Weaver, Wiener wrote “I frankly amafraid the boundaries of words in different languages are too vague and theemotional and international connotations are too extensive to make any
quasi-mechanical translation scheme very hopeful.” Nevertheless, by July
1949, Weaver had elaborated his ideas into a memorandum, titled“Translation” that he sent to several colleagues.
Weaver began his memorandum by stating the following:
There is no need to do more than mention the obvious fact that a
multiplicity of languages impedes cultural interchange between thepeoples of the earth, and is a serious deterrent to international
understanding. The present memorandum, assuming the validity
and importance of this fact, contains some comments andsuggestions bearing on the possibility of contributing at least
something to the solution of the world-wide translation problem
through the use of electronic computers of great capacity,
flexibility, and speed.
According to the editors of the published volume7 in which thememorandum was reprinted, “When he sent it to some 200 of his
acquaintances in various fields, it was literally the first suggestion that mosthad ever seen that language translation by computer techniques might bepossible.” Weaver’s document is often credited with initiating the field ofmachine translation (often abbreviated as MT).8
In June 1952 at MIT, Yehoshua Bar-Hillel (1915–1975), an Israeli logician
who was then at MIT’s Research Laboratory for Electronics, organized thefirst conference devoted to machine translation.9 Originally optimistic about
the possibilities, Bar-Hillel was later to conclude that full automatictranslation was impossible.
In January 1954, automatic translation of samples of Russian text toEnglish was demonstrated at IBM World Headquarters, 57th Street andMadison Avenue, New York City. The demonstration, using a small
vocabulary and limited grammar, was the result of a collaboration between
IBM and Georgetown University. The project was headed by Cuthbert Hurd,
director of the Applied Sciences Division at IBM, and Léon Dostert ofGeorgetown. According to an IBM press release10 on January 8, 1954,
Russian was translated into English by an electronic “brain” today
for the first time.
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7.2

Machine Translation
Brief statements about politics, law, mathematics, chemistry,metallurgy, communications and military affairs were submitted in
Russian by linguists of the Georgetown University Institute of
Languages and Linguistics to the famous 701 computer of theInternational Business Machines Corporation. And the giant
computer, within a few seconds, turned the sentences into easilyreadable English.
A girl who didn’t understand a word of the language of the Soviets
punched out the Russian messages on IBM cards. The “brain”
dashed off its English translations on an automatic printer at the
breakneck speed of two and a half lines per second.
“Mi pyeryedayem mislyi posryedstvom ryechyi,” the girl punched.
And the 701 responded: “We transmit thoughts by means ofspeech.”“Vyelyichyina ugla opryedyelyayetsya otnoshyenyiyem dlyini dugi kradyiusu,” the punch rattled. The “brain” came back: “Magnitudeof angle is determined by the relation of length of arc to radius.”

Although the demonstration caused a great deal of excitement and led toincreased funding for translation research, subsequent work in the field wasdisappointing.11 Evaluating MT work in a 1959 report circulated among
researchers, Bar-Hillel had become convinced that fully automatic,
high-quality translation (which he dubbed FAHQT) was not feasible “not onlyin the near future but altogether.” His expanded report appeared in a 1960
paper that enjoyed wide distribution.12
One of the factors leading Bar-Hillel to his negative conclusions was theapparent difficulty of giving computers the “world knowledge” they would
need for high-quality translation. He illustrated the problem with thefollowing story:
Little John was looking for his toy box. Finally he found it. The
box was in the pen. John was very happy.
How should one translate “The box was in the pen”? Bar-Hillel argued
that even if there were only two definitions of “pen” (a writing utensil and an
enclosure where small children play), a computer knowing only thosedefinitions would have no way of deciding which meaning was intended. In
addition to its knowledge of vocabulary and syntax, a translating computer
would need to know “the relative sizes of pens, in the sense of writing
implements, toy boxes, and pens, in the sense of playpens.” Such knowledge,
Bar-Hillel claimed, was not at the disposal of the electronic computer. He said
that giving a computer such encyclopedic knowledge was “utterly chimericaland hardly deserves any further discussion.”

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7

Natural Language Processing

As later researchers would finally concede, Bar-Hillel was right about his
claim that highly competent natural language processing systems (indeed,broadly competent AI systems in general) would need to have encyclopedic
knowledge. However, most AI researchers would disagree with him about the
futility of attempting to give computers the required encyclopedic knowledge.
Bar-Hillel was well known for being a bit of a nay-sayer regarding artificial
intelligence. (Commenting on John McCarthy’s “Programs with Common
Sense” paper at the 1958 Teddington Conference, Bar-Hillel said “Dr.
McCarthy’s paper belongs in the Journal of Half-Baked Ideas, the creation ofwhich was recently proposed by Dr. I. J. Good.”)13
In April 1964, the National Academy of Sciences formed the Automatic
Language Processing Advisory Committee (ALPAC), with John R. Pierce(1910–2002) of Bell Laboratories as chair, to “advise the Department of
Defense, the Central Intelligence Agency, and the National Science Foundation
on research and development in the general field of mechanical translation offoreign languages.” The committee issued its report in August 1965 andconcluded, among other things, that “. . . there is no immediate or predictableprospect of useful machine translation.”14 They recommended support forbasic linguistics science and for “aids” to translation, but not for further
support of fully automatic translation. This report caused a dramatic
reduction of large-scale funding of research on machine translation.
Nonetheless, machine translation survived and eventually thrived, as we shallsee in later chapters.
The Association for Machine Translation and Computational Linguistics(AMTCL) held its first meeting in 1962. In 1968, it changed its name to the
Association for Computational Linguistics (ACL) and has become aninternational scientific and professional society for people working on problems
involving natural language and computation. It publishes the quarterly
journal Computational Linguistics and sponsors conferences and workshops.15

7.3

Question Answering

In addition to work on machine translation, researchers began exploring how
sentences in a natural language, such as English, could be used tocommunicate with computers. You will recall Weizenbaum’s ELIZA programthat was able to engage a person in a conversation even though the program“understood” nothing about what was being said. And, I have alreadymentioned Raphael’s SIR system that could represent information given to it
and then answer questions.
I’ll mention a few other projects to give a flavor of natural language
processing work during this period. A program called BASEBALL (written in
IPL-V, a special list-processing programming language developed by Newell,Shaw, and Simon to be described later) was developed at the Lincoln
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7.3

Question Answering

Laboratory under the direction of Bert Green, a professor of Psychology at theCarnegie Institute of Technology.16 It could answer simple English questions
about baseball using a database about baseball games played in the American
League during a single year. For example, it could answer a question such as“Where did the Red Sox play on July 7?” The questions had to be of aparticularly simple form and restricted to words in the program’s vocabulary.
In the authors’ words,17
Questions are limited to a single clause; by prohibiting structureswith dependent clauses the syntactic analysis is considerably
simplified. Logical connectives, such as and, or, and not, areprohibited, as are constructions implying relations like most andhighest. Finally, questions involving sequential facts, such as “Didthe Red Sox ever win six games in a row?” are prohibited.
The program worked by converting a question into a special form called a“specification list” using both special-purpose syntactic and semantic analyses.
This list would then be used to access the program’s database to find an
answer to the question. For example, the question “Where did the Red Sox
play on July 7?” would first be converted to the list:
Place = ?
Team = Red Sox
Month = July
Day = 7
The authors claimed that their “restrictions were temporary expedientsthat will be removed in later versions of the program.” As far as I know, there
were no later versions of the program. (As we will see as my history of AI
unfolds, there are several instances in which it proved very difficult to remove“temporary” restrictions.)
Another natural language program, SAD SAM, was written in IPL-V in
1962–1963 by Robert Lindsay at the Carnegie Institute of Technology.18 It
could analyze English sentences about family relationships and encode these
relationships in a family tree. Using the tree, it could then answer English
questions about relationships.
For example, if SAD SAM received the sentence “Joe and Jane are Tom’s
offspring,” it would construct a treelike list structure for a certain “familyunit” in which Tom is the father and Joe and Jane are the children. Then, if it
received the sentence “Mary is Jane’s mother,” it would add Mary to thisstructure as Tom’s wife. It would then be able to answer the question “Who is
Joe’s mother?”
SAD SAM is an acronym for Sentence Appraiser and Diagrammer andSemantic Analyzing Machine. The SAD part parsed the input sentences and151
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7

Natural Language Processing

passed them to SAM, which extracted the semantic information needed forbuilding family trees and for finding answers to questions. The program could
accept a wide variety of sentences in Basic English – a system of grammar anda vocabulary of about 850 words defined by Charles K. Ogden.19
Robert F. Simmons (1925–1994), a psychologist and linguist at the
Systems Development Corporation (SDC) in Santa Monica, California, had
grander goals for his own work in natural language processing. According toan “In Memoriam” page written by Gordon Novak, one of his Ph.D. students
at the University of Texas in Austin where Simmons took up a position as
Professor of Computer Sciences and Psychology,20Simmons’ dream was that one could have “a conversation with abook;” the computer would read the book, and then the user could
have a conversation with the computer, asking questions to beanswered from the computer’s understanding of the book.
Accomplishing this “dream” would turn out to be as hard as AI itself. In
a 1961 note about his proposed “Synthex” project, Simmons described how hewould begin:21
The objective of this project is to develop a research methodology
and a vehicle for the design and construction of a general purpose
computerized system for synthesizing complex human cognitive
functions. The initial vehicle, proto-synthex, will be an elementary
language-processing device which reads simple printed material
and answers simple questions phrased in elementary English.
By 1965, Simmons and Lauren Doyle had conducted some experimentswith their Protosynthex system. According to a report by Trudi Bellardo
Hahn,22 “A small prototype full-text database of chapters from a child’s
encyclopedia (Golden Book) was loaded on the system. Protosynthex could
respond to simple questions in English with an ‘answer.’ . . . it was apioneering effort in the use of natural language for text retrieval.”
In the meantime, Daniel G. Bobrow (1935– ), a Ph.D. student of Marvin
Minsky’s at MIT, wrote a set of programs, called the STUDENT system, that
could solve algebra “story problems” given to it in a restricted subset ofEnglish. Here is an example of a problem STUDENT could solve:
The distance from New York to Los Angeles is 3000 miles. If theaverage speed of a jet plane is 600 miles per hour, find the time ittakes to travel from New York to Los Angeles by jet.
STUDENT solved the problem by using some known relationships about speed

and distance to set up and solve the appropriate equations. Bobrow’s
dissertation gave several other examples of problems STUDENT could solve andthe methods used.23
152
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7.3

NOTES

Notes
1. Noam Chomsky, Syntactic Structures, ’s-Gravenhage: Mouton & Co., 1957. [142]2. The basic structure of PSGs was independently invented by computer scientist John
Backus to describe the syntax of the ALGOL programming language. See John Backus,“The Syntax and Semantics of the Proposed International Algebraic Language of the Zürich
ACM-GAMM Conference,” Proceedings on the International Conference on Information
Processing, pp. 125–132, UNESCO, 1959. [142]3. According to C. George Boeree (see http://www.ship.edu/∼cgboeree/wundtjames.html),
Wilhelm Wundt “invented the tree diagram of syntax we are all familiar with in linguistics
texts.” [143]4. For a survey of work during this period, see Daniel Bobrow, “Syntactic Analysis ofEnglish by Computer: A Survey,” Proceedings of the 1963 Fall Joint Computer Conference,Vol. 24, pp. 365–387, Baltimore: Spartan Books, 1963. [145]5. The Standards Western Automatic Computer (later abbreviated to SWAC) [147]
6. The quotation appears in John Hutchins, “From First Conception to First
Demonstration: The Nascent Years of Machine Translation, 1947–1954. A chronology,”
Machine Translation, Vol. 12 No. 3, pp. 195–252, 1997. (A corrected 2005 version, withminor additions, appears at http://www.hutchinsweb.me.uk/MTJ-1997-corr.pdf.) [147]7. W. N. Locke and A. D. Booth (eds.), Machine Translation of Languages: Fourteen
Essays, pp. 15–23, Cambridge, MA: MIT Press, 1955. [148]8. For a history of MT, see W. John Hutchins, “Machine Translation: A Brief History,” inE. F. K. Koerner and R. E. Asher (eds.), Concise History of the Language Sciences: From
the Sumerians to the Cognitivists, pp. 431–445, Oxford: Pergamon Press, 1995. (Alsoavailable online at http://www.hutchinsweb.me.uk/ConcHistoryLangSci-1995.pdf.)
Hutchins also has a Web page devoted to his publications at
http://ourworld.compuserve.com/homepages/WJHutchins/#History1. [148]9. For reports about this conference see E. Reifler, “The First Conference on MechanicalTranslation,” Mechanical Translation, Vol. 1 No. 2, pp. 23–32, 1954, and A. C. Reynolds,“The Conference on Mechanical Translation Held at MIT, June 17–20, 1952,” MechanicalTranslation, Vol. 1, No. 3, pp. 47–55, 1954. [148]10. http://www-03.ibm.com/ibm/history/exhibits/701/701 translator.html. [148]11. For a summary of the IBM–Georgetown work, see W. John Hutchins, “TheGeorgetown–IBM Experiment Demonstrated in January 1954,” in Robert E. Frederking andKathryn B.Taylor (eds.), Proceedings of Machine Translation: From Real Users to Research,
6th Conference of the Association for Machine Translation in the Americas, AMTA-2004,
pp. 102–114, Washington DC, USA, September 28–October 2, 2004, Berlin: Springer, 2004.
An online version is available at http://www.hutchinsweb.me.uk/ATMA-2004.pdf. [149]
12. Yehoshua Bar-Hillel, “The Present Status of Automatic Translation of Languages,”
Advances in Computers, Vol. 1, No. 1, pp. 91–163, 1960. [149]
13. In D. V. Blake and A. M. Uttley (eds.), Proceedings of the Symposium on Mechanisationof Thought Processes, p. 85, London: Her Majesty’s Stationary Office, 1959. [150]14. John R. Pierce et al., Language and Machines: Computers in Translation and
Linguistics, ALPAC Report, National Academy of Sciences Publication 416, National
Research Council, Washington, DC, 1966. [150]
15. See http://www.aclweb.org/. [150]16. Bert F. Green Jr., Alice K. Wolf, Carol Chomsky, and Kenneth Laughery, “BASEBALL:
An Automatic Question Answerer,” pp. 219–224, Proceedings of the Western Joint

153
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7

NOTES

Computer Conference, May 1961. Reprinted in Edward A. Feigenbaum and Julian Feldman(eds.), Computers and Thought, pp. 207–216, New York: McGraw Hill, 1963, and in B.
Grosz, K. Spark Jones, and B. Lynn Webber (eds.), Readings in Natural Language
Processing, Morgan Kaufman, Los Altos, CA, 1986. [151]17. Ibid. [151]18. See Robert K. Lindsay, “Inferential Memory as the Basis of Machines Which
Understand Natural Language,” in Edward A. Feigenbaum, and Julian Feldman, op. cit.,
pp. 217–233. [151]19. Charles K. Ogden, Basic English: A General Introduction with Rules and Grammar,
4th edition, London: Kegan, Paul, Trench, Trubner & Co., Ltd., 1933. (Lindsay says 1,700
words; other sources say 850.) [152]20. From http://www.cs.utexas.edu/users/ai-lab/simmons.html. [152]
21. Robert F. Simmons, “Synthex,” Communications of the ACM, Vol. 4 , No. 3, p. 140,
March 1961. [152]22. From “Text Retrieval Online: Historical Perspective on Web Search Engines,” by Trudi
Bellardo Hahn, ASIS Bulletin, April/May 1998. Available online at
http://www.asis.org/Bulletin/Apr-98/hahn.html. [152]23. Daniel G. Bobrow, “Natural Language Input for a Computer Problem Solving System,”
MIT Artificial Intelligence Project Memo 66, Memorandum MAC-M-148, March 30, 1964.
Available online at
http://dspace.mit.edu/bitstream/handle/1721.1/5922/AIM-066.pdf?sequence=2. An article
based on the dissertation is Chapter 3 of Marvin Minsky (ed.), Semantic Information
Processing, Cambridge, MA: MIT Press, 1968. [152]154
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8.1

Chapter 8

1960s’ Infrastructure
The technical developments during the 1960s were aided (indeed, one might
say made possible) by several systems support and societal factors. New
computer languages made it much easier to build AI systems. Researchers
from mathematics, from cognitive science, from linguistics, and from what soon
would be called “computer science” came together in meetings and in newly
formed laboratories to attack the problem of mechanizing intelligent behavior.
In addition, government agencies and companies, concluding that they had an
important stake in this new enterprise, provided needed research support.

8.1

Programming Languages

Newell and Simon were among the first to realize that a specialized computer
language would be useful for manipulating the symbolic expressions that wereat the heart of their approach to mechanizing intelligence. The mostelementary kind of symbolic expression is a list of symbols, such as (7, B, 5).
More complex structures can be composed by creating lists of lists of symbols
and lists of lists of lists, and so on.
In my description of symbol structures for the eight-puzzle, I mentioned
the kinds of manipulations that are needed. Recall that the starting positionof the eight-puzzle was represented by the expression((2, 8, 3), (1, 6, 4), (7, B, 5)).
What was needed was a language for writing programs that could produce
expressions representing the positions corresponding to moves of the puzzle.
For example, one of the moves that can be made from the starting position isrepresented by the expression((2, 8, 3), (1, 6, 4), (B, 7, 5)).
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81960s’ Infrastructure

To produce this expression, the program must copy the starting position
expression and then interchange the first and second elements of the third listin that expression.
Newell, Shaw, and Simon set about to develop a language in which thesekinds of manipulations could be programmed. Starting around 1954 at the
RAND Corporation, they created a series of languages all called IPL (forinformation-processing language). Several versions of the language weredeveloped. IPL-I was not actually implemented but served as a design
specification. IPL-II was implemented in 1955 for the RAND Corporation’s
JOHNNIAC computer. Later versions (through IPL-VI) were implemented atCarnegie Tech.
The IPL languages were used to program several early AI programs,
including LT, GPS, NSS (the Newell, Shaw, Simon chess-playing program), andthe programs written by Newell’s and Simon’s students, such as Quillian andGeorge Ernst. After the Dartmouth summer project, John McCarthy alsobegan thinking about using list-processing languages. He was aware of the useof FLPL (FORTRAN fortified by some list-processing operations) in Gelernter’sgeometry theorem-proving machine. Ultimately, however, McCarthy concluded
a new language was needed that was easier to use than IPL and more powerfulthan FLPL.
Starting in the fall of 1958 at MIT, McCarthy began the implementationof a programming language he called LISP (for list processing). He based it(loosely) on a branch of mathematics of special interest in computation calledrecursive function theory. LISP had several elementary operations for copying a
list, stripping off elements of a list, adding an element to a list, and checking
to see whether something were an element of a list. From these, arbitrarily
complex manipulations of lists could be composed. An important feature of
LISP was that programs for manipulating lists were themselves represented aslists. Such programs could thus be elements of other lists and could havesubprograms embedded in them. A program could even have a version of itself
embedded in it. As I have already mentioned, programs that can activate
versions of themselves as part of their operation are called “recursive” and arevery useful (if used with the care needed to avoid endless circularity).1
Because it was easier to use, LISP soon replaced IPL as the primarylanguage of artificial intelligence research and applications. The programs
produced by Minsky’s students, Evans, Raphael, Bobrow, Slagle, and others,
were all written in LISP. (Interestingly, Arthur Samuel did not use alist-processing language for writing his checkers-playing programs. Rather
heroically, he programmed them in the base language of elementary machine
operations to make them run efficiently and use memory sparingly.)
Besides developing LISP, McCarthy proposed a method, called“time-sharing,” by which a single computer could be made to serve several
users simultaneously – acting as if each user had his or her own private

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8.2

Early AI Laboratories

machine.2 Working initially with Ed Fredkin at Bolt, Beranek, and Newman(BBN) and later with others, McCarthy developed an early time-sharing
system at MIT using a DEC PDP-1 computer.3

8.2

Early AI Laboratories

In 1955, Newell moved from the RAND Corporation to Carnegie Tech (whichbecame Carnegie Mellon University, CMU, in 1967) to work on a Ph.D. degree
in industrial management under Herb Simon. After completing his degree,
Newell stayed on as a professor at Carnegie, and he and Simon began advising
a number of Ph.D. students – using the phrase “complex information
processing (CIP)” to describe their work. (For several years they avoided the
AI sobriquet.) In the fall of 1956, Herb Simon took delivery of an IBM 650,
which was the first computer used for CIP work. Later, they used an IBM 704,
followed by a series of DEC machines.
John McCarthy moved from Dartmouth to MIT in the fall of 1958.
Minsky joined MIT a year later. As Minsky puts it,4
[McCarthy and I] were walking down the hall and we met Jerry
Wiesner or Zimmerman or someone and he said how’s it going andwe said well, we’re working on these artificial intelligence ideas but
we need a little more room and support for some graduate
students. So then a room appeared a few days later. . .
The “room” soon developed into the MIT Artificial Intelligence Project.
Initially, the group used MIT’s IBM 704 computer, which proved not to havesufficient memory for the programs being written. So it began to use a DEC
PDP-1 belonging to BBN. With funding from another project at MIT, itbought its own PDP-1, which was followed by the PDP-6 and PDP-10.
Several of the group’s Ph.D. students did their work at BBN and at the
nearby Lincoln Laboratory where Oliver Selfridge continued his AI research –mainly on pattern recognition and machine learning. In 1962, McCarthy
moved to Stanford where he began an AI project. Seymour Papert (1928– ), amathematician who had worked with Jean Piaget, joined Minsky as
co-director of the AI Lab in 1963.
By 1965 at Stanford, McCarthy and colleagues had created a time-sharing
system, called Thor, on a PDP-1 computer. It included twelve Philco display
terminals, which made it the first display-oriented time-sharing system
anywhere in the world.
With the help of Lester Earnest (1930– ), who had moved to Stanfordfrom Lincoln Laboratory, McCarthy set up the Stanford AI Laboratory (SAIL)
in 1965. Outgrowing its on-campus facilities, SAIL moved to a building in the
Stanford foothills during the summer of 1966. (See Fig. 8.1.) With additional
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81960s’ Infrastructure

support from ARPA, the Lab took delivery of a DEC PDP-6 computer and,later, a PDP-10 computer. In addition to its work in AI (which I’ll describe insubsequent chapters), SAIL was involved in many other computer-related
projects including the development of a precursor to computer “windows” andthe early installation of terminals in everyone’s offices.5

Figure 8.1: Site of the Stanford AI Lab from 1966 until 1980. (Photographcourtesy of Lester Earnest.)
Since their early days, the groups at CMU, MIT, and Stanford have beenamong the leaders of research in AI. Often graduates of one of these
institutions became faculty members of one of the other ones.
Around 1965 another world-class AI center emerged at the University of
Edinburgh in Scotland. Its founder was Donald Michie (1923–2007; Fig. 8.2),
who had worked with Alan Turing and I. J. (Jack) Good at Bletchley Park
during the Second World War. Discussions there with Turing and Good about
intelligent machines captivated Michie. As he reported in an October 2002
interview, “I resolved to make machine intelligence my life as soon as such anenterprise became feasible.”6 Because computer facilities in the mid- to late
1940s were primitive and scarce, Michie became a geneticist and molecularbiologist.
Pursuing his interest in machine intelligence, from the sidelines as it were,in 1960 he put together a “contraption of matchboxes and glass beads” that
could learn to play tic-tac-toe (noughts and crosses). He named his “machine”
MENACE, an acronym for Matchbox Educable Noughts and Crosses Engine.7
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8.2

Early AI Laboratories

Figure 8.2: Donald Michie. (Photograph courtesy of the Michie Family.)(See Fig. 8.3.) (As I’ll explain later, MENACE foreshadowed work in what isnow called “reinforcement learning.”) During a year-long visit to Stanford(sponsored by the Office of Naval Research) in the early 1960s, Michie met
John McCarthy, Bernard Widrow, and others working in AI (including me).
While there, he worked on a learning program for balancing a pole on a
motor-driven cart.

Figure 8.3: Michie’s MENACE for learning how to play tic-tac-toe.

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81960s’ Infrastructure

In January 1965, Michie became the Director of the UK’s first AI
laboratory, the Experimental Programming Unit, at the University of
Edinburgh. This group was to become the Department of Machine Intelligenceand Perception in October 1966. Michie recruited some top-flight computertalent, including Rod Burstall, Robin Popplestone, and John Collins. Those
three developed a list-processing language called POP-2, which was thelanguage used for AI program-writing by members of the Unit. (I’ll describe
some of these programs later.) For many years, Michie’s group worked
collaboratively with a nearby University of Edinburgh group, theMetamathematics Unit under Bernard Meltzer (circa 1916–2008). The
Metamathematics Unit is famous for the work of Robert Boyer and J Strother
Moore in mechanized theorem proving and of Robert Kowalski in developing
some of the principles of logic programming.8
At IBM in Poughkeepsie, Nathan Rochester and Herb Gelernter continued
AI research for a short time after the Dartmouth workshop. This research
resulted in the geometry-theorem-proving machine. However, soon after,
according to a book about government support for computing research, “inspite of the early activity of Rochester and other IBM researchers, thecorporation’s interest in AI cooled. Although work continued oncomputer-based checkers and chess, an internal report prepared about 1960
took a strong position against broad support for AI.”9 Perhaps IBM wanted toemphasize how computers helped people perform tasks rather than how theymight replace people. McCarthy’s view about all of this is that “IBM thoughtthat artificial intelligence [that machines were as smart as people] was bad forIBM’s image. . . This may have been associated with one of their other image
slogans, which was ‘data processing, not computing.’”10

8.3

Research Support

As the computing systems needed for AI research became larger and more
expensive, and as AI laboratories formed, it became necessary to secure morefinancial support than was needed in the days when individual investigators
began work in the field. Two of the major sources of funding during the late
1950s and early 1960s were the Office of Naval Research (ONR) and theAdvanced Research Projects Agency (ARPA), each a part of the U.S. defense
establishment.
ONR was formed shortly after the end of the Second World War. Its
mission was “to plan, foster, and encourage scientific research in recognition of
its paramount importance as related to the maintenance of future naval power
and the preservation of national security.” Its Information Systems Branch was
set up in the mid-1950s under the direction of Marshall Yovits. The branch
supported AI work at several institutions and also sponsored conferences andworkshops on self-organizing systems, cybernetics, optical character

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8.3

Research Support

recognition, and artificial intelligence. All of this was done in anticipation thatthese technologies would be generally useful to the U.S. Navy. (A later
director, Marvin Denicoff, supported some of my research and my AI textbook
writing.)
The formation of ARPA was, in part, a response to the successful launchof the Soviet satellite Sputnik in 1957. ARPA’s mission was to provide
significant amounts of research funds to attack problem areas important toU.S. defense. One of its most important projects in the late 1950s was thedevelopment of ablative nose cones to absorb and dissipate heat duringballistic missile reentry. Its Information Processing Techniques Office (IPTO)
was set up in 1962 under the direction of J. C. R. (Lick) Licklider (1915–1990;
Fig. 8.4).
“Lick” (as he was called by all who knew him) was a psychoacoustician
who worked first at Lincoln Laboratory and MIT and later at BBN. Lick’s
1960 paper, “Man-Computer Symbiosis,” proposed that men and computers
should “cooperate in making decisions and controlling complex situations
without inflexible dependence on predetermined programs.”11
Lick was persuaded that computers would play a very important role indefense – especially in those applications in which people and computersworked together. At ARPA, he provided funds to MIT for the formation of
Project MAC (an acronym for Machine-Aided Cognition and perhaps forMulti-Access Computing or Man And Computers). [Project MAC, initiallyfounded in July 1963, was later to become the Laboratory for Computer
Science (LCS), and still later to evolve into the Computer Science andArtificial Intelligence Laboratory (CSAIL).] Project MAC took Minsky and
McCarthy’s Artificial Intelligence Project under its wing and also supported
the development of MIT’s Compatible Time-Sharing System (CTSS) under
Fernando Corbató. (CTSS work was separate from McCarthy’s time-sharing
project.)
ARPA funds helped to establish “centers of excellence” in computerscience. Besides MIT, these centers included Stanford, Carnegie Mellon, and
SRI. ARPA also supported computer science work at the RAND Corporation,
the Systems Development Corporation, and BBN, among others. AI was justone of ARPA’s interests. IPTO also supported research that led to graphicaluser interfaces (and the mouse), supercomputing, computer hardware andvery-large-scale integrated circuits (VLSI), and, perhaps most famously,
research that led to the Internet. According to Licklider, “ARPA budgets did
not even include AI as a separate line item until 1968.”12
But as far as AI was concerned, Lick believed that Newell and Simon,
Minsky, and McCarthy ought to be provided with research funds adequate tosupport big AI projects. With regard to the situation at Stanford (andprobably to that at MIT and CMU also), Paul Edwards explained that13[F]unding from ARPA was virtually automatic; Licklider simply
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81960s’ Infrastructure

Figure 8.4: J. C. R. Licklider. (Photograph by Koby-Antupit from MIT Collection (JCL8).)
asked McCarthy what he wanted and then gave it to him, aprocedure unthinkable for most other government agencies.
Licklider remembered that ‘it seemed obvious to me that he shouldhave a laboratory supported by ARPA. . . . So I wrote him acontract at that time.”
McCarthy remembers all of this somewhat differently. Soon after arriving
at Stanford in 1962, he sent a proposal to Licklider “to do AI.” McCarthy
claims that Licklider demurred at first – citing their close relationship when
McCarthy was at MIT and Licklider at BBN – but then gave him “a small
contract.”14 But perhaps it was not so “small” compared with how research
was usually supported (say by the National Science Foundation) at the time.
Les Earnest claims that McCarthy “obtained financial support for a small
activity (6 persons) from the Advanced Research Projects Agency (ARPA)
beginning June 15, 1963.”15
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8.4

All Dressed Up and Places to Go

Later, ARPA was renamed DARPA (for Defense Advanced Research
Projects Agency) to emphasize its role in defense-related research. DARPA
projects and grants were typically much larger than those of ONR and allowed
the purchase of computers and other equipment as well as support for
personnel. It’s hardly an exaggeration to say that a good part of today’s
computer-based infrastructure is the result of DARPA research support.

8.4

All Dressed Up and Places to Go

By the mid-1960s AI was well prepared for further advances. Flushed withearly successes it was poised to make rapid progress during the rest of the
1960s and 1970s. Indeed, many people made enthusiastic predictions. For
example, in a 1957 talk16 Herb Simon predicted that within ten years “adigital computer will be the world’s chess champion unless the rules bar itfrom competition.” He made three other predictions too. Within ten years
computers would compose music, prove a mathematical theorem, and embody
a psychological theory as a program. He said “it is not my aim to surprise or
shock you. . . but the simplest way I can summarize is to say that there are now
in the world machines that think, that learn and that create. Moreover, theirability to do these things is going to increase rapidly until – in a visible future– the range of problems they can handle will be coextensive with the range towhich the human mind has been applied.”17 Later Simon said that his
predictions were part of an attempt “to give some feeling for what computers
would mean” to society.
One could argue that Simon’s predictions about computers composing
music and proving a mathematical theorem were realized soon after he madethem, but a computer chess champion was not to emerge until forty years
later. And, we are still far, I think, from achieving things “coextensive” withwhat the human mind can achieve.
Simon was not alone in being optimistic. According to Hubert Dreyfus,“Marvin Minsky, head of MITs Artificial Intelligence Laboratory, declared in a
1968 press release for Stanley Kubrick’s movie, 2001: A Space Odyssey, that‘in 30 years we should have machines whose intelligence is comparable toman’s.’ ”18 The difficulty in assessing these sorts of predictions is that“human-level intelligence” is multifaceted. By the year 2000, AI programs didoutperform humans in many intellectual feats while still having a long way to
go in most others.
Even so, what had already been accomplished was an impressive start.
More important perhaps than the specific demonstrations of intelligent
behavior by machines was the technical base developed during the 1950s andearly 1960s. AI researchers now had the means to represent knowledge byencoding it in networks, as logical formulas, or in other symbol structures
tailored to specific problem areas. Furthermore, they had accumulated
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8

NOTES

experience with heuristic search and other techniques for manipulating andusing that knowledge. Also, researchers now had new programming languages,
IPL, LISP, and POP-2, that made it easier to write symbol-processing programs.
Complementing all of this symbol-processing technology were neural networks
and related statistical approaches to pattern recognition. These technicalassets, along with the organizational and financial ones, provided a solid basefor the next stage of AI’s development.

Notes
1. For McCarthy’s own history of the development of LISP, see
http://www-formal.stanford.edu/jmc/history/lisp.html. Also see Herbert Stoyan’s history of
LISP at http://www8.informatik.uni-erlangen.de/html/lisp-enter.html. [156]2. See McCarthy’s memo proposing how to build a time-sharing system at
http://www-formal.stanford.edu/jmc/history/timesharing-memo.html. [157]
3. For more about these early days of computing at MIT and of time-sharing work there(among other things), see the interview with John McCarthy conducted by William Asprayof the Charles Babbage Institute on March 2, 1989. It is available online at
http://www.cbi.umn.edu/oh/display.phtml?id=92. [157]
4. From an interview conducted by Arthur L. Norberg on November 1, 1989, for theCharles Babbage Institute. Available online at
http://www.cbi.umn.edu/oh/display.phtml?id=107. [157]
5. For a history of AI work in the lab up to 1973, see Lester Earnest (ed.), “Final Report:
The First Ten Years of Artificial Intelligence Research at Stanford,” Stanford Artificial
Intelligence Laboratory Memo AIM-228 and Stanford Computer Science Department Report
No. STAN-CS-74-409, July 1973. (Available online at
http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.) For other
SAIL history, see “SAIL Away” by Les Earnest at
http://www.stanford.edu/∼learnest/sailaway.htm. [158]
6. A textscript of the interview can be found online at
http://www.aiai.ed.ac.uk/events/ccs2002/CCS-early-british-ai-dmichie.pdf. [158]
7. Donald Michie, “Experiments on the Mechanisation of Game Learning: 1.
Characterization of the Model and its Parameters,” Computer Journal, Vol. 1, pp. 232–263,
1963. [158]
8. For a history of these Edinburgh groups, see Jim Howe’s online 1994 article “Artificial
Intelligence at Edinburgh University: A Perspective” at
http://www.dai.ed.ac.uk/AI at Edinburgh perspective.html. [160]9. National Research Council, Funding a Revolution: Government Support for Computing
Research, Washington, DC: National Academy Press, 1999. (An html version of this book,which contains a rather conservative account of AI history, is available fromhttp://www.nap.edu/catalog.php?record id=6323#toc.) [160]10. From “An Interview with John McCarthy,” conducted by William Aspray on 2 March
1989, Palo Alto, CA, Charles Babbage Institute, The Center for the History of Information
Processing, University of Minnesota, Minneapolis. [160]11. J. C. R. Licklider, “Man–Computer Symbiosis,” IRE Transactions on Human Factors inElectronics, HFE-1, pp. 4–11, 1960. Available online at http://memex.org/licklider.html.
[161]

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8.4

NOTES

12. J. C. R. Licklider, “The Early Years: Founding IPTO,” p. 220 in Thomas C. Bartee(ed.), Expert Systems and Artificial Intelligence: Applications And Management,
Indianapolis: Howard W. Sams, 1988. [161]13. Paul Edwards, The Closed World: Computers and the Politics of Discourse in Cold
War America, p. 270, Cambridge, MA: MIT Press, 1996. [161]14. From “An Interview with John McCarthy,” op. cit. [162]
15. Lester Earnest (ed.), “Final Report: The First Ten Years of Artificial Intelligence
Research at Stanford,” Stanford Artificial Intelligence Laboratory Memo AIM-228 andStanford Computer Science Department Report No. STAN-CS-74-409, July 1973. (Available
online at http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.)[162]
16. 12th National Meeting of the Operations Research Society (ORSA) in Pittsburgh. [163]17. The published version of this talk is in Herbert Simon and Allen Newell, “Heuristic
Problem Solving: The Next Advance in Operations Research,” Operations Research, Vol. 6,
January–February 1958. [163]18. Hubert L. Dreyfus, “Overcoming the Myth of the Mental,” Topoi, Vol. 25, pp. 43–49,
2006. [163]165
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8

NOTES

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8.4

Part III

Efflorescence: Mid-1960s to
Mid-1970s

During the 1960s and well into the 1970s, AI research blossomed andprogress seemed rapid. The laboratories established at MIT, Carnegie Mellon,
Stanford, SRI, and Edinburgh expanded, and several new groups got started
at other universities and companies. Achievements during the preceding years,
even though modest in retrospect, were exciting and full of promise, which
enticed several new people into the field, myself included. Many of us were
just as optimistic about success as Herb Simon and Marvin Minsky were when
they made their predictions about rapid progress.
AI entered a period of flowering that led to many new and importantinventions. Several ideas originated in the context of Ph.D. dissertation
research projects. Others emerged from research laboratories and fromindividual investigators wrestling with theoretical problems. In this part, I’ll
highlight some of the important projects and research results. Although not acomplete account, they typify much of what was going on in AI during theperiod. I’ll begin with work in computer vision.

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8168
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9.0

Chapter 9

Computer Vision
Sighted humans get much of their information through vision. That part of AI
called “computer vision” (or, sometimes, “machine vision”) deals with giving
computers this ability. Most computer vision work is based on processing
two-dimensional images gathered from a three-dimensional world – images
gathered by one or more television cameras, for example. Because the images
are two-dimensional projections of a three-dimensional scene, the imaging
process loses information. That is, different three-dimensional scenes might
produce the same two-dimensional image. Thus, the problem of reconstructing
the scene faithfully from an image is impossible in principle.
Yet, people and other animals manage very well in a three-dimensional
world. They seem to be able to interpret the two-dimensional images formedon their retinas in a way that gives them reasonably accurate and useful
information about their environments.
Stereo vision, using two eyes, helps provide depth information. Computer
vision too can use “stereopsis” by employing two or more differently locatedcameras looking at the same scene. (The same effect can be achieved byhaving one camera move to different positions.) When two cameras are used,
for example, the images formed by them are slightly displaced with respect toeach other, and this displacement can be used to calculate distances to various
parts of the scene. The computation involves comparing the relative locationsin the images that correspond to the objects in the scene for which depth
measurements are desired. This “correspondence problem” has been solved invarious ways, one of which is to seek high correlations between small areas in
one image with small areas in the other. Once the “disparity” of the locationof an image feature in the two images is known, the distance to that part ofthe scene giving rise to this image feature can be calculated by using
trigonometric calculations (which I won’t go into here.)1
Perhaps surprisingly, a lot of depth information can be obtained from

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9Computer Vision

other cues besides stereo vision. Some of these cues are inherent in a single
image, and I’ll be describing these in later chapters. Even more importantly,
background knowledge about the kinds of objects one is likely to see accounts
for much of our ability to interpret images. Consider the image shown in Fig.
9.1 for example.

Figure 9.1: Two tables. (Illustration courtesy of Michael Bach.)
Most people would describe this image as being of two tables, one longand narrow and the other more-or-less square. Yet, if you measure the actual
table tops in the image itself, you might be surprised to find that they areexactly the same size and shape! (The illustration is based on an illusion
called “turning the tables” by the psychologist Roger Shepherd and is adaptedfrom Michael Bach’s version of Shepherd’s diagram. If you visit Bach’s Web
site, http://www.michaelbach.de/ot/sze shepardTables/, you can watch while
one table top moves over to the other without changing shape.)
Something apart from the image provides us with information thatinduces us to make inferences about the shapes of the three-dimensional tables
captured in the two-dimensional image shown in Fig. 9.1. As we shall see, thatextra information consists of two things: knowledge about the image-forming
process under various lighting conditions and knowledge about the kinds ofthings and their surfaces that occur in our three-dimensional world. If we
could endow computers with this sort of knowledge, perhaps they too would
be able to see.

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9.1

Hints from Biology

9.1

Hints from Biology

There has been a steady flow of information back and forth between scientists
attempting to understand how vision works in animals and engineers working
on computer vision. An early example of work at the intersection of these twointerests was described in an article titled “What the Frog’s Eye Tells the
Frog’s Brain”2 by four scientists at MIT. Guided by previous biological work,
the four, Jerome Lettvin, H. R. Maturana, Warren McCulloch, and Walter
Pitts, probed the parts of the frog’s brain that processed images. They foundthat the frog’s visual system consisted of “detectors” that responded only tocertain kinds of things in its visual field. It had detectors for small, moving
convex objects (such as flies) and for a sudden darkening of illumination (suchas might be caused by a looming predator). These, together with a couple ofother simple detectors, gave the frog information about food and danger. In
particular, the frog’s visual system did not, apparently, construct a complete
three-dimensional model of its visual scene. As the authors wrote,
The frog does not seem to see or, at any rate, is not concerned withthe detail of stationary parts of the world around him. He will
starve to death surrounded by food if it is not moving. His choiceof food is determined only by size and movement. He will leap to
capture any object the size of an insect or worm, providing it
moves like one. He can be fooled easily not only by a bit of dangled
meat but by any moving small object. His sex life is conducted bysound and touch. His choice of paths in escaping enemies does notseem to be governed by anything more devious than leaping towhere it is darker. Since he is equally at home in water and onland, why should it matter where he lights after jumping or whatparticular direction he takes?
Other experiments produced further information about how the brain
processes visual images. Neurophysiologists David Hubel (1926– ) and Torsten
Wiesel (1924– ) performed a series of experiments, beginning around 1958,
which showed that certain neurons in the mammalian visual cortex responded
selectively to images and parts of images of specific shapes. In 1959 they
implanted microelectrodes in the primary visual cortex of an anesthetized cat.
They found that certain neurons fired rapidly when the cat was shown imagesof small lines at one angle and that other neurons fired rapidly in response tosmall lines at another angle. In fact, they could make a “map” of this area ofthe cat’s brain, relating neuron location to line angle. They called theseneurons “simple cells” – to be distinguished from other cells, called “complex
cells,” that responded selectively to lines moving in a certain direction. Laterwork revealed that other neurons were specialized to respond to images
containing more complex shapes such as corners, longer lines, and large
edges.3 They found that similar specialized neurons also existed in the brains
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9Computer Visionof monkeys.4 Hubel and Wiesel were awarded the Nobel Prize in Physiology or
Medicine in 1981 (jointly with Roger Sperry for other work).5
As I’ll describe in later sections, computer vision researchers weredeveloping methods for extracting lines (both large and small) from images.
Hubel and Wiesel’s work helped to confirm their view that finding lines inimages was an important part of the visual process. Yet, straight lines seldom
occur in the natural environments in which cats (and humans) evolved, so why
do they (and we) have neurons specialized for detecting them? In fact, in
1992 the neuroscientists Horace B. Barlow and David J. Tolhurst wrote a
paper titled “Why Do You Have Edge Detectors?”6 As a possible answer tothis question, Anthony J. Bell and Terrence J. Sejnowski later showedmathematically that natural scenes can be analyzed as a weighted summationof small edges even though the scenes themselves do not have obvious edges.7

9.2

Recognizing Faces

In the early 1960s at his Palo Alto company, Panoramic Research, Woodrow(Woody) W. Bledsoe (who later did work on automatic theorem proving at
the University of Texas), along with Charles Bisson and Helen Chan (later
Helen Chan Wolf), developed techniques for face recognition supported byprojects from the CIA.8 Here is a description of their approach taken from amemorial article:9
This [face-recognition] project was labeled man-machine because
the human extracted the coordinates of a set of features from thephotographs, which were then used by the computer forrecognition. Using a GRAFACON, or RAND TABLET, theoperator would extract the coordinates of features such as thecenter of pupils, the inside corner of eyes, the outside corner ofeyes, point of widows peak, and so on. From these coordinates, alist of 20 distances, such as width of mouth and width of eyes, pupil
to pupil, were computed. These operators could process about 40
pictures an hour. When building the database, the name of theperson in the photograph was associated with the list of computed
distances and stored in the computer. In the recognition phase, theset of distances was compared with the corresponding distance foreach photograph, yielding a distance between the photograph andthe database record. The closest records are returned.
Bledsoe continued this work with Peter Hart at SRI after leaving Panoramic in
1966.10
Then, in 1970, a Stanford Ph.D. student, Michael D. Kelly, wrote acomputer program that was able automatically to detect facial features in172
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9.3

Computer Vision of Three-Dimensional Solid Objects

pictures and use them to identify people.11 The task for his program was, as
he put it,
to choose, from a collection of pictures of people taken by a TV
camera, those pictures that depict the same person. . . .
In brief, the program works by finding the location of features suchas eyes, nose, or shoulders in the pictures. . . . The interesting anddifficult part of the work reported in this thesis is the detection ofthese features in digital pictures. The nearest-neighbor method isused for identification of individuals once a set of measurements
has been obtained.
Another person who did pioneering work in face recognition was visionresearcher Takeo Kanade, now a professor at Carnegie Mellon University. In a
2007 speech at the Eleventh IEEE International Conference on Computer
Vision, he reflected on his early work in this field:12 “I wrote my facerecognition program in an assembler language, and ran it on a machine with10 microsecond cycle time and 20 kB of main memory. It was with pride that
I tested the program with 1000 face images, a rare case at the time when
testing with 10 images was called a ‘large-scale’ experiment.” (By the way,
Kanade has continued his face recognition work up to the present time. His
face-recognition Web page is at http://www.ri.cmu.edu/labs/lab 51.html.)
Face recognition programs of the 1960s and 1970s had several limitations.
They usually required that images be of faces of standard scale, pose,expression, and illumination. Toward the end of the book, I’ll describe
research leading to much more robust automatic face recognition.

9.3

Computer Vision of Three-Dimensional
Solid Objects

9.3.1

An Early Vision System

Lawrence G. Roberts (1937– ), an MIT Ph.D. student working at Lincoln
Laboratory, was perhaps the first person to write a program that could
identify objects in black-and-white (gray-scale) photographs and determine
their orientation and position in space. (His program was also the first to use
a “hidden-line” algorithm, so important in subsequent work in computer
graphics. As chief scientist and later director of ARPA’s Information
Processing Techniques Office, Roberts later played an important role in thecreation of the Arpanet, the forerunner of the Internet.)
In the introduction to his 1963 MIT Ph.D. dissertation,13 Roberts wrote

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9Computer Vision
The problem of machine recognition of pictorial data has long beena challenging goal, but has seldom been attempted with anything
more complex than alphabetic characters. Many people have feltthat research on character recognition would be a first step, leading
the way to a more general pattern recognition system. However,
the multitudinous attempts at character recognition, including myown, have not led very far. The reason, I feel, is that the study of
abstract, two-dimensional forms leads us away from, not toward,
the techniques necessary for the recognition of three-dimensional
objects. The perception of solid objects is a process which can bebased on the properties of three-dimensional transformations andthe laws of nature. By carefully utilizing these properties, a
procedure has been developed which not only identifies objects, butalso determines their orientation and position in space.

Roberts’s system first processed a photograph of a scene to produce a
representation of a line drawing. It then transformed the line drawing into athree-dimensional representation. Matching this representation against a
stored list of representations of solid objects allowed it to classify the object it
was viewing. It could also produce a computer-graphics image of the object as
it might be seen from any point of view.
Our main interest here is in how Roberts processed the photographic
image. After scanning the photograph and representing it as an array ofnumbers (pixels) representing intensity values, Roberts used a special
calculation, later called the “Roberts Cross,” to determine whether or not eachsmall 2 × 2 square in the array corresponded to a part of the image having anabrupt change in image intensity. (The Roberts Cross was the first example ofwhat were later called “gradient operators.”) He then rerepresented the image“lighting up” only those parts of the image where the intensity changed
abruptly and leaving “dark” those parts of the image with more-or-lessuniform intensity. The result of this process is illustrated in Fig. 9.2 for atypical image used in Roberts’s dissertation. As can be seen in that figure,large changes in image intensity are usually associated with the edges ofobjects. Thus, gradient operators, such as the Roberts Cross, are often called“edge detectors.”
Further processing of the image on the right attempted to connect thedots representing abrupt intensity changes by small straight-line segments,
then by longer line segments. Finally, a line drawing of the image wasproduced. This final step is shown in Fig. 9.3.
Roberts’s program was able to analyze many different photographs ofsolid objects. He commented that “The entire picture-to-line-drawing process
is not optimal but works for simple pictures.” Roberts’s success stimulated
further work on programs for finding lines in images and for assembling theselines into representations of objects. Perhaps primed by Roberts’s choice of174
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9.3

Computer Vision of Three-Dimensional Solid Objects

Figure 9.2: Detecting changes in intensity. (Photographs used with permissionof Lawrence Roberts.)

Figure 9.3: Producing the final line drawing. (Photographs used with permissionof Lawrence Roberts.)
solid objects, much of the subsequent work dealt with toy blocks (or “bricks”
as they are called in Britain).

9.3.2

The “Summer Vision Project”

Interestingly, Larry Roberts was a student of MIT information theory
professor Peter Elias, not of Marvin Minsky. But Minsky’s group soon began
to work on computer vision also. In the summer of 1966, the mathematician
and psychologist Seymour Papert, a recent arrival at MIT’s Artificial
Intelligence Group, launched a “summer vision project.” Its goal was todevelop a suite of programs that would analyze a picture from a “videsector”(a kind of scanner) to “actually name objects [such as balls, cylinders, andblocks] by matching them with a vocabulary of known objects.” One
motivation for the project was “to use our summer workers effectively in the175
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9Computer Vision

construction of a significant part of a visual system.”14
Of course, the problem of constructing “a significant part of a visualsystem” was much more difficult than Papert expected. Nevertheless, theproject was successful in that it began a sustained effort in computer visionresearch at MIT, which continues to this day.
After these early forays at MIT (and similar ones at Stanford and SRI to
be described shortly), computer vision research focused on two areas. The firstwas what might be called “low-level” vision – those first stages of image
processing that were aimed at constructing a representation of the image as aline drawing, given an image that was of a scene containing rather simple
objects. The second area was concerned with how to analyze the line drawing
as an assemblage of separate objects that could be located and identified. An
important part of low-level vision was “image filtering,” to be described next.

9.3.3

Image Filtering

The idea of filtering an image to simplify it, to correct for noise, and toenhance certain image features had been around for a decade or more. I havealready mentioned, for example, that in 1955 Gerald P. Dinneen processed
images to remove noise and enhance edges. Russell Kirsch and colleagues had
also experimented with image processing.15 (Readers who have manipulated
their digital photography pictures on a computer have used some of theseimage filters.) Filtering two-dimensional images is not so very different fromfiltering one-dimensional electronic signals – a commonplace operation.
Perhaps the simplest operation to describe is “averaging,” which blurs finedetail and removes random noise specks. As in all averaging operations, image
averaging takes into account adjacent values and combines them. Consider, forexample, the image array of intensity values shown in Fig. 9.4 containing a
3 × 3 “averaging window” outlined in bold. These intensity values correspondto an image whose right side is bright and whose left side is dark with a sharpedge between. (I adopt the convention that large numbers, such as 10
correspond to brightly illuminated parts of the image, and the number 0
corresponds to black.)
The averaging operation moves the averaging window over the entire
image so that its center lies over each pixel in turn. For each placement of thewindow, the value of the intensity at its center is replaced (in the filteredversion) by the average intensity of the values within the window. (Theprocess of moving a window around the image and doing calculations based on
the numbers in the window is called convolution.) In this example, the 0 at
the center of the window would be replaced by 3.33 (perhaps rounded down to3). One can see that averaging blurs the sharp edge – with the 10 fading to (arounded) 7 fading to 3 fading to 0 as one moves from right to left. However,intensities well within evenly illuminated regions are not changed.

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9.3

Computer Vision of Three-Dimensional Solid Objects

Figure 9.4: An array of image intensity values and an averaging window.
I have already mentioned another important filtering operation, theRoberts Cross, for detecting abrupt brightness changes in an image. Anotherone was developed in 1968 by a Ph.D. student at Stanford, Irwin Sobel. It was
dubbed the “Sobel Operator” by Raj Reddy who described it in a Computer
Vision course at Stanford.16 The operator uses two filtering windows – one
sensitive to large gradients (intensity changes) in the vertical direction and one
to large gradients in the horizontal direction. These are shown in Fig. 9.5.

Figure 9.5: Sobel’s vertical (left) and horizontal (right) filters.
Each of the Sobel filters works the same way as the averaging filter,
except that the image intensity at each point is multiplied by the number inthe corresponding cell of the filtering window before adding all of the numbers.
The sum would be 0 inside regions of uniform illumination. If the verticalfilter is centered over a vertical edge (with the right side brighter than theleft), the sum would be positive. (I’ll let you think about the otherpossibilities.) Results from the two filtering windows are combined
mathematically to detect abrupt changes in any direction.
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9Computer Vision

The images in Fig. 9.6 illustrate the Sobel Operator. The image on theright is the result of applying the Sobel Operator to the image on the left.

Figure 9.6: Finding abrupt changes in image brightness with the Sobel Operator.
(Photographs taken by George Miller and available at http://en.wikipedia.org/
wiki/Sobel operator. Used under the terms of the GNU Free Documentation
License.)A number of other more complex and robust image processing operations
have been proposed and used for finding edges, lines, and vertices of objects inimages.17 A particularly interesting one for finding edges was proposed by the
British neuroscientist and psychologist David Marr (1945–1980) and Ellen
Hildreth.18 The Marr–Hildreth edge detector uses a filtering window called a“Laplacian of Gaussian (LoG).” (The name arises because a mathematicaloperator called a “Laplacian” is used on a bell-shaped curve called a“Gaussian,” commemorating two famous mathematicians, namely,
Pierre-Simon Laplace and Carl Friedrich Gauss.) In Fig. 9.7, I show an
example of LoG numbers in a 9 × 9 filtering window. This window is moved
around an image, multiplying image numbers and adding them up, in thesame way as the other filtering windows I have already mentioned.

Figure 9.7: A Laplacian of Gaussian filtering window.
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9.3

Computer Vision of Three-Dimensional Solid Objects

If LoG numbers are plotted as “heights” above (and below) a plane, aninteresting-looking surface results. An example is shown in Fig. 9.8. This LoG
function is often called, not surprisingly, a Mexican hat or sombrero function.

Figure 9.8: A Laplacian of Gaussian surface.

Figure 9.9: An image (left) and its LoG-processed version (right). (Images takenfrom David Marr and E. Hildreth, “Theory of Edge Detection,” Proceedings of
the Royal Society of London, Series B, Biological Sciences, Vol. 207, No. 1167,
p. 198, February 1980.)
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9Computer Vision

Marr and Hildreth used the LoG filtering window on several example
images. One example, taken from their paper, is shown in Fig 9.9. Notice thatthe image on the right has whitish bands surrounding darker parts of theimage. The Marr–Hildreth edge detector employs a second image-processing
operation that looks for the transitions from light to dark (and vice versa) in
the LoG-processed image to produce a final “line drawing,” as shown in Fig.
9.10.

Figure 9.10: The final result of a Marr–Hildreth edge-detecting operation.
(From David Marr and E. Hildreth, “Theory of Edge Detection,” Proceedings of
the Royal Society of London, Series B, Biological Sciences, Vol. 207, No. 1167,
p. 198, February 1980.)
Further advances have been made in edge detection since Marr and
Hildreth’s work. Among the currently best detectors are those related to one
proposed by John Canny called the Canny edge detector.19
As a neurophysiologist, Marr was particularly interested in how thehuman brain processes images. In a 1976 paper,20 he proposed that the firststage of processing produces what he called a “primal sketch.” As he puts it inhis summary of that paper,
It is argued that the first step of consequence is to compute aprimitive but rich description of the grey-level changes present in
an image. The description is expressed in a vocabulary of kinds ofintensity change (EDGE, SHADING-EDGE, EXTENDED-EDGE,
LINE, BLOB etc.). . . . This description is obtained from theintensity array by fixed techniques, and it is called the primal
sketch.
Marr and Hildreth put forward their edge detector as one of theoperations the brain uses in producing a primal sketch. They stated that their
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9.3

Computer Vision of Three-Dimensional Solid Objects

theory “explains several basic psychophysical findings, and. . . forms the basis
for a physiological model of simple [nerve] cells.”
Marr’s promising career in vision research ended when he succumbed tocancer in 1980. During the last years of his life he completed an importantbook detailing his theories of human vision.21 I’ll describe some of Marr’s
ideas about other visual processing steps in a subsequent chapter.

9.3.4

Processing Line Drawings

Assuming, maybe somewhat prematurely, that low-level vision routines could
produce a line-drawing version of an image, many investigators moved on todevelop methods for analyzing line drawings to find objects in images.
Adolfo Guzman-Arenas (1943– ), a student in Minsky’s AI Group,
focused on how to segment a line drawing of a scene containing blocks into its
constituents, which Guzman called “bodies.” His LISP program foraccomplishing this separation was called SEE and ran on the MIT AI Group’s
PDP-6 computer.22 The input to SEE was a line-drawing representation of ascene in terms of its surfaces, lines (where two surfaces came together), andvertices (where lines came together).
SEE’s analysis of a scene began by sorting its vertices into a number ofdifferent types. For each vertex, depending on its type, SEE connectedadjacent planar surfaces with “links.” The links between surfaces provide
evidence that those surfaces belong to the same body. For example, some links
for a scene analyzed by SEE are shown in Fig. 9.11.
SEE performed rather well on a wide variety of line drawings. For
example, it correctly found all of the bodies in the scene shown in Fig. 9.12.

For most of his work, Guzman assumed that somehow other programs
would produce his needed line drawings from actual images. As he wrote in apaper describing his research,23
The scene itself is not obtained from a visual input device, or froman array of intensities of brightness. Rather, it is assumed that a
preprocessing of some sort has taken place, and the scene to beanalyzed is available in a symbolic format. . . in terms of points(vertices), lines (edges), and surfaces (regions).”
Additionally, Guzman did not concern himself with what might be done
after the scene had been separated into bodies:. . . it cannot find “cubes” or “houses” in a scene, since it does notknow what a “house” is. Once SEE has partitioned a scene into
bodies, some other program will work on them and decide which of
those bodies are “houses.”
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9Computer Vision

Figure 9.11: Links established by SEE for a sample scene. (Illustration usedwith permission of Adolpho Guzman.)
Later extensions to SEE, reported in the final version of his thesis,
involved some procedures for image capture. But the images were of speciallyprepared scenes, as he recently elaborated:24
Originally SEE worked on hand-drawn scenes, “perfect scenes”(drawings of lines). . .
Later, I constructed a bunch of wooden polyhedra (mostly
irregular), painted them black, carefully painted their edges white,
piled several of them together, and took pictures of the scenes. The
pictures were scanned, edges found, and given to SEE. It worked
quite well on them.
Although SEE was capable of finding bodies in rather complex scenes, italso could make mistakes, and it could not identify blocks that had holes inthem.
The next person to work on the problem of scene articulation was David
Huffman (1925–1999), a professor of Electrical Engineering at MIT. (Huffman
was famous for his invention, while a graduate student at MIT, of what cameto be called “Huffman coding,” an efficient scheme that is used today in many
applications involving the compression and transmission of digital data.)
Huffman was bothered by what he considered Guzman’s incomplete analysis ofwhat kinds of objects could correspond to what kinds of line drawings. After
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9.3

Computer Vision of Three-Dimensional Solid Objects

Figure 9.12: A scene analyzed by SEE. (Illustration used with permission of
Adolpho Guzman.)leaving MIT in 1967 to become a professor of Information and Computer
Science at the University of California at Santa Cruz, he completed a theory
for assigning labels to the lines in drawings of trihedral solids – objects inwhich exactly three planar surfaces join at each vertex of the object. The
labels depended on the ways in which planes could come together at a vertex.
(I got to know Huffman well at that time because he consulted frequently at
the Stanford Research Institute.)
Huffman pointed out that there are only four ways in which three planesurfaces can come together at a vertex.25 These are shown in Fig. 9.13. In
addition to these four kinds of vertices, a scene might contain what Huffman
called “T-nodes” – line intersection types caused by one object in a scene
occluding another. These all give rise to a number of different kinds of labels
for the lines in the scene; these labels specify whether the lines correspond toconvex, concave, or occluding edges.
Huffman noted that the labels of the lines in a drawing might be locallyconsistent (around some vertices) but still be globally inconsistent (around allof the vertices). Consider, for example, Roger Penrose’s famous line drawing ofan “impossible object” shown in Fig. 9.14.26 (It is impossible because nothree-dimensional object, viewed in “general position,” could produce thisimage.) No “real scene” can have a line with two different labels.

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9Computer Vision

Figure 9.13: The four different kinds of vertices that can occur in trihedralsolids.

Figure 9.14: An impossible object.

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9.3

Computer Vision of Three-Dimensional Solid Objects

Max Clowes (circa 1944–1981) of Sussex University in Britain developed
similar ideas independently,27 and the labeling scheme is now generally known
as Huffman–Clowes labeling.
Next comes David Waltz (1943– ). In his 1972 MIT Ph.D. thesis, heextended the Huffman–Clowes line-labeling scheme to allow for line drawingsof scenes with shadows and possible “cracks” between two adjoining objects.28
Waltz’s important contribution was to propose and implement an efficientcomputational method for satisfying the constraint that all of the lines must
be assigned one and only one label. (For example, an edge can’t be concave
at one end and convex at the other.) In Fig. 9.15, I show an example of a line
drawing that Waltz’s program could correctly segment into its constituents.

Figure 9.15: A scene with shadows analyzed by Waltz’s program. (Illustration
used with permission of David Waltz.)
Summarizing some of the work on processing line drawings at MIT,
Patrick Winston says that “Guzman was the experimentalist, Huffman thetheoretician, and Waltz the encyclopedist (because Waltz had to catalog
thousands of junctions, in order to deal with cracks and shadows).”29
Meanwhile, similar work for finding, identifying, and describing objects inthree-dimensional scenes was being done at Stanford. By 1972 ElectricalEngineering Ph.D. student Gilbert Falk could segment scenes of line drawings
into separate objects using techniques that were extensions of those of
Guzman.30 And by 1973, Computer Science Ph.D. student Gunnar Grape
performed segmentation of scenes containing parallelepipeds and wedges using
models of those objects.31
Other work on analysis of scenes containing polyhedra was done by
Yoshiaki Shirai while he was visiting MIT’s AI Lab32 and by Alan Mackworth
at the Laboratory of Experimental Psychology of the University of Sussex.33

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9

NOTES

Notes
1. For a thorough treatment, see David Forsyth and Jean Ponce, Computer Vision: A
Modern Approach, Chapter 13, Upper Saddle River, NJ: Prentice Hall, 2003. [169]2. Lettvin et al., “What the Frog’s Eye Tells the Frog’s Brain,” Proceedings of the IRE,
Vol. 47, No. 11, pp. 1940–1951, 1959. [Reprinted as Chapter 7 in William C. Corning andMartin Balaban (eds.), The Mind: Biological Approaches to Its Functions, pp. 233–258,
1968.] [171]3. David H. Hubel and Torsten N. Wiesel, “Receptive Fields, Binocular Interaction andFunctional Architecture in the Cat’s Visual Cortex,” Journal of Physiology, Vol. 160, pp.
106–154, 1962. [171]4. David H. Hubel and Torsten N. Wiesel, “Receptive Fields and Functional Architectureof Monkey Striate Cortex, Journal of Physiology, Vol. 195, pp. 215–243, 1968. [172]5. An interesting account of Hubel’s and Wiesel’s work and descriptions about how thebrain processes visual images can be found in Hubel’s online book Eye, Brain, and Vision at
http://neuro.med.harvard.edu/site/dh/index.html. [172]6. Horace B. Barlow and D. J. Tolhurst, “Why Do You Have Edge Detectors?,” inProceedings of the 1992 Optical Society of America Annual Meeting, Technical Digest Series,
Vol. 23, pp. 172, Albuquerque, NM, Washington: Optical Society of America, 1992. [172]7. Anthony J. Bell and Terrence J. Sejnowski, “Edges Are the ‘Independent Components’of Natural Scenes,” Advances in Neural Information Processing Systems, Vol. 9, Cambridge,
MA: MIT Press, 1996. Available online at ftp://ftp.cnl.salk.edu/pub/tony/edge.ps.Z. [172]8. Woodrow W. Bledsoe and Helen Chan, “A Man–Machine Facial Recognition System:
Some Preliminary Results,” Technical Report PRI 19A, Panoramic Research, Inc., Palo
Alto, CA, 1965. [172]9. Michael Ballantyne, Robert S. Boyer, and Larry Hines, “Woody Bledsoe: His Life andLegacy,” AI Magazine, Vol. 17, No. 1, pp. 7–20, 1996. Also available online at
http://www.utexas.edu/faculty/council/1998-1999/memorials/Bledsoe/bledsoe.html. [172]10. Woodrow W. Bledsoe, “Semiautomatic Facial Recognition,” Technical Report SRI
Project 6693, Stanford Research Institute, Menlo Park, CA, 1968. [172]11. Michael D. Kelly, Visual Identification of People by Computer, Stanford AI Project,
Stanford, CA, Technical Report AI-130, 1970. [173]12. http://iccv2007.rutgers.edu/TakeoKanadeResponse.htm. [173]13. Lawrence G. Roberts, “Machine Perception of Three-Dimensional Solids,” MIT Ph.D.
thesis, 1963; published as Lincoln Laboratory Technical Report #315, May 22, 1963;
appears in J. T. Tippett et al. (eds.), Optical and Electro-Optical Information Processing,pp. 159–197, Cambridge, MA: MIT Press, 1965. Available online at
http://www.packet.cc/files/mach-per-3D-solids.html. [173]14. The project is described in MIT’s Artificial Intelligence Group Vision Memo No. 100
available at ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-100.pdf. [176]15. Russell A. Kirsch et al., “Experiments in Processing Pictorial Information with a Digital
Computer,” Proceedings of the Eastern Joint Computer Conference, pp. 221–229, Instituteof Radio Engineers and Association Association for Computing Machinery, December 1957.
[176]
16. According to Sobel, he and a fellow student, Gary Feldman, first presented the operator
in a Stanford AI seminar in 1968. It was later described in Karl K. Pingle, “Visual
Perception by a Computer,” in A. Grasselli (ed.), Automatic Interpretation andClassification of Images, pp. 277–284, New York: Academic Press, 1969. It was also186
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9.3

NOTES

mentioned in Richard O. Duda and Peter E. Hart, Pattern Classification and Scene
Analysis, pp. 271–272, New York: John Wiley & Sons, 1973. [177]17. See, for example, M. H. Hueckel, “An Operator Which Locates Edges in Digitized
Pictures,” Journal of the ACM, Vol. 18, No. 1, pp. 113–125, January 1971, and Berthold K.
P. Horn, “The Binford–Horn Line Finder,” MIT AI Memo 285, MIT, July 1971 (revised
December 1973 and available online at
http://people.csail.mit.edu/bkph/AIM/AIM-285-OPT.pdf). [178]18. David Marr and Ellen Hildreth, “Theory of Edge Detection,” Proceedings of the Royal
Society of London, Series B, Biological Sciences, Vol. 207, No. 1167, pp. 187–217, February
1980. [178]19. John E. Canny, “A Computational Approach to Edge Detection,” IEEE Transactions
Pattern Analysis and Machine Intelligence, Vol. 8, pp. 679–714, 1986. [180]20. David Marr, “Early Processing of Visual Information,” Philosophical Transactions of
the Royal Society of London, Series B, Biological Sciences, Vol. 275, No. 942, pp. 483–519,
October 1976. [180]21. David Marr, Vision: A Computational Investigation into the Human Representation
and Processing of Visual Information, San Francisco: W.H. Freeman and Co., 1982. [181]22. Guzman’s 1968 Ph.D. thesis is titled “Computer Recognition of Three Dimensional
Objects in a Visual Scene” and is available online at
http://www.lcs.mit.edu/publications/pubs/pdf/MIT-LCS-TR-059.pdf. [181]23. Adolfo Guzman, “Decomposition of a Visual Scene into Three-Dimensional Bodies,”
AFIPS, Vol. 33, pp. 291–304, Washington, DC: Thompson Book Co., 1968. Available online
as an MIT AI Group memo at
ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-171.pdf. [181]24. Personal communication, September 14, 2006. [182]25. David A. Huffman, “Impossible Objects as Nonsense Sentences,” in B. Meltzer and D.
Michie (eds.), Machine Intelligence 6, pp. 195–234, Edinburgh: Edinburgh University Press,
1971, and David A. Huffman, “Realizable Configurations of Lines in Pictures of Polyhedra,”in E. W. Elcock and D. Michie (eds.), Machine Intelligence 8, pp. 493–509, Chicester: Ellis
Horwood, 1977. [183]26. According to Wikipedia, this impossible object was first drawn by the Swedish artist
Oscar Reutersvärd in 1934. [183]27. Max B. Clowes, “On Seeing Things,” Artificial Intelligence, Vol. 2, pp. 79–116, 1971.
[185]
28. David L. Waltz, “Generating Semantic Descriptions from Drawings of Scenes with
Shadows,” MIT AI Lab Technical Report No. AITR-271, November 1, 1972. Available
online at https://dspace.mit.edu/handle/1721.1/6911. A condensed version appears in
Patrick Winston (ed.), The Psychology of Computer Vision, pp. 19–91, New York:
McGraw-Hill, 1975. [185]29. Personal communication, September 20, 2006. [185]30. Gilbert Falk, “Computer Interpretation of Imperfect Line Data as a Three-Dimensional
Scene,” Ph.D. thesis in Electrical Engineering, Stanford University, Artificial Intelligence
Memo AIM-132, and Computer Science Report No. CS180, August 1970. Also see Gilbert
Falk, “Interpretation of Imperfect Line Data as a Three-Dimensional Scene,” Artificial
Intelligence, Vol. 3, pp. 101–144, 1972. [185]31. Gunnar Rutger Grape, “Model Based (Intermediate Level) Computer Vision,” Stanford
Computer Science Ph.D. thesis, Artificial Intelligence Memo AIM-204, and Computer
Science Report No. 266, May 1973. [185]32. Yoshiaki Shirai, “A Heterarchical Program for Recognition of Polyhedra,” MIT AI

187
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9

NOTES

Memo No. 263, June 1972. Available online at
ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-263.pdf. [185]33. Alan K. Mackworth, “Interpreting Pictures of Polyhedral Scenes,” Artificial Intelligence,Vol. 4, No. 2, pp. 121–137, June 1973. [185]188
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10.1

Chapter 10“Hand–Eye” Research
The motivation for much of the computer vision research that I have described
during this period was to provide information to guide a robot arm. Because
the images that could be analyzed best were of simple objects such as toy
blocks, work was concentrated on getting a robot arm to stack and unstackblocks. I’ll describe some typical examples of this “hand–eye” research,
beginning with a project that did not actually involve an “eye.”

10.1

At MIT

A computer-guided mechanical “hand” was developed by Heinrich A. Ernst in
1961 as part of his Electrical Engineering Sc.D. work at MIT.1 (His advisor
was Claude Shannon.) The hand, named MH-1, was a “mechanicalservomanipulator [an American Machine and Foundry model 8] adapted for
operation by the TX-0 computer.” It used tactile sensors mounted on the handto guide it because, as Ernst wrote, “organs for vision are too difficult to build
at the present time.” The abstract of Ernst’s thesis describes some of what thesystem could do:[O]ne program consisting of nine statements will make the hand do
the following: Search the table for a box, remember its position,
search the table for blocks, take them and put them into the box.
The position of the objects is irrelevant as long as they are on thetable. If as a test for the built-in mechanical intelligence, the box
should be taken away and placed somewhere else while the handsearches for blocks, MH-1 will remember the new position of thebox and continue to work with it as soon as it has realized thechange in the situation, that is, has bumped into the box whilelooking for blocks. This will be done automatically, without any
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10“Hand–Eye” Researchneed to mention it in the specific program for this block-and-box
performance.

Actually, MH-1 was not the first computer-guided hand, although it was
the first to employ touch sensors to guide its motion. One was developed andpatented in 1954 by George Devol, an American engineer. Based on thisinvention, he and another engineer, Joseph F. Engelberger, founded
Unimation, Inc. Soon after, they installed a prototype of their first industrial
robot, called a “Unimate,” in the General Motors Corporation Ternstedt
Division plant near Trenton, New Jersey.
Back at MIT, Ph.D. students Patrick Winston (later an MIT professor
and director of its AI Laboratory), Thomas O. Binford (later a Stanford
professor), Berthold K. P. Horn (later an MIT professor), and Eugene Freuder(later a University of New Hampshire professor) developed a system that used
an AMF Versatran robot arm to “copy” a configuration of blocks. The sceneconsisting of the blocks was first scanned, and lines were extracted from theimage using a “line-finder,” which was under development by Binford andHorn.2 Using these lines, objects in the image were identified, and a plan wasmade for the robot arm to disassemble the blocks in the scene. The robot arm
then carried out this plan and reassembled the blocks in their original
configuration. The system was demonstrated in December 1970 for various
configurations of blocks. An example block configuration successfully handled
by their “copy demo” is shown in Fig. 10.1. (A film, called Eye of the Robot,
showing the copy demo in action is available at
http://projects.csail.mit.edu/films/aifilms/digitalFilms/9mpeg/88-eye.mpg.)
The system depended on precise illumination and carefully constructed
blocks. Attempts to extend the range of computer vision to less constrained
scenes led to further concentration at MIT and elsewhere on the early stagesof vision processing. I’ll describe some of the ensuing work on these problems
in more detail later.3

10.2

At Stanford

Meanwhile at John McCarthy’s SAIL, a team led by Professor Jerome
Feldman (1938– ) pursued work on hand–eye projects using the PDP-1 andlater the PDP-6 and PDP-10 computers.4 McCarthy later told me that he got
interested in robots because of his interest in computer vision. He was notvery excited about the work in pattern recognition – it was “discrimination”
rather than “description.” “If you want to pick something up, you have todescribe it not merely recognize it.”5
In 1966 SAIL had acquired a Rancho Los Amigos Hospitalelectromechanical prosthetic arm. By the spring of 1967, a hand–eye system
was developed by Karl Pingle, Jonathan Singer, and Bill Wichman that could
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10.2

At Stanford

Figure 10.1: A block arrangement for the MIT copy demo. (Used with permission of Berthold Horn.)
use a TV camera and primitive vision routines to locate blocks scattered on atable. Using the information thus obtained, it could control the arm to sortthe blocks.6 According to the authors,
One section of the system scans the TV image to find the outeredge of an object, then traces around the outside edges of theobject using a gradient operator [an edge detector] to find thelocation and direction of the edge. Curve fitting routines fit
straight lines to a list of points found on the edges and calculate
the position of the corners. . . . A second section of the system isdevoted to control of the arm. . . . The sections which control it
consist of a solution program which calculates the angular position
required at each actuator and a servo program which drives thearm to the desired positions. . .
Les Earnest wrote that this was “the first robotics visual feedback
system,” although “only the outer edges of the blocks were observed, [and] the191
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10“Hand–Eye” Researchhand had to be removed from view when visual checking was done. . . .”7
Several versions of this block-sorting and stacking system were
demonstrated. In one, the system located colored blocks on a table and placed
them in separate stacks of red and blue blocks.8
By 1971, a vision-guided block stacking system solved the “instantinsanity” puzzle.9 In that puzzle, four cubes, each face of which has one offour different colors, must be arranged in a tower such that each side of thetower shows four different colors. The system, running on SAIL’s PDP-10
computer, used a TV camera equipped with a turret of four lenses and a color
wheel to locate four cubes on a table top. The arm picked up and turned eachcube to expose all faces to the camera. Then, knowing the color of each faceand having found a solution to the puzzle, the arm stacked the cubes in a
tower exhibiting the solution. [A silent, 16-mm color six-minute film, titledInstant Insanity, was made by Richard Paul and Karl Pingle in August 1971and shown at the second International Joint Conference on Artificial
Intelligence (IJCAI) in London. The film can be seen at
http://www.youtube.com/watch?v=O1oJzUSITeY. ]Dabblal Rajagopal “Raj” Reddy (1937– ; Fig. 10.2) was the first Ph.D.
student of Stanford’s new Department of Computer Science. His thesis
research was on speech recognition. After obtaining his Ph.D. in 1966, Reddy
joined Stanford’s faculty and continued research on speech recognition at
SAIL. While there he participated in a project to control a hand–eye system
by voice commands.10 As stated in a project review, “Commands ascomplicated as ‘Pick up the small block in the lower lefthand corner,’ arerecognized and the tasks are carried out by the computer controlled arm.”(The system was demonstrated in a 1969 fifteen-minute, 16-mm color, soundfilm showing some of Reddy’s results on speech recognition. It is titled Hear
Here and was produced by Raj Reddy, Dave Espar, and Art Eisensen. The
film is available at http://www.archive.org/details/sailfilm hear.) In 1969
Reddy moved to CMU where he pursued research in speech recognition andlater became Dean of CMU’s School of Computer Science.
In the early 1970s, the Stanford team used a vision system and a newelectromechanical hand designed by mechanical engineering student Vic
Scheinman11 to assemble a model “T” Ford racing water pump.12 An
industrial-style setup was used with tools in fixed places, screws in a feeder,
and the pump body and cover on a pallet. A diagram of the workspace isshown in Fig. 10.3.
The hand–eye system executed the following complex set of steps thatwas computed previously:
locate the pump base, move it into standard position, determine
the final grasping position by touch, place the pump base in its
standard position, insert two pins to guide the alignment of thegasket and cover, put on the gasket, visually check the position of
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10.4

In JapanFigure 10.2: Raj Reddy. (Photograph courtesy of Raj Reddy.)
the gasket, locate the cover by touch, put on the cover over theguide pins, pick up a hex head power screw driver, pick up a screwfrom the feeder, screw in the first two screws, remove the aligningpins, screw in the last four screws, and finally check the force
required to turn the rotor.
A film of the water pump assembly can be seen at
http://www.archive.org/details/sailfilm pump. It is also available at
http://www.saildart.org/films/, along with several other Stanford AI Lab
films.

10.3

In Japan

Hand–eye work was also being pursued at Hitachi’s Central ResearchLaboratory in Tokyo. There, Masakazu Ejiri and colleagues developed a robot
system called HIVIP consisting of three subsystems called EYE, BRAIN, and
HAND. (See Fig. 10.4.) One of EYE’s two television cameras looked at a plandrawing depicting an assembly of blocks. The other camera looked at someblocks on a table. Then, BRAIN figured out how to pick up and assemble theblocks as specified in the drawing, and HAND did the assembly.13

10.4

Edinburgh’s “FREDDY”

During the late 1960s and into the 1970s, researchers under the direction ofProfessor Donald Michie in the Department of Machine Intelligence andPerception at the University of Edinburgh developed robot systems generally
193
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10“Hand–Eye” Research

Figure 10.3: Diagram of a water pump assembly workspace. (Illustration usedwith permission of Robert Bolles.)called “FREDDY.”14 The best known of these was the hand–eye system
FREDDY II, which had a large robot arm and two TV cameras suspended
over a moving table. Even though the arm did not move relative to the room,
it did relative to its “world,” the table. The setup is shown in Fig. 10.5.
A demonstration task for FREDDY II was to construct complete
assemblies, such as a toy car or boat, from a kit of parts dumped onto thetable. The aim was to develop AI techniques that could provide the basis forbetter industrial assembly robots, that is, robots that were more versatile,
more reliable, and more easily programmed than those in operation at thattime.
At the beginning, the component parts were in an unorganized jumble.
FREDDY had to find and identify them and then lay them out neatly. Once it
had found all the parts needed, FREDDY could then perform the assembly
sequence, using a small workstation with a vice.

194
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10.4

Edinburgh’s “FREDDY”

Figure 10.4: Hitachi’s HIVIP robotic assembly system.
Isolated parts were recognized from features of their outline (corners,
curves, etc.), their holes, and their general properties. These were taught to
FREDDY by showing it different views of each of the parts in a prior training
phase.
To deal with heaps of parts, FREDDY applied several tactics: It could try
to find something protruding from the heap, which it could grasp and pull out;
it could attempt to lift something (unknown) off the top; or it could simply
plough the hand through the heap to try to split it into two smaller ones.
Constructing the assembly was performed by following a sequence of
instructions that had been programmed interactively during the training
phase. Some instructions were simple movements, but others were much moresophisticated and used the force sensors in the hand. For example, in a“constrained move,” the hand would slide the part it held along a surface until
it hit resistance; in “hole fitting,” the hand would fit one part (such as an axle)
into a hole in another (such as a wheel) by feel, as humans do.
FREDDY could assemble both the car and the boat when the two kits
were mixed together and dumped on the table. It took about four hours to doso, primarily because of the limited power of FREDDY’s two computers.15
The main computer was an Elliot 4130 with 64k 24-bit words (later upgradedto 128k) and with a clock speed of 0.5 MHz. It was programmed in the POP-2
195
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10“Hand–Eye” Research

Figure 10.5: FREDDY II, the University of Edinburgh robot. (Photographcourtesy of University of Edinburgh.)
language. The robot motors and cameras were controlled by a Honeywell
H316, with 4k 16-bit words (later upgraded to 8k words, at a cost of about$8,000 for the additional 8k bytes!).
Harry Barrow (1943– ), a key person involved in the work, later gave thisaccount of FREDDY’s operation:16[A]cquiring an image from the TV camera took quite a few seconds
and processing took even longer, and in a single run FREDDY
took between 100 and 150 pictures! It took a picture every time it
picked up an object to check it has successfully lifted it and not
dropped it, and it took a picture every time it put an object down
to verify the space was empty. It also scanned the entire world(which required multiple pictures) several times to make a map,
196
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10.4

NOTES
and it looked at each object from two different cameras to do somestereo-style estimation of position and size. In fact, the system
made the most intensive use of image data of any robot system in
the world. The Stanford and MIT systems only took a very small
number of pictures to perform their tasks, and relied heavily on
dead reckoning and things not going wrong. We, on the other
hand, assumed that things were likely to go wrong (objects
dropped, rolling, etc.) and made our system highly robust. I really
believe that in many ways it was probably the most advanced
hand–eye system in existence at the time.

FREDDY is now on permanent exhibition in the Royal Scottish Museumin Edinburgh, with a continuous-loop movie of FREDDY assembling themixed model car and boat kits.
Hand–eye research at Edinburgh was suspended during the mid-1970s inpart owing to an unfavorable assessment of its prospects in a study
commissioned by the British Science Research Council. (I’ll have more to say
about that assessment later.)

Notes
1. Heinrich A. Ernst, “MH-1, A Computer-Operated Mechanical Hand,” Sc.D. thesis,
Massachusetts Institute of Technology, Department of Electrical Engineering, 1962.
Available online at
http://dspace.mit.edu/bitstream/handle/1721.1/15735/09275630.pdf?sequence=1. [189]2. Reported in Berthold K. P. Horn, “The Binford–Horn Line Finder,” MIT AI Memo 285,
MIT, July 1971 (revised December 1973). Available online at
http://people.csail.mit.edu/bkph/AIM/AIM-285-OPT.pdf. [190]3. Patrick Winston gives a nice description of the MIT programs just mentioned (including
the copy demo and Winston’s own thesis work on learning structural descriptions of object
configurations) in Patrick H. Winston, “The MIT Robot,” Machine Intelligence 7, Bernard
Meltzer and Donald Michie (eds.), pp. 431–463, New York: John Wiley and Sons, 1972.
[190]
4. For a brief review, see Lester Earnest (ed.), “Final Report: The First Ten Years ofArtificial Intelligence Research at Stanford,” Stanford Artificial Intelligence Laboratory
Memo AIM-228 and Stanford Computer Science Department Report No. STAN-CS-74-409,
July 1973. (Available online at
http://www-db.stanford.edu/pub/cstr/reports/cs/tr/74/409/CS-TR-74-409.pdf.) For moredetails see Jerome A. Feldman et al., “The Stanford Hand–Eye Project,” Proceedings of the
IJCAI, pp. 521–526, Washington, DC, 1969, and Jerome A. Feldman et al., “The Stanford
Hand-Eye Project – Recent Results,” presented at IFIP Congress, Stockholm, 1974. [190]
5. John McCarthy, private communication, August 11, 2007. [190]
6. The system is described in Karl K. Pingle, Jonathan A. Singer, and William M.
Wichman, “Computer Control of a Mechanical Arm through Visual Input,” Proceedings ofthe IFIP Congress (2), pp. 1563–1569, 1968. The vision part of the system is described in
William Wichman, “Use of Optical Feedback in the Computer Control of an Arm,” Stanford

197
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10

NOTES

Electrical Engineering Department Engineers thesis, August 1967 (and also appears as
Stanford Artificial Intelligence Memo AIM-56, 1967.) [191]7. Lester Earnest, op. cit.. [192]
8. Butterfinger, an 8-minute, 16-mm color film showing a version of this sorting system inoperation was produced and directed by Gary Feldman in 1968. The film is available at
http://projects.csail.mit.edu/films/aifilms/digitalFilms/1mp4/09-robot.mp4. [192]
9. The system is described in Jerome Feldman et al., “The Use of Vision and Manipulation
to Solve the ‘Instant Insanity’ Puzzle,” Proceedings of the IJCAI, pp. 359–364, London:
British Computer Society, September 1971. [192]
10. The system is described in Les Earnest et al., “A Computer with Hands, Eyes, andEars,” Proceedings of the 1968 Fall Joint Computer Conference, Washington, DC:
Thompson, 1968. [192]
11. Victor D. Scheinman, “Design of a Computer Manipulator,” Stanford AI Memo AIM-92,
June 1, 1969. [192]
12. This task is described in Robert Bolles and Richard Paul, “The Use of Sensory Feedback
in a Programmable Assembly System,” Stanford AI Laboratory Memo AIM-220, Stanford
Computer Science Department Report STAN-CS-396, October 1973, which is available
online at ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/73/396/CS-TR-73-396.pdf.
[192]
13. See Masakazu Ejiri et al., “An Intelligent Robot with Cognition and Decision-Making
Ability,” Proceedings of the IJCAI, pp. 350–358, London: British Computer Society,
September 1971, and Masakazu Ejiri et al., “A Prototype Intelligent Robot That Assembles
Objects from Plan Drawings,” IEEE Transactions on Computers, Vol. 21, No. 2, pp.
161–170, February 1972. [193]
14. This is an abbreviation, according to Donald Michie, of Frederick, an acronym of
Friendly Robot for Education, Discussion and Entertainment, the Retrieval of Information,and the Collation of Knowledge. [194]15. http://www.aiai.ed.ac.uk/project/freddy/. The key reference is A. P. Ambler, H. G.
Barrow, C. M. Brown, R. M. Burstall, and R. J. Popplestone, “A Versatile
Computer-Controlled Assembly System,” Artificial Intelligence, Vol. 6, pp. 129–156, 1975.
[195]
16. E-mail note from Harry Barrow of January 3, 2009. [196]198
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11.1

Chapter 11

Knowledge Representation
and Reasoning
For a system to be intelligent, it must have knowledge about its world and themeans to draw conclusions from, or at least act on, that knowledge. Humans
and machines alike therefore must have ways to represent this needed
knowledge in internal structures, whether encoded in protein or silicon.
Cognitive scientists and AI researchers distinguish between two main ways inwhich knowledge is represented: procedural and declarative. In animals, theknowledge needed to perform a skilled action, such as hitting a tennis ball, iscalled procedural because it is encoded directly in the neural circuits thatcoordinate and control that specific action. Analogously, automatic landing
systems in aircraft contain within their control programs procedural knowledge
about flight paths, landing speeds, aircraft dynamics, and so on. In contrast,
when we respond to a question, such as “How old are you?,” we answer with adeclarative sentence, such as “I am twenty-four years old.” Any knowledge thatis most naturally represented by a declarative sentence is called declarative.
In AI research (and in computer science generally), procedural knowledge
is represented directly in the programs that use that knowledge, whereas
declarative knowledge is represented in symbolic structures that are
more-or-less separate from the many different programs that might use theinformation in those structures. Examples of declarative-knowledge symbol
structures are those that encode logical statements (such as those McCarthy
advocated for representing world knowledge) and those that encode semantic
networks (such as those of Raphael or Quillian). Typically, procedural
representations, specialized as they are to particular tasks, are more efficient(when performing those tasks), whereas declarative ones, which can be used bya variety of different programs, are more generally useful. In this chapter, I’ll
describe some of the ideas put forward during this period for reasoning withand for representing declarative knowledge.
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11

Knowledge Representation and Reasoning

11.1

Deductions in Symbolic Logic

Aristotle got things started in logic with his analysis of syllogisms. In thenineteenth century, George Boole developed the foundations of propositional
logic, and Gottlob Frege improved the expressive power of logic by proposing alanguage that could include internal components (called “terms”) as part ofpropositions. Later developments by various logicians gave us what we call
today the predicate calculus – the very language in which McCarthy proposed
to represent the knowledge needed by an intelligent system.
Here is an instance of one of Aristotle’s syllogisms, stated in the languageof the predicate calculus:
1. (∀x)[Man(x) ⊃ Mortal(x)](The expression “(∀x)” is a way of writing “for all x”; and theexpression “⊃” is a way of writing “implies that.” “Man(x)” is a
way of writing “x is a man”; and “Mortal(x)” is a way of writing“x is mortal.” Thus, the entire expression is a way of writing “forall x, x is a man implies that x is mortal” or, equivalently, “all men
are mortal.”)2. Man(Socrates)(Socrates is a man.)
3. Therefore, Mortal(Socrates)(Socrates is mortal.)Statement 3, following “Therefore,” is an example of a deduction in logic.
McCarthy proposed that the knowledge that an intelligent agent might need ina specific situation could be deduced from the general knowledge given to it
earlier. Thus, for McCarthy-style AI, not only do we need a language (perhaps
that of the predicate calculus) but a way to make the necessary deductions
from statements in the language.
Logicians have worked out a variety of deduction methods based on what
they call “rules of inference.” For example, one important inference rule iscalled modus ponens (Latin for “mode that affirms”). It states that if we havethe two logical statements P and P ⊃ Q, then we are justified in deducing thestatement Q.
By the 1960s programs had been written that could use inference rules toprove theorems in the predicate calculus. Chief among these were those ofPaul Gilmore at IBM,1 Hao Wang at IBM,2 and Dag Prawitz,3 now at
Stockholm University. Although their programs could prove simple theorems,
proving more complex ones would have required too much search.4
A Harvard Ph.D. student, Fisher Black (1938–1995), later a co-inventor of
the Black–Scholes equation for pricing options,5 had done early work
200
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11.1

Deductions in Symbolic Logic

implementing some of McCarthy’s ideas.6 But it was a Stanford Ph.D. student
and SRI researcher, C. Cordell Green, who programmed a system, QA3, that
more fully realized McCarthy’s recommendation. Although it was not difficult
to represent world knowledge as logical statements, what was lacking at thetime of Black’s work was an efficient mechanical method to deduce conclusions
from these statements. Green was able to employ a new method for efficientreasoning developed by John Alan Robinson.
During the early 1960s, the English (and American) mathematician andlogician John Alan Robinson (1930– ) developed a deduction method
particularly well suited to computer implementation. It was based on an
inference rule he called “resolution.”7 Although a full description of resolution
would involve too much technical detail, it is a rule (as modus ponens is)whose application produces a new statement from two other statements. For
example, resolution applied to the two statements ¬P ∨ Q and P produces Q.
(The symbol “¬” is a way of writing “not,” and the symbol “∨” is a way ofwriting “or.”). Resolution can be thought of as canceling out the P and the ¬P
in the two statements. (Resolution is a kind of generalized modus ponens as
can be seen from the fact that ¬P ∨ Q is logically equivalent to P ⊃ Q.) This
example was particularly simple because the statements had no internal terms.
Robinson’s key contribution was to show how resolution could be applied togeneral expressions in the predicate calculus, expressions such as ¬P(x) ∨ Q(x)with internal terms.
The advantage of resolution is that it can be readily implemented inprograms to make deductions from a set of logical statements. To do so, thestatements must first be converted to a special form consisting of what
logicians call “clauses.” (Loosely speaking, a clause is a formula that uses only∨’s and ¬’s.) Any logical statement can be converted to clause form (althoughsome, such as John McCarthy, complain that conversion might eliminate clues
about how statements might best be used in logical deductions).
The first use of resolution was in computer programs to prove
mathematical theorems. (Technically, a “theorem” is any logical statement
obtained by successively applying a rule of inference, such as resolution, tomembers of a base set of logical statements, called “axioms,” and tostatements deduced from the axioms.) Groups at Argonne National
Laboratories (under Lawrence Wos), at the University of Texas at Austin(under Woody Bledsoe), and at the University of Edinburgh (under Bernard
Meltzer) soon began work developing theorem-proving programs based on
resolution. These programs were able to prove theorems that had previously
been proved “by hand” and even some new, never-before-proved,mathematical theorems.8 One of these latter concerned a conjecture by
Herbert Robbins that a Robbins algebra was Boolean. The conjecture wasproved in 1996 by William McCune, using an automated theorem prover.9
Our concern here, though, is with using deduction methods to automate
the reasoning needed by intelligent systems. Around 1968, Green (aided by
201
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11

Knowledge Representation and Reasoning

another Stanford student, Robert Yates) programmed, in LISP, a
resolution-based deduction system called QA3, which ran on SRI’s time-shared
SDS 940 computer. (QA1, Green’s first effort, guided by Bertram Raphael at
SRI, was an attempt to improve on Raphael’s earlier SIR system. QA2 was
Green’s first system based on resolution, and QA3 was a more sophisticatedand practical descendant.) “QA” stood for “question answering,” one of the
motivating applications.
I’ll present a short illustrative example of QA3’s question-answering
ability taken from Green’s Stanford Ph.D. thesis.10 First, two statements are
given to the system, namely,
1. ROBOT(Rob)(Rob is a robot.)
2. (∀x)[MACHINE(x) ⊃ ¬ANIMAL(x)](x is a machine implies that it is not an animal.)
The system is then asked “Is everything an animal?” by having it attempt todeduce the statement
3. (∀x)ANIMAL(x)QA3 not only answers “NO,” finding that such a deduction is impossible, but it

also gives a “counterexample” as an answer to the question:
4. x = Rob(This indicates that ¬ANIMAL(Rob) contradicts what was to be
deduced.)The use of resolution, like that of any inference rule, to deduce some specificconclusion from a large body of logical statements involves the need to decide
to which two statements, among the many possibilities, the rule should beapplied. Then a similar decision must be made again and again until, one
hopes, finally the desired conclusion is obtained. So just as with programs forplaying checkers, solving puzzles, and proving geometry theorems, deduction
programs are faced with the need to try many possibilities in their search for asolution. As with those other programs, various heuristic search methods havebeen developed for deduction programs.

11.2

The Situation Calculus

Green realized that “question answering” was quite a broad topic. One could
ask questions about almost anything. For example, one could ask “What is a
202
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11.3

Logic Programming

program for rearranging a list of numbers so that they are in increasing
numerical order?” Or one could ask, “What is the sequence of steps a robot
should take to assemble a tower of toy blocks?” The key to applying QA3 to
answer questions of this sort lay in using McCarthy’s “situation calculus.”
McCarthy proposed a version of logic he called the “situation calculus” inwhich one could write logical statements that explicitly named the situation inwhich something or other was true. For example, one toy block may be on topof another in one situation but not in another. Green developed a version of
McCarthy’s logic in which the situation, in which something was true,
appeared as one of the terms in an expression stating that something was true.
For example, to say that block A is on top of block B in some situation S(allowing for the fact that this might not be the case in other situations),
Green would writeOn(A, B, S),
to say that block A is blue in all situations, Green would write(∀s)Blue(A, s),and to say that there exists some situation in which block A is on block B,
Green would write(∃s)On(A, B, s).
Here “(∃s)” is a way of writing “there exists some s such that . . . ”
Not only was QA3 able to deduce statements, but when it deduced aso-called existential statement (such as the one just mentioned), it was able tocompute an instance of what was alleged to “exist.” Thus, when it deduced
the statement (∃s)On(A, B, s), it also computed for which situation the
deduction was valid. Green devised a way in which this value could be
expressed in terms of a list of actions for a robot that would change some
initial situation into the situation for which the deduced statement was true.
Thus, for example, QA3 could be used to plan courses of action for a robot.
Later, we’ll see how it was used for this purpose.

11.3

Logic Programming

In the same way that QA3 could be used to make robot plans, it could also
construct simple computer programs. In his 1969 paper, Green wrote
The formalization given here [can] be used to precisely state andsolve the problem of automatic generation of programs, including
recursive programs, along with concurrent generation of proofs of
the correctness of these programs. Thus any programs
automatically written by this method have no errors.

203
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Knowledge Representation and Reasoning

Figure 11.1: Robert Kowalski (left) and Alain Colmerauer (right). (Photographs
courtesy of Robert Kowalski and of Alain Colmerauer.)
Green’s work on automatic programming was the first attempt to write
programs using logical statements. Around this time, Robert A. Kowalski(1941– ; Fig. 11.1), an American who had just earned a Ph.D. at the
University of Edinburgh, and Donald Kuehner developed a more efficientversion of Robinson’s resolution method, which they called “SL-resolution.”11
In the summer of 1972, Kowalski visited Alain Colmerauer (1941– ; Fig. 11.1),
the head of Groupe d’Intelligence Artificielle (GIA), Centre National de laRecherche Scientifique and Université II of Aix-Marseille in Marseille.
Kowalski wrote “It was during that second visit that logic programming, as we
commonly understand it, was born.”12Colmerauer and his Ph.D. student, Philippe Roussel, were the ones whodeveloped, in 1972, the new programming language, PROLOG. (Roussel chosethe name as an abbreviation for “PROgrammation en LOGique.”) In PROLOG,
programs consist of an ordered sequence of logical statements. The exact orderin which these statements are written, along with some other constructs, is the
key to efficient program execution. PROLOG uses an ordering based on theordering of deductions in SL-resolution. Kowalski, Colmerauer, and Roussel all
share credit for PROLOG, but Kowalski admits “. . . it is probably fair to saythat my own contributions were mainly philosophical and Alain’s were morepractical.”13
The PROLOG language gradually grew in importance to rival LISP,
although it is used mainly by AI people outside of the United States. Some
American researchers, especially those at MIT, argued against PROLOG (andother resolution-based deduction systems), claiming (with some justification)
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11.4

Semantic Networks

that computation based on deduction was not efficient. They advocatedcomputation controlled by embedding knowledge about the problem being
solved and how best to solve it directly into programs to reduce search. This
“procedural embedding of knowledge” was a feature of the PLANNER
languages developed by Carl Hewitt and colleagues at MIT. (Hewitt coined
the phrase “procedural embedding of knowledge” in a 1971 paper.)14

11.4

Semantic Networks

Semantic networks were (and still are) another important format for
representing declarative knowledge. I have already mentioned their use by
Ross Quillian as a model of human long-term memory. In the 1970s, Stanford
cognitive psychologist Gordon Bower (1932– ) and his student John Anderson(1947– ) presented a network-based theory of human memory in their book
Human Associative Memory.15 According to a biographical sketch ofAnderson, the book “immediately attracted the attention of everyone thenworking in the field. The book played a major role in establishingpropositional semantic networks as the basis for representation in memory andspreading activation through the links in such networks as the basis for
retrieval of information from memory.”16
The theory was partially implemented in a computer simulation called
HAM (an acronym for Human Associative Memory). HAM could parse simple

propositional sentences and store them in a semantic network structure. Using
its accumulated memory, HAM could answer simple questions.
Several other network-based representations were explored during the late1960s and early 1970s. Robert F. Simmons, after moving from SDC to the
University of Texas in Austin, began using semantic networks as acomputational linguistic theory of structures and processing operations
required for computer understanding of natural language. He wrote “Semantic
nets are simple – even elegant – structures for representing aspects of meaningof English strings in a convenient computational form that supports usefullanguage-processing operations on computers.”17
In 1971, Stuart C. Shapiro (1944– ), then at the University of Wisconsin
in Madison, introduced a network structure called MENS (MEmory Net
Structure) for storing semantic information.18 An auxiliary system called
MENTAL (MEmory Net That Answers and Learns) interacted with a user andwith MEMS. MENTAL aided MEMS in deducing new information from that
already stored. Shapiro envisioned that MENTAL would be able to answer
users’ questions using information stored in MEMS.
Shapiro later moved to the State University of New York at Buffalo where
he and colleagues are continuing to develop a series of systems called SNePS(Semantic NEtwork Processing System).19 SNePS combines features of logical205
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11

Knowledge Representation and Reasoning

representations with those of network representations and has been used fornatural language understanding and generation and other applications.20
In his Ph.D. research in linguistics at the University of Texas at Austin,
Roger C. Schank (1946– ; Fig. 11.2) began developing what he called“conceptual dependency representations for natural language sentences.”21
Subsequently, as a Professor at Stanford and at Yale, he and colleagues
continued to develop these ideas. The basis of Schank’s work was his belief
that people transform natural language sentences into “conceptual structures”
that are independent of the particular language in which the sentences wereoriginally expressed. These conceptual structures, he claimed, were how the
information in sentences is understood and remembered. So, for example,
when one translates a sentence from one language into another, one first
represents its information content as a conceptual structure and then uses thatstructure to reason about what was said or to regenerate the information as asentence in another language. As he put it in one of his papers, “. . . any twoutterances that can be said to mean the same thing, whether they are in thesame or different languages, should be characterized in only one way by theconceptual structures.”22

Figure 11.2: Roger Schank. (Photograph courtesy of Roger Schank.)
The notation Schank used for his conceptual structures (sometimes called“conceptual dependency graphs”) evolved somewhat during the 1970s.23 As
an example, Fig. 11.3, taken from one of his papers, shows how he would
represent the sentence “John threw the pencil to Sam.” This structure uses
three of the “primitive actions” Schank has defined for these representations.
These are ATRANS, which means a transfer of possession; PTRANS, which meansa transfer of physical location; and PROPEL, which means an application offorce to an object. Schank defined several other primitive actions to represent
206
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11.5

Scripts and Frames

movement, attending to, speaking, transferring of ideas, and so on.
An expanded literal reading of what this structure represents would be“John applied physical force to a pencil, which caused it to go through the airfrom John’s location to Sam’s location, which caused Sam to possess it” orsomething like that. Schank, like many others who are interested in meaning
representation languages, notes that these representations can be used directly
to perform deductions and answer questions. For example, answers toquestions such as “How did Sam get the pencil?” and “Who owned the pencil
after John threw it?” are easily extracted.
Although network structures are illustrated graphically in papers about
them, they were encoded using LISP for computer processing.

Figure 11.3: Conceptual structure for “John threw the pencil to Sam.” (FromRoger C. Schank, “Identification of Conceptualizations Underlying NaturalLangauge,” in Roger Schank and Kenneth Colby (eds.), Computer Models of
Thought and Language, p. 226, San Francisco: W. H. Freeman and Co., 1973.)

11.5

Scripts and Frames

Graphical knowledge representations, such as semantic networks andconceptual structures, connect related entities together in groups. Suchgroupings are efficient computationally because things that are related often
participate in the same chain of reasoning. When accessing one such entity it
is easy to access close-by ones also. Roger Schank and Robert Abelson
expanded on this idea by introducing the concept of “scripts.”24 A script is a
way of representing what they call “specific knowledge,” that is, detailed
knowledge about a situation or event that “we have been through manytimes.” They contrast specific knowledge with “general knowledge,” the latterof which is the large body of background or commonsense knowledge that isuseful in many situations.
Their “restaurant” script (“Coffee Shop version”) became their most207
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11

Knowledge Representation and Reasoning

famous illustrative example. The script consists of four “scenes,” namely,Entering, Ordering, Eating, and Exiting. Its “Props” are Tables, Menu,
F-Food, Check, and Money. Its “Roles” are S-Customer, W-Waiter, C-Cook,
M-Cashier, and O-Owner. Its “Entry conditions” are S is hungry and S has
money. Its “Results” are S has less money, O has more money, S is not
hungry, and S is pleased (optional). Figures 11.4 shows their script for the“Ordering” scene.

Figure 11.4: A scene in the restaurant script. (From Roger C. Schank andRobert P. Abelson, Scripts, Plans, Goals, and Understanding: An Inquiry into
Human Knowledge Structures, p. 43, Hillsdale, NJ: Lawrence Erlbaum Associates, 1977.)
Besides the actions PTRANS (transfer of location) and ATRANS (transfer ofpossession), this script uses two more of their primitive actions, namely,
MTRANS (transfer of information) and MBUILD (creating or combining thoughts).
208
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11.5

Scripts and Frames

CP(S) stands for S’s “conceptual processor” where thought takes place, and DO
stands for a “dummy action” defined by what follows. The lines in the diagramshow possible alternative paths through the script. So, for example, if themenu is already on the table, the script begins at the upper left-hand corner;otherwise it begins at the upper right-hand corner. I believe most of the script
is self-explanatory, but I’ll help out by explaining what goes on in the middle.
S brings the “food list” into its central processor where it is able to mentally
decide (build) a choice of food. S then transfers information to the waiter tocome to the table, which the waiter does. Then, S transfers the information
about his or her choice of food to the waiter. This continues until either thecook tells the waiter that he does not have the food that is ordered or the cookprepares the food. The three other scenes in the restaurant script are similarly
illustrated in Schank and Abelson’s book.25 Several other variations of therestaurant script (for different kinds of restaurants, and so on) are possible.
Scripts help explain some of the reasoning we do automatically when wehear a story. For example, if we hear that John went to a coffee shop andordered lasagne, we can reasonably assume that lasagne was on the menu. If
we later learn that John had to order something else instead, we can assume
that the coffee shop was out of lasagne. Schank and Abelson give anecdotal
evidence that even small children build such scripts and that people must havea great number of them to enable them to navigate through and reason aboutsituations they encounter.
Schank later expanded on scripts and related ideas in another book, inwhich he introduced the idea of “memory organization packets” (MOPS) that
describe situations in a more distributed and dynamic way than scripts do.26
He later “revisited” some of these ideas in a book about their application toeducation, a field to which he has made significant contributions.27
Schank and his claims generated a good deal of controversy among AI
researchers. For example, I remember arguing with him in 1983 in a
restaurant somewhere (while waiting for the menu?) about the comparative
performance of his programs for natural language understanding and that ofour programs at SRI. As I recall, he was eager to make more grandiose claims
about what his programs could do than I was prepared to believe or to claim
about ours. Tufts University philosopher Daniel Dennett is quoted as having
said “I’ve always relished Schank’s role as a gadfly and as a naysayer, aguerrilla in the realm of cognitive science, always asking big questions, always
willing to discard his own earlier efforts and say they were radically incomplete
for interesting reasons. He’s a gadfly and a good one.”28 I think his basic idea
about scripts was prescient. Also, he has produced a great bunch of students.
The “AI Genealogy” Web site29 lists almost four dozen Schank students, manyof whom have gone on to distinguished careers.
Around the time of Schank’s work, Marvin Minsky proposed thatknowledge about situations be represented in structures he called “frames.”30
He mentioned Schank’s ideas (among others) as exemplary of a movement
209
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11

NOTES

away from “trying to represent knowledge as collections of separate, simple
fragments” such as sentences in a logical language. As he defined them,
A frame is a data-structure for representing a stereotyped
situation, like being in a certain kind of living room, or going to achild’s birthday party. Attached to each frame are several kinds ofinformation. Some of this information is about how to use theframe. Some is about what one can expect to happen next. Someis about what to do if these expectations are not confirmed.
...
Collections of related frames are linked together into frame-systems.
The effects of important actions are mirrored by transformations
between the frames of a system. These are used to make certain
kinds of calculations economical, to represent changes of emphasis
and attention, and to account for the effectiveness of “imagery.”
Minsky’s paper described how frame systems could be applied to visionand imagery, linguistic and other kinds of understanding, memory acquisition,
retrieval of knowledge, and control. Although his paper was rich in ideas,
Minsky did not actually implement any frame systems. A couple of years
later, some of his students and former students did implement some framelike
systems. One, called FRL (for Frame Representation Language), was developedby R. Bruce Roberts and Ira P. Goldstein.31 Daniel Bobrow and Terry
Winograd (the latter being one of Papert’s students), implemented a more
ambitious system called KRL (for Knowledge Representation Language).32
Frame systems accommodated a style of reasoning in which details “notspecifically warranted” could be assumed, thus “bypassing “logic,” as Minsky
would have it. This style was already used earlier in Raphael’s SIR system (seep. 134), and researchers advocating the use of logical languages for knowledge
representation would later extend logic in various ways to accommodate thisstyle also. Even so, the last section (titled “Criticism of the Logistic
Approach”) of Minsky’s paper about frames gives many reasons why one might
doubt (along with Minsky) “the feasibility of representing ordinary knowledge
effectively in the form of many small, independently ‘true’ propositions.”

Notes
1. Paul C. Gilmore, “A Proof Method for Quantification Theory: Its Justification andRealization,” IBM Journal of Research and Development, Vol. 4, pp. 28–35, 1960. [200]2. Hao Wang, “Proving Theorems by Pattern Recognition,” Communications of the ACM,
Vol. 4, No. 3, pp. 229–243, 1960, and Hao Wang, “Toward Mechanical Mathematics,” IBM
Journal of Research and Development, Vol. 4, pp. 2–21, 1960. [200]210
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11.5

NOTES

3. D. Prawitz, H. Prawitz, and N. Voghera, “A Mechanical Proof Procedure and Its
Realization in an Electronic Computer,” Journal of the Association for Computing
Machinery, Vol. 7, pp. 102–128, 1960. [200]4. For additional background and history about automated deduction, see Wolfgang Bibel,“Early History and Perspectives of Automated Deduction,” in J. Hertzberg, M. Beetz, and
R. Englert (eds.), Proceedings of the 30th Annual German Conference on Artificial
Intelligence (KI-2007), Lecture Notes on Artificial Intelligence, pp. 2–18, Berlin:
Springer-Verlag, 2007. [200]5. In 1997 Myron Scholes and Robert C. Merton were awarded a Nobel Prize in economics
for their option-pricing work. Black died of cancer in 1995. The Nobel Prize is not given
posthumously; however, in its announcement of the award, the Nobel committee
prominently mentioned Black’s key role. [200]6. Fischer Black, “A Deductive Question-Answering System,” Ph.D. dissertation, Harvard
University, June 1964. Reprinted in Marvin Minsky (ed.), Semantic Information Processing,pp. 354–402, Cambridge, MA: MIT Press, 1968. [201]7. John Alan Robinson, “A Machine-Oriented Logic Based on the Resolution Principle,”
Journal of the ACM, Vol. 12, No. 1, pp. 23–41, 1965. [201]8. For a description of some of this work, see Larry Wos, Ross Overbeek, Ewing Lusk, andJim Boyle, Automated Reasoning: Introduction and Applications, second edition, New York:
McGraw-Hill, 1992. For more recent work, visit Larry Wos’s Web page at
http://www.mcs.anl.gov/∼wos/. [201]9. William McCune, “Solution of the Robbins Problem,” Journal of Automated Reasoning,
Vol. 19, No. 3, pp. 263–276, 1997. [201]
10. Available as an SRI Technical Note: C. Green, “Application of Theorem Proving to
Problem Solving,” Technical Note 4, AI Center, SRI International, 333 Ravenswood Ave,
Menlo Park, CA 94025, March 1969. Online version available at
http://www.ai.sri.com/pubs/files/tn004-green69.pdf. See also C. Green, “Theorem Proving
by Resolution as a Basis for Question-Answering Systems,” in B. Meltzer and D. Michie,
Machine Intelligence 4, pp. 183ff, Edinburgh: Edinburgh University Press, 1969, and C.
Green, “Applications of Theorem Proving to Problem Solving,” reprinted from a 1969 IJCAI
conference article in B. L. Webber and N. J. Nilsson (eds.), Readings in Artificial
Intelligence, pp, 202–222, San Francisco: Morgan Kaufmann, 1981. [202]
11. Robert A. Kowalski and Donald Kuehner, “Linear Resolution with Selection Function,”
Artificial Intelligence, Vol. 2, Nos. 3–4, pp. 227–260, 1971. [204]12. From one of Kowalski’s Web pages: http://www.doc.ic.ac.uk/∼rak/history.html. [204]13. http://www.doc.ic.ac.uk/∼rak/history.html. For Colmerauer and Roussel’s account of
the birth of PROLOG see Alain Colmerauer and Philippe Roussel, “The Birth of PROLOG,
in Thomas J. Bergin and Richard G. Gibson (eds.), Programming Languages, New York:ACM Press, Addison-Wesley, 1996. Available online at
http://alain.colmerauer.free.fr/ArchivesPublications/HistoireProlog/19november92.pdf.
[204]
14. See Carl Hewitt, “Procedural Embedding of Knowledge in PLANNER,” Proceedings of
the Second International Joint Conference on Artificial Intelligence, pp. 167–182, Los Altos,
CA: Morgan Kaufmann Publishing Co., 1971. [205]15. John R. Anderson and Gordon H. Bower, Human Associative Memory, Washington,
DC: Winston and Sons, 1973. [205]16. From the Web site http://rumelhartprize.org/john.htm. [205]
17. Robert F. Simmons, “Semantic Networks: Computation and Use for Understanding
English Sentences,” in Roger Schank and Kenneth Colby (eds.), Computer Models of
Thought and Language, pp. 63–113, San Francisco: W. H. Freeman and Co., 1973. [205]211
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11

NOTES

18. Stuart C. Shapiro, “A Net Structure for Semantic Information Storage, Deduction and
Retrieval,” Proceedings of the Second International Joint Conference on Artificial
Intelligence, pp. 512–523, Los Altos, CA: Morgan Kaufmann Publishing Co., 1971. [205]
19. An early paper is Stuart C. Shapiro, “The SNePS Semantic Network Processing
System,” in Nicholas V. Findler (ed.), Associative Networks: The Representation and Use of
Knowledge by Computers, pp. 179–203, New York: Academic Press, 1979. [205]
20. The SNePS Web page is at http://www.cse.buffalo.edu/sneps/. [206]
21. Roger C. Schank, “A Conceptual Dependency Representation for a Computer-Oriented
Semantics,” Ph.D. thesis, University of Texas at Austin, 1969. Available as Stanford AI
Memo 83 or Computer Science Technical Note 130, Computer Science Department, Stanford
University, Stanford, CA, 1969. [206]
22. Roger C. Schank, “Identification of Conceptualizations Underlying Natural Language,”in Roger Schank and Kenneth Colby (eds.), Computer Models of Thought and Language, pp.
187–247, San Francisco: W. H. Freeman and Co., 1973. [206]23. Interested readers might refer to various of his books and papers – for example, Roger
C. Schank, “Conceptual Dependency: A Theory of Natural Language Understanding”
Cognitive Psychology, Vol. 3, pp. 552–631, 1972, and Roger C. Schank, Conceptual
Information Processing, New York: Elsevier, 1975. [206]
24. Roger C. Schank and Robert P. Abelson, Scripts, Plans, Goals, and Understanding: An
Inquiry into Human Knowledge Structures, Hillsdale, NJ: Lawrence Erlbaum Associates,
1977. [207]25. Ibid. [209]26. Roger C. Schank, Dynamic Memory: A Theory of Reminding and Learning in
Computers and People, Cambridge: Cambridge University Press, 1982. [209]27. Roger C. Schank, Dynamic Memory Revisited, Cambridge: Cambridge University Press,
1999. [209]28. See http://www.edge.org/3rd culture/bios/schank.html. [209]29. See http://aigp.csres.utexas.edu/∼aigp/researcher/show/192. [209]30. Marvin Minsky, “A Framework for Representing Knowledge,” MIT AI Laboratory
Memo 306, June 1974. Reprinted in Patrick Winston (ed.), The Psychology of Computer
Vision, New York: McGraw-Hill, 1975. Available online at
http://web.media.mit.edu/∼minsky/papers/Frames/frames.html. [209]31. R. Bruce Roberts and Ira P. Goldstein, The FRL Primer, Massachusetts Institute ofTechnology AI Laboratory Technical Report AIM-408, July 1977; available online at
ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-408.pdf. [210]32. Daniel G. Bobrow and Terry A. Winograd, “An overview of KRL, a Knowledge
Representation Language,” Report Number CS-TR-76-581, Department of Computer
Science, Stanford University, November 1976. Available online at
ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/76/581/CS-TR-76-581.pdf. Appeared
later as Daniel Bobrow and Terry Winograd, “An Overview of KRL, a Knowledge
Representation Language,” Cognitive Science, Vol. 1, No. 1, pp. 3–46, January 1977. [210]212
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12.1

Chapter 12

Mobile Robots
The hand–eye systems described earlier might be thought of as “robots,” butthey could not move about from their fixed base. Up to this time, very little
work had been done on mobile robots even though they figured prominently inscience fiction. I have already mentioned Grey Walter’s “tortoises,” which were
early versions of autonomous mobile robots. In the early 1960s researchers at
the Johns Hopkins University Applied Physics Laboratory built a mobile robot
they called “The Beast.” (See Fig. 12.1.) Controlled by on-board electronics
and guided by sonar sensors, photocells, and a “wallplate-feeling” arm, it
could wander the white-walled corridors looking for dark-colored power plugs.
Upon finding one, and if its batteries were low, it would plug itself in andrecharge its batteries. The system is described in a book by Hans Moravec.1
Beginning in the mid-1960s, several groups began working on mobile
robots. These included the AI Labs at SRI and at Stanford. I’ll begin with anextended description of the SRI robot project for it provided the stimulus forthe invention and integration of several important AI technologies.

12.1

Shakey, the SRI Robot

In November 1963, Charles Rosen, the leader of neural-network research at
SRI, wrote a memo in which he proposed development of a mobile“automaton” that would combine the pattern-recognition and memory
capabilities of neural networks with higher level AI programs – such as werebeing developed at MIT, Stanford, CMU, and elsewhere. Rosen had
previously attended a summer course at UCLA on LISP given by Bertram
Raphael, who was finishing his Ph.D. (on SIR) at MIT.
Rosen and I and others in his group immediately began thinking about
mobile robots. We also enlisted Marvin Minsky as a consultant to help us.

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Mobile Robots

Figure 12.1: The Johns Hopkins “Beast.” (Photograph courtesy of the Johns
Hopkins University Applied Physics Laboratory.)
Minsky spent two weeks at SRI during August 1964. We made the first of
many trips to the ARPA office (in the Pentagon at that time) to generateinterest in supporting mobile robot research at SRI. We also talked with Ruth
Davis, the director of the Department of Defense Research and Engineering(DDR&E) – the office in charge of all Defense Department research. We wrote

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12.1

Shakey, the SRI Robota proposal in April 1964 to DDR&E for “Research in Intelligent Automata(Phase I)” that would, we claimed, “ultimately lead to the development ofmachines that will perform tasks that are presently considered to require
human intelligence.”2 The proposal, along with several trips and discussions
culminated, in November 1964, in a “work statement” issued by thethen-director of ARPA’s Information Processing Techniques Office, Ivan
Sutherland. The excerpt in Fig. 12.2 describes the goals of the program.3

Figure 12.2: Excerpt from the typescript of the automaton work statement.
In the meantime, Bertram Raphael completed his MIT Ph.D. degree in1964 and took up a position at UC Berkeley for an academic year. In April
1965, he accepted our offer to join SRI to provide our group with needed AI
expertise. After several research proposal drafts and discussions with people in
the relevant offices in the Defense Department (complicated by the fact that
Ivan Sutherland left ARPA during this time), SRI was finally awarded a rather
large (for the time) contract based essentially on Sutherland’s work statement.
The “start-work” date on the project, which was administered for ARPA by
the Rome Air Development Center (RADC) in Rome, New York, was March215
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Mobile Robots

17, 1966. (Coincidentally, just before joining SRI in 1961, I had just finished athree-year stint of duty as an Air Force Lieutenant at RADC working on
statistical signal-processing techniques for radar systems.) Ruth Davis playeda prominent role in getting ARPA and RADC to move forward on getting the
project started. The “knitting together” of several disparate AI technologies
was one of the primary challenges and one of the major contributions of SRI’s
automaton project.4
One of the tasks was the actual construction of a robot vehicle whose
activities would be controlled by a suite of programs. Because of various
engineering idiosyncrasies, the vehicle shook when it came to an abrupt stop.
We soon called it “Shakey,” even though one of the researchers thought thatsobriquet too disrespectful. [Shakey was inducted into the “Robot Hall ofFame” (along with C-3PO among others) in 2004.5 It was also named as thefifth-best robot ever (out of 50) by Wired Magazine in January 2006. Wired’s
numbers 2 and 4 were fictional, “Spirit” and ”Opportunity” (the Mars robots)
were number 3, and “Stanley” (winner of the 2005 DARPA “Grand
Challenge”) was named “the #1 Robot of All Time.” Shakey is now exhibitedat the Computer History Museum in Mountain View, California.]6
Shakey had an on-board television camera for capturing images of its
environment, a laser range finder (triangulating, not time-of-flight) for sensing
its distance from walls and other objects, and cat-whisker-like bump detectors.
Shakey’s environment was a collection of “rooms” connected by doorways but
otherwise separated by low walls that we could conveniently see over but
Shakey could not. Some of the rooms contained large objects, as shown in Fig.
12.3. The size of Shakey can be discerned from inspection of Fig. 12.4.
Most of the programs that we developed to control Shakey were run on a
DEC PDP-10 computer. Between the PDP-10 and the mobile vehicle itself
were a PDP-15 peripheral computer (for handling the lower levelcommunications and commands to on-board hardware) and a two-way radio
and video link. The PDP-10 programs were organized in what we called a“three-layer” hierarchy. Programs in the lowest level drove all of the motors
and captured sensory information. Programs in the intermediate levelsupervised primitive actions, such as moving to a designated position, and also
processed visual images from Shakey’s TV camera. Planning more complex
actions, requiring the execution of a sequence of intermediate-level actions,
was done by programs in the highest level of the hierarchy. The Shakey
project involved the integration of several new inventions in search techniques,in robust control of actions, in planning and learning, and in vision. Many ofthese ideas are widely used today. The next few subsections describe them.

12.1.1A∗ : A New Heuristic Search Method

One of the first problems we considered was how to plan a sequence of “waypoints” that Shakey could use in navigating from place to place. In getting
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Shakey, the SRI Robot

Figure 12.3: Shakey as it existed in November 1968 (with some of its components
labeled). (Photograph courtesy of SRI International.)around a single obstacle lying between its initial position and a goal position,
Shakey should first head toward a point near an occluding boundary of the
obstacle and then head straight for the unobstructed final goal point.
However, the situation becomes more complicated if the environment islittered with several obstacles, and we sought a general solution to this moredifficult problem.
Shakey kept information about the location of obstacles and about its

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Mobile Robots

Figure 12.4: Charles A. Rosen with Shakey. (Photograph courtesy of SRI International.)
own position in a “grid model,” such as the one shown in Fig. 12.5. (To obtain
the required accuracy, grid cells were decomposed into smaller cells near theobjects. I think this was one of the first applications of adaptive cell
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12.1

Shakey, the SRI Robot

decomposition in robot motion planning and is now a commonly usedtechnique.) Consider, for example, the navigation problem in which Shakey isat position R and needs to travel to G (where R and G are indicated by theshaded squares). It can use a computer representation of the grid model toplan a route before beginning its journey – but how? The map shows the
positions of three objects that must be avoided. It is not too difficult tocompute the locations of some candidate way points near the corners of theobjects. (These way points must be sufficiently far from the corners so that
Shakey wouldn’t bump into the objects.) The way points are indicated byshaded stars and labeled “A,” “B,” and so on through “K.” Using techniques
now familiar in computer graphics, it also is not difficult to compute which
way points are reachable using an obstacle-free, straight-line path from anyother way point and from R and G.

Figure 12.5: A navigation problem for Shakey. (Illustration used with permission of SRI International.)219
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Mobile Robots

Looked at in this way, Shakey’s navigation problem is a search problem,
similar to ones I have mentioned earlier. Here is how a search tree can beconstructed and then searched for a shortest path from R to G. First, because
A and F are directly reachable by obstacle-free, straight-line paths from R,
these are set up as direct-descendant “nodes” of R in the search tree. We
continue the process of computing descendant nodes (along obstacle-free,straight-line paths) from each of A and F and so on until G is added to thetree. Then, it is a simple matter to identify the shortest path from R to G.
Several methods for searching trees (and their more general cousins,graphs) were already in use by the mid-1960s. One point in favor of theseknown methods was that they were guaranteed to find shortest paths whenused to solve Shakey’s navigation problems. However, they could becomputationally inefficient for difficult problems. Of course, solving simple
navigation problems (such as the one in the diagram) does not involve muchsearch, so any search method would solve such problems quickly. But we wereinterested in general methods that would work efficiently on larger, moredifficult problems. I was familiar with the heuristic search method proposed
by J. Doran and Donald Michie for solving the eight-piece, sliding-tile puzzle.
They assigned a numerical value to each node in the search tree, based on theestimated difficulty of reaching the goal from that node. The node with thelowest score was the one that was selected next to have its descendants
generated.7
I reasoned that a good “heuristic” estimate for the difficulty of gettingfrom a way point position to the goal (before actually searching further) would
be the “airline distance,” ignoring any intervening obstacles, from that
position to the goal. I suggested that we use that estimate as the score of thecorresponding node in the search tree. Bertram Raphael, who was directing
work on Shakey at that time, observed that a better value for the score would
be the sum of the distance traveled so far from the initial position plus myheuristic estimate of how far the robot had to go.8
Raphael and I described this idea to Peter Hart, who had recently
obtained a Ph.D. from Stanford and joined our group at SRI. Hart recalls9“going home that day, sitting in a particular chair and staring at the wall for
more than an hour, and concluding” that if the estimate of remaining distance(whatever it might be) was never larger than the actual remaining distance,then the use of such an estimate in our new scoring scheme would always finda path having the shortest distance to the goal. (Of course, my heuristic
airline distance satisfied Hart’s more-general condition.) Furthermore, hethought such a procedure would generate search trees no larger than any otherprocedures that were also guaranteed to find shortest paths and that usedheuristic estimates no better than ours.
Together, Hart, Raphael, and I were able to construct proofs for theseclaims, and we named the resulting search process “A∗ .” (The “A” was foralgorithm and the “ ∗ ” denoted its special property of finding shortest paths.
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12.1

Shakey, the SRI Robot

I think Hart and Raphael did most of the heavy lifting in devising the proofs.)
When paths have costs associated with them that depend on more than justdistance, and when such costs (rather than distances) are taken into accountin computing scores, A∗ is guaranteed to find lowest cost paths.10
The inclusion of the estimate of remaining distance (or cost) to the goal
contributes to searching in the general direction of the goal. The inclusion of
the actual distance (or cost) incurred so far ensures that the search process
will not forever be led down promising but perhaps futile paths and will beable to “leak around” obstacles.
A∗ has been extended in many ways – especially by Richard Korf to makeit more practical when computer memory is limited.11 Today, A∗ is used inmany applications including natural language parsing,12 the computation ofdriving directions,13 and interactive computer games.14

12.1.2

Robust Action Execution

The A∗ algorithm was embedded in Shakey’s programs for navigating fromone place to another within a room containing obstacles and for pushing an
object from one place to another. Navigation programs, along with others,
occupied the middle level of the hierarchy of Shakey’s programs. These
intermediate-level programs were all designed to achieve certain goals, such asgetting an object in front of a doorway for example. They were also quite
robust in that they “kept trying” even in the face of unforeseen difficulties.
For example, if an object being pushed happened accidentally to slip off thefront “pushing bar,” the push program noticed this problem (through built-in
contact sensors in the pushing bar) and repositioned Shakey so that it could
reengage the object and continue pushing.
In thinking about how to achieve this robustness, I was inspired both byMiller, Galanter, and Pribram’s TOTE units and by the idea of homeostasis.
(Recall that a TOTE unit for driving in a nail keeps pounding until the nail iscompletely driven in and that homeostatic systems take actions to return themto stability in the face of perceived environmental disturbances.) I wanted the
mid-level programs to seek and execute that action that was both “closest” toachieving their goals and that could actually be executed in the current
situation. If execution of that action produced a situation in which, as
anticipated, an action even closer to achieving the goal could be executed, fine;the mid-level program was at least making progress. If not, or something
unexpected caused a setback, some other action would be executed next to getback on track. Richard Duda and I developed a format, called “Markov
tables,” for writing these intermediate-level programs having this“keep-trying” property.15

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Mobile Robots

12.1.3

STRIPS: A New Planning Method

The mid-level programs could accomplish a number of simple tasks, such asgetting Shakey from one place to another in the same room, pushing objects,
and getting Shakey through a doorway into an adjoining room. However, to go
to some distant room and push an object there into some designated position
would require joining together a sequence of perhaps several of these mid-level
programs. Just as humans sometimes make and then execute plans foraccomplishing their tasks, we wanted Shakey to be able to assemble a plan ofactions and then to execute the plan. The plan would consist of a list of the
programs to be executed.
Information needed for planning was stored in what was called an “axiom
model.” This model contained logical statements in the language of thepredicate calculus (which I talked about earlier.) For example, Shakey’s
location was represented by a statement such as AT(ROBOT, 7,5), the factthat Box1 was pushable was represented by the statement PUSHABLE(BOX1),
and the fact that there was a doorway named D1 between rooms R1 and R2
was represented by the statement JOINSROOMS(D1, R1,R2). The axiom model
had close to two-hundred statements such as these and was the basis of
Shakey’s reasoning and planning abilities.
Our first attempt at constructing plans for Shakey used the QA3
deduction system and the situation calculus. We would ask QA3 to prove(using a version of the axiom model) that there existed a situation in which
Shakey’s goal (for example, being in some distant room) was true. The resultof the deduction (if successful) would name that situation in terms of a list of
mid-level actions to be executed.16
The use of the situation calculus for planning how to assemble mid-level
actions involved using logical statements to describe the effects of these actions
on situations. Not only did we have to describe how a mid-level action changed
certain things about the world, but we also had to state that it left manythings unaffected. For example, when Shakey pushed an object, the position ofthat object in the resulting situation was changed, but the positions of allother objects were not. That most things in Shakey’s world did not change had
to be explicitly represented as logical statements and, worse, reasoned about
by QA3. This difficulty, called the “frame problem,” has been the subject of agreat deal of research in AI, and there have been many attempts to mitigate
it, if not solve it.17 Because of the frame problem, QA3 could be used only forputting together the simplest two- or three-step plans. Any attempt togenerate plans very much longer would exhaust the computer’s memory.
The problem with the situation calculus (as it was used then) was that it
assumed that all things might change unless it was explicitly stated that they
did not change. I reasoned that a better convention would be to assume that
all things remained unchanged unless it was explicitly stated that they didchange. To employ a convention like that, I proposed a different way of222
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12.1

Shakey, the SRI Robot

updating the collection of logical statements describing a situation. The idea
was that certain facts, specifically those that held before executing the action
but might not hold after, should be deleted and certain new facts, namely,
those caused by executing the action, should be added. All other facts (those
not slated for deletion) should simply be copied over into the collection
describing the new situation. Besides describing the effects of an action in thisway, each action description would have a precondition, that is, a statement ofwhat had to be true of a situation to be able to execute the action in thatsituation. (A year or so earlier, Carl Hewitt, a Ph.D. student at MIT, wasdeveloping a robot programming language called PLANNER that hadmechanisms for similar kinds of updates.)18
For example, to describe the effects of the program goto((X1,Y1),(X2,Y2)) for moving Shakey from some position (X1,Y1) to some position(X2,Y2), one should delete the logical statement AT(ROBOT, X1,Y1), add thestatement AT(ROBOT, X2,Y2), and keep all of the other statements. Of course,
to execute goto((X1,Y1), (X2,Y2)), Shakey would already have to be at
position (X1,Y1); that is, the axiom model had to contain the precondition
statement AT(ROBOT,X1,Y1), or at least contain statements from which
AT(ROBOT,X1,Y1) could be proved.
Around this time (1969), Richard Fikes (1942– ) had just completed his
Ph.D. work under Allen Newell at Carnegie and joined our group at SRI.
Fikes’s dissertation explored some new ways to solve problems using
procedures rather than using logic as in QA3. Fikes and I worked together on
designing a planning system that used preconditions, delete lists, and add lists(all expressed as logical statements) to describe actions. Fikes suggested thatin performing a search for a goal-satisfying sequence of actions, the systemshould use the “means–ends” analysis heuristic central to Newell, Shaw, andSimon’s General Problem Solver (GPS). Using means–ends analysis, searchwould begin by identifying those actions whose add lists contained statementsthat helped to establish the goal condition. The preconditions of those actions
would be set up as subgoals, and this backward reasoning process would
continue until a sequence of actions was finally found that transformed theinitial situation into one satisfying the goal.
By 1970 or so, Fikes had finished programming (in LISP) our new planningsystem. We called it STRIPS, an acronym for Stanford Research Institute
Problem Solver.19 After its completion, STRIPS replaced QA3 as Shakey’s
system for generating plans of action. Typical plans consisting of six or so
mid-level actions could be generated on the PDP-10 in around two minutes.
The STRIPS planning system itself has given way to more efficient AI
planners, but many of them still describe actions in terms of what are called“STRIPS operators” (sometimes “STRIPS rules”) consisting of preconditions,delete lists, and add lists.

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Mobile Robots

12.1.4

Learning and Executing Plans

It’s one thing to make a plan and quite another to execute it properly. Also,
we wanted to be able to save the plans already made by STRIPS for possible
future use. We were able to come up with a structure, called a “triangle
table,” for representing plans that was useful not only for executing plans but
also for saving them. (John Munson originally suggested grouping theconditions and effects of robot actions in a triangular table. Around 1970,
Munson, Richard Fikes, Peter Hart, and I developed the triangle tableformalism to represent plans consisting of STRIPS operators.) The triangle
table tabulated the preconditions and effects of each action in the plan so thatit could keep track of whether or not the plan was being executed properly.
Actions in the plans generated by STRIPS had specific values for theirparameters. For example, if some goto action was part of a plan, actual place
coordinates were used to name the place that Shakey was to go from and theplace it was to go to, perhaps goto((3,7),(8,14)). Although we might wantto save a plan that had that specific goto as a component, a more generally
applicable plan would have a goto component with nonspecific parametersthat could be replaced by specific ones depending on the specific goal. That is,
we would want to generalize something like goto((3,7),(8,14)), forexample, to goto((x1,y1),(x2,y2)). One can’t willy-nilly replace constants
by variables, but one must make sure that any such generalizations result in
viable and executable plans for all values of the variables. We were able tocome up with a procedure that produced correct generalizations, and it wasthese generalized plans that were represented in the triangle table.
After a plan was generated, generalized, and represented in the triangle
table, Shakey’s overall executive program, called “PLANEX,” supervised its
execution.20 In the environment in which Shakey operated, plan execution
would sometimes falter, but PLANEX, using the triangle table, could decide
how to get Shakey back on the track toward the original goal. PLANEX gavethe same sort of “keep-trying” robustness to plan execution that the Markov
tables gave to executing mid-level actions.

12.1.5

Shakey’s Vision Routines

Shakey’s environment consisted of the floor it moved about on, the wallsbounding its rooms, doorways between the rooms, and large rectilinear objects
on the floor in some of the rooms. We made every effort to make “seeing” easy
for Shakey. A dark baseboard separated the light-colored floor from thelight-colored walls. The objects were painted various shades of red, whichappeared dark to the vidicon camera and light to the infrared laser range
finder. Even so, visual processing still presented challenging problems.
Rather than attempt complete analyses of visual scenes, our work
concentrated on using vision to acquire specific information that Shakey
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12.1

Shakey, the SRI Robotneeded to perform its tasks. This information included Shakey’s location andthe presence and locations of objects – the sort of information that was
required by the mid-level actions. The visual routines designed to gather that
information were embedded in the programs for performing those actions.
Known properties of Shakey’s environment were exploited in these routines.
Exploiting the fact that the objects, the floor, and the wall containedplanes of rather constant illumination, Claude Brice and Claude Fennema in
our group developed image-processing routines that identified regions of
uniform intensity in an image.21 Because the illumination on a single plane,
say the face of an object, might change gradually over the region, theregion-finding routine first identified rather small regions. These were thenmerged across region boundaries in the image if the intensity change across theboundary was not too great. Eventually, the image would be partitioned into a
number of large regions that did a reasonable job of representing the planes inthe scene. The boundaries of these regions could then be fitted withstraight-line segments.
Another vision routine was able to identify straight-line segments in theimage directly. Richard Duda and Peter Hart developed a method for doing
this based on a modern form of the “Hough transform.”22 After edge-detection
processing had identified the locations and directions of small line segments,
the Hough transform was used to construct those longer lines that were
statistically the most likely, given the small line segments as evidence.
Both region finding and line detection were used in various of the visionroutines for the mid-level actions. One of these routines, called obloc, wasused to refine the location of an object whose location was known only
roughly. The pictures in Fig. 12.6 show a box, how it appears as a TV image
from Shakey’s camera, and two of the stages of obloc’s processing. From theregions corresponding to the box and the floor (and using the fact that Shakey
is on the same floor as the box), straightforward geometric computations could
add the box and its location to Shakey’s models.
Shakey ordinarily kept track of its location by dead-reckoning (counting
wheel revolutions), but this estimate gradually accumulated errors. When
Shakey determined that it should update its location, it used another visionroutine, called picloc. A nearby “landmark,” such as the corner of a room,
was used to update Shakey’s position with respect to the landmark. The
pictures in Fig. 12.7 show how obloc traces out the baseboard and finds theregions corresponding to the walls and the floor. The final picture shows thediscrepancy between Shakey’s predicted location of the corner (based on
Shakey’s estimate of its own location) and the actual location based on
picloc. This discrepancy was used to correct Shakey’s estimate of its position.
Before Shakey began a straight-line motion in a room where the presenceof obstacles might not be known, it used a routine called clearpath todetermine whether its path was clear. This routine checked the image of its

225
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12

Mobile Robots

Figure 12.6: Using vision to locate an object. (From the film Shakey: An
Experiment in Robot Planning and Learning. Used with permission of SRI
International.)
path on the floor (a trapezoidal-shaped region) for changes in brightness thatmight indicate the presence of an obstacle.
In appraising Shakey’s visual performance, it is important to point out
that it was really quite primitive and subject to many errors – even in
Shakey’s specially designed environment. As one report acknowledges,“Regions that we wish to keep distinct – such as two walls meeting at a corner– are frequently merged, and fragments of meaningful regions that should bemerged are too often kept distinct.” Regarding clearpath, for example, thissame report notes that “. . . shadows and reflections can still cause false alarms,
and the only solution to some of these problems is to do more thorough scene
analysis.”23 Nevertheless, vision played an important part in Shakey’s overall
performance, and many of the visual processing techniques developed during
the Shakey project are still used (with subsequent improvements) today.24
226
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12.1

Shakey, the SRI Robot

Figure 12.7: Using vision to update position. (From the film Shakey: An Experiment in Robot Planning and Learning. Used with permission of SRI International.)

227
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12

Mobile Robots

12.1.6

Some Experiments with Shakey

To illustrate Shakey’s planning and plan-execution and learning methods inaction, we set up a task in which Shakey was to push a specified box in frontof a specified doorway in a nonadjacent room. To do so, Shakey had to use
STRIPS to make a plan to travel to that room and then to push the box.
Before beginning its execution of the plan, Shakey saved it in the generalized
form described earlier. In the process of executing the plan, we arranged for
Shakey to encounter an unexpected obstacle. Illustrating its robust plan
execution procedure, Shakey was able to find a different version of thegeneralized plan that would take it on a somewhat different route to the target
room where it could carry on.25
One of the researchers working on the Shakey project was L. Stephen
Coles (1941– ), who had recently obtained a Ph.D. degree under Herb Simon
at Carnegie Mellon University working on natural language processing. Coles
wanted to give Shakey tasks stated in English. He developed a parser andsemantic analysis system that translated simple English commands into logicalstatements for STRIPS. For example, the task of box pushing just mentioned
was posed for Shakey in English as follows:
Use BOX2 to block door DPDPCLK from room RCLK.
(BOX2, DPDPCLK, and RCLK were the names Shakey used to identify thebox, door, and room in question. We were obliging enough to use Shakey’s
names for things when giving it tasks to perform.)
Coles’s program, called ENGROB,26 translated this English command into thefollowing condition to be made true (expressed in the language of thepredicate calculus):
BLOCKED(DPDPCLK, RCLK, BOX2)
This condition was then given to STRIPS to make a plan for achieving it.
Coles was also interested in getting Shakey to solve problems requiring
indirect reasoning. He set up an experiment in which Shakey was to push abox off an elevated platform. To do so, it would have to figure out that it
would need to push a ramp to the platform, roll up the ramp, and then push
the box. This task was given to Shakey in English as “Push the box that is on
the platform onto the floor.” The task was successfully executed and describedin one of the Shakey technical reports.27
The “push-the-box-off-the-platform” task was Coles’s way of showing that
Shakey could solve problems like the “monkey-and-bananas” problem. That
problem, made famous by John McCarthy as an example for deductive
reasoning, involved a monkey, a box, and some bananas hanging out of reach.
The monkey was supposed to be able to reason that to get the bananas, it
228
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12.1

Shakey, the SRI Robot

would have to push the box under the bananas, climb up on the box, and thengrab the bananas.28 McCarthy is said to have heard Karl Lashley at the 1948
Caltech Hixon symposium describe a similar problem for demonstrating
intelligent problem solving by chimpanzees.
One of the persons who was impressed with Shakey was Bill Gates, wholater co-founded Microsoft. He saw the 1972 Shakey film as a junior in highschool and drove down from Seattle to SRI (with Paul Allen, who would be
the other co-founder of Microsoft) to have a look. According to one source, he
was “particularly excited about Shakey moving things around so it could go
up a ramp.”29

12.1.7

Shakey Runs into Funding Troubles

Shakey was the first robot system having the abilities to plan, reason, andlearn; to perceive its environment using vision, range-finding, and touch
sensors; and to monitor the execution of its plans. It was, perhaps, a bit aheadof its time. Much more research (and progress in computer technology
generally) would be needed before practical applications of robots withabilities such as these would be feasible. We mentioned some of the limiting
assumptions that were being made by robot research projects at that time inone of our reports about Shakey:
Typically, the problem environment [for the robot] is a dull sort ofplace in which a single robot is the only agent of change – eventime stands still until the robot moves. The robot itself is easily
confused; it cannot be given a second problem until it finishes thefirst, even though the two problems may be related in some
intimate way. Finally, most robot systems cannot yet generateplans containing explicit conditional statements or loops.
Even though the SRI researchers had grand plans for continuing work on
Shakey, DARPA demurred, and the project ended in 1972. This termination
was unfortunate, because work on planning, vision, learning, and theirintegration in robot systems had achieved a great deal of momentum andenthusiasm among SRI researchers. Furthermore, several new ideas forplanning and visual perception were being investigated. Many of these were
described in detail in a final report for the Shakey project.30
Among these ideas, a particularly important one involved techniques forconstructing plans in a hierarchical fashion. To do so, an overall planconsisting of just “high-level” actions must be composed first. Such a plan canbe found with much less searching than one consisting of all of the lowest level
actions needed. For example, one’s plan for getting to work might involve only
the decision either to take the subway or to drive one’s car. Then, gradually,
the high-level plan must be refined in more and more detail until actions at
229
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12

Mobile Robots

the lowest level (such as which set of car keys should be used) would
eventually be filled in.
A Stanford computer science graduate student working at SRI, Earl
Sacerdoti (1948– ), proposed two novel methods for hierarchical planning.
First (as part of his master’s degree work), he programmed a system he called
ABSTRIPS.31 It consisted of a series of applications of STRIPS – beginning withan easy-to-compose plan that ignored all but the most important operator
preconditions. Subsequent applications of STRIPS, guided by the higher levelplans already produced, would then gradually take the more detailed
preconditions into account. The result was a series of ever-more-detailed plans,culminating in one that could actually be executed.
For his Ph.D. work, Sacerdoti went on to develop a more powerfulhierarchical planning system he called NOAH (for Nets of Action
Hierarchies).32 Unlike ABSTRIPS, whose action operators were all at the samelevel of detail (albeit with preconditions that could be selectively ignored),
NOAH employed action operators at several levels of detail. Each operator
came equipped with specifications for how it could be elaborated by operators
at a lower level of detail. Furthermore, NOAH’s representation of a plan, in aform Sacerdoti called a “procedural network,” allowed indeterminacy about
the order in which plan steps at one level might be carried out. This “delayedcommitment” about ordering permitted the more detailed steps of theelaborations of nonordered plans at one level to be interleaved at the level
below, often with a consequent improvement in overall efficiency.
Sacerdoti was hoping to use his hierarchical planning ideas in the Shakey
project, so he and the rest of us at SRI were quite disappointed that DARPA
was not going to support a follow-on project. (Basic research on robots was
one of the casualties of the DARPA emphasis on applications work that began
in the early 1970s.) However, we were able to talk DARPA into a project that
had obvious military relevance but still allowed us to continue work on
automatic planning, vision, and plan execution. Interestingly, the project waspretty much a continuation of our research work on Shakey but with a humancarrying out the planned tasks instead of a robot. We called it the“computer-based consultant (CBC) project.” I’ll describe it in a subsequent
chapter.
Sacerdoti and the SRI researchers were not alone in recognizing theimportance of hierarchical planning. As part of his Ph.D. work at the
University of Edinburgh, Austin Tate (1951– ) was developing a network-basedplanning system called INTERPLAN.33 In 1975 and 1976, supported by the
British Science Research Council, Tate and colleagues from operations
research produced a hierarchical planner called NONLIN.34 The planner took
its name from the fact that, like NOAH, some of the plan steps were left
unordered until they were elaborated at lower levels of the hierarchy.

230
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12.2

The Stanford Cart

Other planning systems grew out of the NOAH and NONLIN tradition. One
was the interactive plan-generation and plan-execution system SIPE-2
developed by David E. Wilkins at SRI International.35 Another was O-PLAN
developed by Tate and colleagues at the Artificial Intelligence Applications
Institute (AIAI) at the University of Edinburgh.36 These systems have been
widely used, extended, and applied.37

12.2

The Stanford Cart

In the early 1960s, James Adams, a Mechanical Engineering graduate student
at Stanford (and later a Stanford professor), began experimenting with afour-wheeled, mobile cart with a TV camera and a radio control link. Lester
Earnest wrote (in his history of the several projects using this cart) “Amongother things, Adams showed in his dissertation that with a communication
delay corresponding to the round trip to the Moon (about 2 21 seconds) thevehicle could not be reliably controlled if traveling faster than about 0.2 mph(0.3 kph).”38
After Earnest joined the Stanford AI Laboratory, he and Rodney
Schmidt, an Electrical Engineering Ph.D. student, got an upgraded version of
the cart to “follow a high contrast white line [on the road around the Lab]
under controlled lighting conditions at a speed of about 0.8 mph (1.3 kph).”Other AI graduate students also experimented with the cart from time to time
during the early 1970s. A picture of the cart (as it appeared around this time)is shown in Fig. 12.8.
When Hans Moravec came to Stanford to pursue Ph.D. studies on visualnavigation, he began work with the cart, “but suffered a setback in October
1973 when the cart toppled off an exit ramp while under manual control andended up with battery acid throughout its electronics.” By 1979 Moravec got
the refurbished cart, now equipped with stereo vision, to cross a cluttered
room without human intervention. But it did this very slowly. According toMoravec,39
The system was reliable for short runs, but slow. The Cart moved
1 m every 10 to 15 min, in lurches. After rolling a meter it stopped,
took some pictures, and thought about them for a long time. Thenit planned a new path, executed a little of it, and paused again. It
successfully drove the Cart through several 20-m courses (eachtaking about 5 h) complex enough to necessitate three or four
avoiding swerves; it failed in other trials in revealing ways.
A short video of the cart in action can be seen at
http://www.frc.ri.cmu.edu/users/hpm/talks/Cart.1979/Cart.final.mov. Alongwith Shakey, the Stanford Cart resides in the Computer History Museum in
231
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12

NOTES

Figure 12.8: The Stanford cart. (Photograph courtesy of Lester Earnest.)
Mountain View, California. They were the progenitors of a long line of robot
vehicles, which will be described in subsequent chapters.

Notes
1. Hans P. Moravec, Robot: Mere Machine to Transcendent Mind, pp. 18–19, Oxford:
Oxford University Press, 1999. [213]
2. A copy of the proposal is available online at http://www.ai.sri.com/pubs/files/1320.pdf.
Its cover page says “Prepared by Nils J. Nilsson,” but it was really a team effort, and manyof the ideas were elaborations of those put forward in Rosen’s 1963 memo. [215]
3. A copy of the complete statement can be found at
http://ai.stanford.edu/∼nilsson/automaton-work-statement.pdf. [215]4. Online copies of SRI’s proposals for the automaton project, subsequent progress reports,
and related papers can be found at http://www.ai.sri.com/shakey/. [216]5. See http://www.robothalloffame.org/. [216]6. See http://www.computerhistory.org/timeline/?category=rai. [216]232
Copyright c 2010 Nils J. Nilsson
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12.2

NOTES

7. J. Doran and Donald Michie, “Experiments with the Graph Traverser Program,”
Proceedings of the Royal Society of London, Series A, Vol. 294, pp. 235–259, 1966. [220]8. The first written account of this idea was in Charles A. Rosen and Nils Nilsson,“Application of Intelligent Automata to Reconnaissance,” pp. 21–22, SRI Report, December
1967. Available online at http://www.ai.sri.com/pubs/files/rosen67-p5953-interim3.pdf.
[220]
9. Personal communication, October 24, 2006. [220]10. See Peter Hart, Nils Nilsson, and Bertram Raphael, “A Formal Basis for the Heuristic
Determination of Minimum Cost Paths,” IEEE Transactions System Science andCybernetics, Vol. 4, No. 2, pp. 100–107, 1968, and Peter Hart, Nils Nilsson, and Bertram
Raphael, “Correction to ‘A Formal Basis for the Heuristic Determination of Minimum-Cost
Paths,”’ SIGART Newsletter, No. 37, pp. 28–29, December 1972. [221]11. See, for example, Korf’s publications at
http://www.cs.ucla.edu/∼korf/publications.html. [221]12. In a 2003 paper titled “A∗ Parsing: Fast Exact Viterbi Parse Selection,” Dan Klein andChristopher Manning wrote “The use of A∗ search can dramatically reduce the timerequired to find a best parse by conservatively estimating the probabilities of parsecompletions.” [221]13. In an e-mail to Peter Hart dated March 27, 2002, Brian Smart, the chief technical officer
of a vehicle-navigation company, wrote “Like most of the ‘location based services’ and‘vehicle navigation’ industry, we use a variant of A∗ for computing routes for vehicle andpedestrian navigation applications.” [221]14. In an e-mail to me dated June 14, 2003, Steven Woodcock, a consultant on the use of AI
in computer games, wrote “A∗ is far and away the most used. . . and most useful. . . algorithm
for pathfinding in games today. At GDC roundtables since 1999, developers have noted thatthey make more use of A∗ than any other tool for pathfinding.” [221]15. See Bertram Raphael et al., “Research and Applications – Artificial Intelligence,” pp.
27–32, SRI Report, April 1971. Available online at
http://www.ai.sri.com/pubs/files/raphael71-p8973-semi.pdf. [221]16. For a description of how QA3 developed a plan for Shakey to push three objects to thesame place, for example, see Nils J. Nilsson, “Research on Intelligent Automata,” Stanford
Research Institute Report 7494, pp. 10ff, February 1969; available online at
http://www.ai.sri.com/pubs/files/nilsson69-p7494-interim1.pdf. [222]
17. McCarthy and Hayes first described this problem in John McCarthy and Patrick Hayes,“Some Philosophical Problems from the Standpoint of Artificial Intelligence,” in Donald
Michie and Bernard Meltzer (eds.), Machine Intelligence, Vol. 4, pp. 463–502, 1969.
Reprinted in Matthew Ginsberg (ed.), Readings in Nonmonotonic Reasoning, pp. 26–45,
San Francisco: Morgan Kaufmann Publishers, Inc., 1987. Preprint available online at
http://www-formal.stanford.edu/jmc/mcchay69/mcchay69.html. [222]
18. See Carl Hewitt, “ PLANNER: A Language for Proving Theorems in Robots,”
Proceedings of the First International Joint Conference on Artificial Intelligence, pp.
295–301, 1969. [223]
19. See Richard Fikes and Nils Nilsson, “STRIPS: A New Approach to the Application ofTheorem Proving to Problem Solving,” Artificial Intelligence, Vol. 2, Nos. 3–4, pp. 189–208,
1971. Available online at
http://ai.stanford.edu/users/nilsson/OnlinePubs-Nils/PublishedPapers/strips.pdf. [223]
20. The generalization and execution mechanisms are described in Richard Fikes, Peter
Hart, and Nils Nilsson, “Learning and Executing Generalized Robot Plans,” Artificial
Intelligence, Vol. 3, No. 4, pp. 251–288, 1972. Available online (as an SRI report) at
http://www.ai.sri.com/pubs/files/tn070-fikes72.pdf. [224]233
Copyright c 2010 Nils J. Nilsson
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12

NOTES

21. See Claude Brice and Claude Fennema, “Scene Analysis Using Regions,” Artificial
Intelligence, Vol. 1, No. 3, pp. 205–226, 1970. [225]
22. Richard O. Duda and Peter E. Hart, “Use of the Hough Transformation to Detect Lines
and Curves in Pictures,” Communications of the ACM, Vol. 15, pp. 11–15, January 1972.
See also, Peter E. Hart, “How the Hough Transform Was Invented,” IEEE Signal Processing
Magazine, November, 2009. [225]23. Bertram Raphael et al., “Research and Applications – Artificial Intelligence,” Part V,
SRI Final Report, December 1971; available online at
http://www.ai.sri.com/pubs/files/raphael71-p8973-final.pdf. [226]24. For more information about Shakey’s visual routines, in addition to the final report justcited, see Richard O. Duda, “Some Current Techniques for Scene Analysis,” SRI Artificial
Intelligence Group Technical Note 46, October 1970, available online at
http://www.ai.sri.com/pubs/files/tn046-duda70.pdf. [226]
25. This experiment, as well as other information about Shakey, is described in Bertram
Raphael et al., “Research and Applications – Artificial Intelligence,” SRI Final Report,
December 1971, available online at
http://www.ai.sri.com/pubs/files/raphael71-p8973-final.pdf; in Nils Nilsson (ed.), “Shakey
The Robot,” SRI Technical Note 323, April 1984, available online at
http://www.ai.sri.com/pubs/files/629.pdf; and in a 1972 film, Shakey: An Experiment in
Robot Planning and Learning, available online at
http://www.ai.sri.com/movies/Shakey.ram. [228]26. L. Stephen Coles, “Talking with a Robot in English,” Proceedings of the International
Joint Conference on Artificial Intelligence, Washington, DC, May 7–9, Bedford, MA: The
MITRE Corporation, 1969. [228]27. See L. Stephen Coles et al., “Application of Intelligent Automata to Reconnaissance,”
SRI Final Report, November 1969, available online at
http://www.ai.sri.com/pubs/files/coles69-p7494-final.pdf. [228]28. The problem was introduced by McCarthy in his July 1963 memo “Situations, Actions,and Causal Laws,” reprinted as Section 7.2 of his paper “Program with Commonsense,”
which appeared in Marvin Minsky (ed.), Semantic Information Processing, pp. 403–418,
Cambridge, MA: MIT Press, 1968. [229]29. E-mail from Eric Horvitz of May 12, 2003. [229]30. Peter E. Hart et al., “Artificial Intelligence – Research and Applications,” Technical
Report, Stanford Research Institute, December 1972. (Available online at
http://www.ai.sri.com/pubs/files/hart72-p1530-annual.pdf.) See also Richard E. Fikes,
Peter E. Hart, and Nils J. Nilsson, “Some New Directions in Robot Problem Solving,” inMachine Intelligence 7, Bernard Meltzer and Donald Michie (eds.), pp. 405–430, Edinburgh:
Edinburgh University Press, 1972. (The SRI Technical Note 68 version is available online at
http://www.ai.sri.com/pubs/files/1484.pdf.) [229]31. Earl D. Sacerdoti, “Planning in a Hierarchy of Abstraction Spaces,” pp. 412–422,
Proceedings of the Third International Joint Conference on Artificial Intelligence, 1973.
(The SRI AI Center Technical Note 78 version is available online at
http://www.ai.sri.com/pubs/files/1501.pdf.) [230]32. Earl D. Sacerdoti, “The Non-Linear Nature of Plans,” Proceedings of the International
Joint Conference on Artificial Intelligence, 1975. (The SRI AI Center Technical Note 101
version is available online at http://www.ai.sri.com/pubs/files/1385.pdf.) Also see Earl D.
Sacerdoti, A Structure for Plans and Behavior, New York: Elsevier North-Holland, 1977.
(The SRI AI Center Technical Note No. 109 version is available online at
http://www.ai.sri.com/pubs/files/762.pdf.) [230]
33. Austin Tate, “Interacting Goals and Their Use,” Proceedings of the Fourth International
Joint Conference on Artificial Intelligence (IJCAI-75), pp. 215–218, Tbilisi, USSR,

234
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12.2

NOTES

September 1975; available online at http://www.aiai.ed.ac.uk/project/oplan/documents/
1990-PRE/1975-ijcai-tate-interacting-goals.pdf. Austin Tate, “Using Goal Structure to
Direct Search in a Problem Solver,” Ph.D. thesis, University of Edinburgh, September 1975;
available online at http://www.aiai.ed.ac.uk/project/oplan/documents/1990-PRE/. [230]34. Austin Tate, “Generating Project Networks,” Proceedings of the Fifth International
Joint Conference on Artificial Intelligence (IJCAI-77) pp. 888–893, Boston, MA, August
1977; available online at http://www.aiai.ed.ac.uk/project/oplan/documents/1990-PRE/
1977-ijcai-tate-generating-project-networks.pdf. [230]35. See the SIPE-2 Web page at http://www.ai.sri.com/∼sipe/. [231]36. Ken Currie and Austin Tate, “O-PLAN: The Open Planning Architecture,” Artificial
Intelligence, Vol. 52, pp. 49–86, 1991. Available online at
http://www.aiai.ed.ac.uk/project/oplan/documents/1991/91-aij-oplan-as-published.pdf.
[231]
37. See, for example, AIAI’s “Planning and Activity Management” Web page at
http://www.aiai.ed.ac.uk/project/plan/. [231]38. Lester Earnest, “Stanford Cart,” August 2005; available online at
http://www.stanford.edu/∼learnest/cart.htm. [231]39. Hans P. Moravec, “The Stanford Cart and the CMU Rover,” Proceedings of the IEEE,
Vol. 71, No. 7, pp. 872–884, July 1983. [231]235
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12

NOTES

236
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13.1

Chapter 13

Progress in Natural
Language Processing
As mentioned previously, the problems of understanding, generating, andtranslating material in ordinary human (rather than computer) languages fallunder the heading of natural language processing. During the “early
explorations” phase of AI research, some good beginnings were made on NLP
problems. In the subsequent phase, the late 1960s to early 1970s, new work
built on these foundations, as I’ll describe in this part of the book.

13.1

Machine Translation

W. John Hutchins, who has written extensively about the history of machinetranslation (MT), has called the period 1967 to 1976, “the quiet decade.”1
Inactivity in the field during this period is due in part to the ALPAC report,
which, as I have already said, was pessimistic about the prospects for machinetranslation. Hutchins claimed “The influence of the ALPAC report was
profound. It brought a virtual end to MT research in the USA for over a
decade and MT was for many years perceived as a complete failure. . . . The
focus of MT activity switched from the United States to Canada and toEurope.”2
One exception to this decade-long lull in the United States was thedevelopment of the Systran (System Translator) translating program by PetrToma, a Hungarian-born computer scientist and linguistics researcher who had
worked on the Georgetown Russian-to-English translation system. In 1968,
Toma set up a company called Latsec, Inc., in La Jolla, California, to continue
the Systran development work he had begun earlier in Germany. The U.S. Air
Force gave the company a contract to develop a Russian-to-English translation
237
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13Progress in Natural Language Processing

system. It was tested in early 1969 at the Wright-Patterson Air Force Base in
Dayton, Ohio, “where it continues to provide Russian–English translations for
the USAF’s Foreign Technology Division to this day.”3 Systran has evolved to
be one of the main automatic translation systems. It is marketed by the
Imageforce Corporation in Tampa, Florida.4
How well does Systran translate? It all depends on how one wants tomeasure performance. Margaret Boden mentions two measures, namely,“intelligibility” and “correctness.” Both of these measures depend on humanjudgement. For the first, one asks “Can the translation be generally
understood?” For the second, one asks “Do human ‘post-editors’ need tomodify the translation?” Boden states that “in the two-year period from 1976
to 1978, the intelligibility of translations generated by Systran rose from 45 to
78 percent for [raw text input]. . . ” She also notes that human translations
score only 98 to 99 percent, not 100 percent. Regarding correctness, Boden
states that in 1978 “only 64 percent of the words were left untouched byhuman post-editors. Even so, human post-editing of a page of Systran output
took only twenty minutes in the mid-1980s, whereas normal (fully human)translation would have taken an hour.”5

13.2

Understanding

Although the late 1960s and early 1970s might have been a “quiet decade” formachine translation, it was a very active period for other NLP work.
Researchers during these years applied much more powerful syntactic,
semantic, and inference abilities to the problem of understanding natural
language. Typical of the new attitude was the following observation by Terry
Winograd, an MIT Ph.D. student during the late 1960s:6
If we really want computers to understand us, we need to givethem ability to use more knowledge. In addition to a grammar of
the language, they need to have all sorts of knowledge about the
subject they are discussing, and they have to use reasoning to
combine facts in the right way to understand a sentence and torespond to it. The process of understanding a sentence has tocombine grammar, semantics, and reasoning in a very intimate
way, calling on each part to help with the others.

13.2.1SHRDLU

Perhaps the NLP achievement that caused the greatest excitement was the
SHRDLU natural language dialog system programmed by Terry Winograd(1946– ; Fig. 13.1) for his Ph.D. dissertation (under Seymour Papert) at MIT.7

238
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13.2

Understanding

Figure 13.1: Terry Winograd. (Photograph courtesy of Terry Winograd.)SHRDLU was able to carry on a dialog about what Winograd called a“micro-world,” a world consisting of toy blocks and a “gripper” for moving
them about. Unlike the the world of real blocks used in previous MIT and
Stanford hand–eye research, Winograd’s blocks world was simulated on a DEC
PDP-10 computer and viewed on a DEC 340 black-and-white graphics display
such as that shown in Fig. 13.2. SHRDLU could be given commands, typed inordinary English, to manipulate the blocks using its gripper. It could also be
asked questions about the state of its world. It could “understand” thosecommands and questions, carry out requested manipulations, and ask the user
clarifying questions in English about questions or commands it did notunderstand.

Before describing something about SHRDLU’s design, let us look at an
example of part of a dialog.8 In the following, the user’s input is numberedand shown in lowercase letters, and SHRDLU’s response is in all caps,
Winograd’s commentary follows in the same typeface, and mine is inparentheses in normal type:1.

pick up a big red block.

In order to pick up the red block, it had to clear it
off by finding a space for the green one and moving thegreen one away.
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13Progress in Natural Language Processing

Figure 13.2: SHRDLU’s world. (Illustration used with permission of Terry Winograd.)OK.
The system answers "OK" when it carries out a command.
(I won’t bother to show what the block configuration looks like as the dialog
proceeds, assuming you can use your own mental “model” to keep track ofthings. The gripper is now holding the red block.)2.

grasp the pyramid.

There are three pyramids in the scene, so the program
does nothing.
I DON’T UNDERSTAND WHICH PYRAMID YOU MEAN.
3. find a block which is taller than the one you areholding and put it into the box.
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13.2

Understanding(The user has now apparently changed his mind about pyramid grasping.)
The system has to figure out what is meant by "one" and"it," as well as using deduction to decide which block
it is holding.
BY "IT," I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER
THAN THE ONE I AM HOLDING.
Printed out to keep us informed.
The hand has to set down the red block to pick up thetaller blue one.
OK.
4.

what does the box contain?

THE BLUE PYRAMID AND THE BLUE BLOCK.
5.

what is the pyramid supported by?

Earlier the system responded to "the pyramid" by saying"I don’t understand which pyramid you mean." now, it
knows that we must mean the one which was justmentioned.
THE BOXThis dialog illustrating SHRDLU’s capabilities goes on for forty-two more user
inputs and system responses.9
To engage in dialogs like this, SHRDLU needed “knowledge.” It needed toknow about the syntax of that part of English it used, and it needed to knowwhat various words and word combinations mean in the context of their use.
It also needed to know about its blocks world – how blocks can be
manipulated and what it means for an object to be inside of the “box.” Itneeded to keep track of the dialog so that it could decide to which object
mentioned previously a word such as “it” referred.
All of this needed knowledge was represented in LISP programs, or“procedures,” as Winograd called them. Knowledge about syntax was
represented as a collection of procedures based on the principles of “systemic
grammar.”10 Knowledge about the meanings of words in context wasrepresented in procedures that could refer to a dictionary of word meanings, toother parts of the sentence in which the word was used, and to the discourse.
Knowledge about the blocks world was represented in two ways: There was amodel that gave the locations of all of the objects and there were proceduresthat could infer the predicted effects (in the model) of manipulations by thegripper on the various objects. The object-moving procedures had information
both about the preconditions and about the effects of these manipulations.
These procedures were encoded in a version of Hewitt’s PLANNER language,
which, as mentioned previously, bore some resemblance to STRIPS operators.
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13Progress in Natural Language Processing

Additional procedures in the PLANNER language were used for other types ofinference needed by the system. Logical rules were expressed as programs,
which were capable of making both forward and backward deductions.
SHRDLU’s processes for language understanding can be divided into threeparts, namely, syntax, semantics, and inference, but doing so is somewhat
misleading because the interplay among these parts was a key feature of thesystem. As Winograd stated, “Since each piece of knowledge can be aprocedure, it can call on any other piece of knowledge of any type.” Forexample, Winograd wrote, “As it finds each piece of the syntactic structure, it
checks its semantic interpretation, first to see if it is plausible, then (ifpossible) to see if it is in accord with the system’s knowledge of the world,both specific and general.”

Winograd’s procedural representation of knowledge (together with
Hewitt’s PLANNER language for encoding such representations) can becontrasted with McCarthy’s use of logical formulas to represent knowledge
declaratively. The success of SHRDLU fueled a debate among AI researchers
about the pros and cons of these two knowledge representation strategies –procedural versus declarative. Actually, the use of LISP to represent
procedures blurs this distinction to some extent because, as Winograd pointed
out, “LISP allows us to treat programs as data and data as programs.” So,even though SHRDLU’s knowledge was represented procedurally, it was able toincorporate some declarative new knowledge (presented to it as English
sentences) into its procedures.
SHRDLU’s performance was indeed quite impressive and made somenatural language researchers optimistic about future success.11 However,
Winograd soon abandoned this line of research in favor of pursuing work
devoted to the interaction of computers and people. Perhaps because he had
first-hand experience of how much knowledge was required for successful
language understanding in something so simple as the blocks world, hedespaired of ever giving computers enough knowledge to duplicate the fullrange of human verbal competence. In a 2004 e-mail, Winograd put SHRDLU’sabilities in context with those of humans:12

There are fundamental gulfs between the way that SHRDLU and its
kin operate, and whatever it is that goes on in our brains. I don’t
think that current research has made much progress in crossingthat gulf, and the relevant science may take decades or more to getto the point where the initial ambitions become realistic. In themeantime AI took on much more doable goals of working in lessambitious niches, or accepting less-than-human results (as intranslation).

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13.2

Understanding

13.2.2

LUNAR

On their return from the first manned moon landing, the Apollo 11 astronauts
brought back several pounds of moon rocks for scientific study. Various data
about these rocks were stored in databases that could be accessed bygeologists and other scientists. To make retrieval of this information easier for
lunar geologists, NASA asked William A. Woods, a young computer scientist
at BBN, about the possibility of designing some sort of natural-language“front end” so that the databases could be queried in English instead of inarcane computer code. Woods had just completed his Ph.D. research at
Harvard on question-answering systems.13
Sponsored by NASA’s Manned Spacecraft Center, Woods and BBN
colleagues Ron Kaplan and Bonnie Webber developed a system they called“LUNAR” for answering questions about the moon rocks.14 LUNAR used bothsyntactic and semantic processes to transform English questions into moonrock database queries. Syntactic analysis was performed by using “augmented
transition networks” (ATNs), a methodology developed by Woods during his
Harvard Ph.D. research. (I’ll describe what ATNs are all about shortly.) The
semantic component, guided by the ATN-derived parse trees, transformed
English sentences into what Woods called a “meaning representation language”(MRL). This language was a logical language (like that of the predicate
calculus) but extended with procedures that could be executed. MRL was
originally conceived by Woods at Harvard and further developed at BBN.
LUNAR was able to “understand” and answer a wide variety of questions,
including, for example,“What is the average concentration of aluminum in high alkali rocks?”“How many breccias contain olivine?”“What are they?” (LUNAR recognized that “they” referred to thebreccias named as answers to the last question.)
LUNAR was the first question-answering system to publish performance data.

It was able to answer successfully 78% of the questions put to it by geologists
at the Second Annual Lunar Science Conference held in Houston in January
1971. Reportedly, 90% would have been answerable with “minor fixes” to thesystem.
In a June 2006 talk15 about LUNAR, Woods mentioned some of its
limitations. The following dialog illustrates one shortcoming:
User: What is a breccia?
LUNAR: S10018.

User: What is S10018?
LUNAR: S10018.

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13Progress in Natural Language Processing

Woods said, “LUNAR simply finds referents of referring expressions and gives
their names. There is no model of the purpose behind the user’s question or ofdifferent kinds of answers for different purposes.”
Although LUNAR could recognize several different ways of phrasing
essentially the same question, Woods claimed that “there are other requests
which (due to limitations in the current grammar) must be stated in a specificway in order for the grammar to parse them and there are others which are
only understood by the semantic interpreter when they are stated in certain
ways.”16

13.2.3

Augmented Transition Networks

Many people realized that context-free grammars (like the ones I discussed
earlier) were too weak for most practical natural language processing
applications. For example, if we were to expand the illustrative grammar I
described in Section 7.1 so that it included (in addition to “threw” and “hit”
and “man”) the present-tense verbs “throw,” “throws,” and “hits” and theplural noun “men,” then the strings “the men hits the ball” and “the man
throw the ball” would be inappropriately accepted as grammatical sentences.
To expand a context-free grammar to require that nouns and verbs must agree
as to number would involve an impractically large collection of rules. Also,
allowing for passive sentences, such as “the ball was hit by the men,” would
require even further elaboration. Clearly, the sorts of sentences that geologists
might ask about moon rocks required more powerful grammars – such as theaugmented transition networks that Woods and others had been developing.
In Chomsky’s 1957 book17 he had proposed a hierarchy of grammaticalsystems of which context-free grammars were just one example. His more
powerful grammars had a “transformational component” and were able, forexample, to parse a sentence such as “the ball was hit by the man” and give it
the same “deep structure” as it would give the sentence “the man hit theball.” Augmented transition network grammars could also perform these kindsof transformations but in a more computationally satisfying way.
An augmented transition network is a maplike graphical structure inwhich the nodes represent points of progress in the parsing process, and thepaths connecting two nodes represent syntactic categories. We can think ofparsing a sentence as traversing a path through the network from the startnode (no progress at all yet) to an end node (where the sentence has beensuccessfully parsed). Traversing the path builds the syntactic structure of thesentence in the form of a parse tree. Analysis of a sentence involves peeling off
the words in left-to-right fashion and using them to indicate which path in the
network to take.
Syntactic analysis could begin by peeling off a single word and finding out
from a lexicon whether it was a noun, a determiner, an auxiliary (such as
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13.2

Understanding“does”), an adjective, or some other “terminal” syntactic category. Or it could
begin by peeling off a group of words and checking to see whether this group
was a noun phrase, a verb phrase, a prepositional phrase, or what have you. In
the first case, depending on the category of the single word, we would take apath corresponding to that category leading out from the start node. To
accommodate the second case, there would be possible paths corresponding toa noun phrase and the other possible higher level syntactic categories.
But how would we decide whether or not we could take the noun-phrase
path, for example? The answer proposed by Woods and others was that there
would be additional transition networks corresponding to these higher level
categories. We would be permitted to take the noun-phrase path in the maintransition network only if we could successfully traverse the noun-phrase
network. And because one path in the noun-phrase network might start with aprepositional phrase, we would have to check to see whether we could takethat path (in the noun-phrase network) by successfully traversing a
prepositional-phrase network. This process would continue with one network“calling” other networks in a manner similar to the way in which a program
can fire up (or “call”) other programs, possibly recursively. (You will recall my
discussion of recursive programs: programs that can call versions ofthemselves.) For this reason, assemblages of networks like these are calledrecursive transition networks.
The first networks of this kind were developed at the University of
Edinburgh in Scotland by James Thorne, Paul Bratley, and Hamish Dewar.18
Later, Dan Bobrow and Bruce Fraser proposed a transition network systemthat elaborated on the Scottish one.19 Both of these systems also performed
auxiliary computations while traversing their networks. These“augmentations” allowed the construction of a “deep structure” representationof the sentence being analyzed. Woods’s work on “augmented, recursive
transition networks” built on and refined these ideas and introduced an
elegant network definition language.20
As an example, Woods described how one of his networks analyzed thesentence “John was believed to have been shot.”21 After all of the calls to
subsidiary networks and all of the auxiliary computations were performed theparse tree shown in Fig. 13.3 was obtained. We can observe two things about
this parse tree. First, note the occurrence of “PAST” and “PAST PERFECT”
as tense markers. Second, note that the form of the original passive-voicesentence has been transformed to an active-voice sentence using a presumed
pronoun “SOMEONE.” As Woods notes, the structure can be paraphrased as“Someone believed that someone had shot John.” Network grammars get atthe “deep structure” of sentences by transforming then into a standard form.

245
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13Progress in Natural Language Processing

Figure 13.3: A parse tree obtained for the sentence “John was believed tohave been shot.” (From William A. Woods, “Transition Network Grammars
for Natural Language Analysis,” Communications of the ACM, Vol. 13, No. 10,
pp. 591–606, 1970.)

13.2.4GUS

Dan Bobrow and Ronald Kaplan, two of the researchers doing NLP work at
BBN, moved to the newly formed Palo Alto Research Center (PARC) of Xerox
in the mid 1970s. One of the projects that they and other researchers workedon there was GUS (an acronym for Genial Understander System), which was“intended to engage a sympathetic and highly cooperative human in an
English dialog, directed towards a specific goal within a very restricted domainof discourse.”22 The restricted domain was that of a travel agent engaged in “aconversation with a client who wants to make a simple return trip to a single
city in California.” The following transcript of an actual dialog shows off someof its abilities:
GUS: Hello. My name is GUS. I can help you plan a simple trip byair. Where do you want to go?
(1) Client: I want to go to San Diego on May 28.
GUS: What time do you want to leave?

(2) Client: I must be in San Diego before 10 am.
GUS: Would you like P.S.A. flight 102 that arrives at 9.15 am?

(3) Client: What is the next flight?
GUS: Air California flight 310 that leaves at 8.30 am and arrives at

10.00 am.
(4) Client: I’ll take the first one.
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13.2

Understanding
GUS: In what name should I make a reservation, please?

(5) Client: Dan Bobrow.
GUS: I have confirmed the following flight: P.SA. flight 102 on

Wednesday May 28 from San Jose to San Diego that leaves at 7.30
am and arrives at 9.15 am. What date do you want to return on?
(6) Client: On Friday in the evening.
GUS: Would you like the flight that leaves at 7.45 pm?

(7) Client: That’s fine.
GUS: I have confirmed the following flight: P.S.A. flight 307 on

Friday May 30 from San Diego to San Jose that leaves at 7.45 pm
and arrives at 9.30 pm. Thank you for calling. Goodbye.
Although the abilities of the system were certainly limited, GUS was able
to deal with a number of problems. One of these involves what NLP
researchers call “resolving anaphora,” by which they mean deciding on theobjects or events to which various words or phrases in a dialog refer. Several
examples, keyed to the numbered sentences in the dialog above, are mentioned
in the paper about GUS:At line (3), for example, the client’s query refers to the flightmentioned in GUS’s immediately preceding utterance. In (4) there
is a reference to the flight mentioned earlier in the conversation,[following line (2)]. Note that “next flight” in (3) was to beinterpreted relative to the order of flights in the airline guidewhereas “first one” in (4) refers to the order in which the flights
were mentioned. Another implicit referent underlies the use of“Friday” to specify a date in (6). Resolution of this reference
requires some complicated reasoning involving both the content
and the context of the conversation. Since May 28 has been given
as the departure date, it must presumably be the following Friday
that the client has in mind. On the other hand, suppose that the
specifications were reversed and Friday had been given as thedeparture date at line (1). It would then be most readilyinterpretable as referring to the Friday immediately following theconversation.
GUS was a combination of several communicating subsystems, amorphological analyzer for dealing with word components, a syntactic analyzer
for generating parse trees, a “reasoner” for figuring out a user’s meanings andintentions, and a language generator for responding. Controlling thesecomponents was done by using an “agenda” mechanism. As the authors
explain,247
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13Progress in Natural Language Processing
GUS operates in a cycle in which it examines this agenda, chooses

the next job to be done, and does it. In general, the execution of
the selected task causes entries for new tasks to be created andplaced on the agenda. Output text generation can be prompted byreasoning processes at any time, and inputs from the client arehandled whenever they come in. There are places at which
information from a later stage (such as one involving semantics)
are fed back to an earlier stage (such as the parser). A supervisory
process can reorder the agenda at any time.
The syntactic component of GUS had “access to a main dictionary of morethan 3,000 stems and simple idioms.” The syntactic analyzer was based on asystem developed earlier by Ronald Kaplan, which used a transition-network
grammar and was called a “General Syntactic Processor.”23 Client sentenceswere encoded in “frames” (which are related to Minsky’s frames but closer inform to semantic networks). Some frames described the sequence of a normal
dialog, whereas others represented the attributes of a date, a trip plan, or a
traveler. GUS’s reasoning component used the content and structure of theframes to deduce how best to interpret client sentences.
Besides anaphora, the paper mentioned several other problems that GUS
was able to deal with. However, it also cautioned that “it is much too easy to
extrapolate from [the sample dialog] a mistaken notion that GUS contained
solutions to far more problems than it did.” Sample dialogs recorded between
human clients and humans playing the role of a GUS revealed numerous
instances in which the computer GUS would fail. The authors concluded that if
users of systems like GUS departed “from the behavior expected of them in theminutest detail, or if apparently insignificant adjustments are made in theirstructure,” the systems would act as if they had “gross aphasia” or had justsimply died. The authors conceded that “GUS itself is not very intelligent, but
it does illustrate what we believe to be essential components of [an intelligent
language understanding] system. . . . [It] must have a high quality parser, areasoning component, and a well structured data base of knowledge.”
Subsequent work on NLP at PARC and many other places sought to improve
all of these components.
The systems developed by researchers such as Winograd, Woods, Bobrow,and their colleagues were very impressive steps toward conversing withcomputers in English. Yet, there was still a long way to go before natural
language understanding systems could perform in a way envisioned by
Winograd in the preface to his Ph.D. dissertation:
Let us envision a new way of using computers so they can take
instructions in a way suited to their jobs. We will talk to them justas we talk to a research assistant, librarian, or secretary, and they
will carry out our commands and provide us with the information
we ask for. If our instructions aren’t clear enough, they will ask for
248
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13.2

NOTES
more information before they do what we want, and this dialog will
all be in English.

Notes
1. W. John Hutchins, “Machine Translation: A Brief History,” in E. F. K. Koerner and R.
E. Asher (eds.), Concise History of the Language Sciences: From the Sumerians to the
Cognitivists, pp. 431–445, Oxford: Pergamon Press, 1995. (Also available online at
http://ourworld.compuserve.com/homepages/WJHutchins/Conchist.htm.) [237]2. Ibid. [237]3. W. John Hutchins, Machine Translation: Past, Present, Future, Chichester: Ellis
Horwood, 1986. An updated (2003) version is available online at
http://www.hutchinsweb.me.uk/PPF-TOC.htm. Some of the technical details of Systran’s
operation are described in the book. [238]4. http://www.translationsoftware4u.com/. [238]
5. Margaret A. Boden, Mind as Machine: A History of Cognitive Science, p. 683, Oxford:
Oxford University Press, 2006. [238]
6. This quote is from the preface of Winograd’s Ph.D. dissertation. [238]
7. SHRDLU is described in Winograd’s dissertation “Procedures as a Representation for
Data in a Computer Program for Understanding Natural Language.” It was issued as an
MIT AI Technical Report No. 235, February 1971, and is available online athttps://dspace.mit.edu/bitstream/1721.1/7095/2/AITR-235.pdf. The thesis was also
published as a full issue of Cognitive Psychology, Vol. 3, No. 1, 1972, and as a book
Understanding Natural Language, New York: Academic Press, 1972. The letters in
SHRDLU comprise the second column of keys in linotype machines, which were used to set
type before computers were used for that. This nonsense word was often used in MAD
magazine, which Winograd read in his youth. Failing to think of an acceptable acronym to
use to name his system, Winograd used SHRDLU. For Winograd’s account, see
http://hci.stanford.edu/∼winograd/shrdlu/name.html. [238]
8. Taken from Section 1.3 of Winograd’s thesis. [239]
9. Readers interested in the entire dialog can see it either in Winograd’s thesis or on one of
his Web sites at http://hci.stanford.edu/∼winograd/shrdlu/. [241]10. Winograd cites, among others, M. A. K. Halliday, “Categories of the Theory ofGrammar,” Word, Vol, 17, No. 3, pp. 241-292, 1961. [241]11. For a short film of SHRDLU in action, see
http://projects.csail.mit.edu/films/aifilms/digitalFilms/3mpeg/26-robot.mpg. [242]12. From http://www.semaphorecorp.com/misc/shrdlu.html. [242]
13. William A. Woods, “Semantics for a Question-Answering System,” Ph.D. dissertation,
Harvard University, August 1967. Reprinted as a volume in the series Outstanding
Dissertations in the Computer Sciences, New York: Garland Publishing, 1979. [243]14. William A. Woods, Ron M. Kaplan, and Bonnie Nash-Webber, “The Lunar Sciences
Natural Language Information System: Final Report,” BBN, Cambridge, MA, June 1, 1972.
See also William A. Woods, “Progress in Natural Language Understanding – An Application
to Lunar Geology,” AFIPS Conference Proceedings, Vol. 42, pp. 441–450, Montvale, New
Jersey: AFIPS Press, 1973. [243]15. See http://www.ils.albany.edu/IQA06/Files/Bill Woods IQA06.pdf. [243]249
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13

NOTES

16. William A. Woods, “Progress in Natural Language Understanding – An Application to
Lunar Geology,” AFIPS Conference Proceedings, Vol. 42, pp. 441–450, Montvale, New
Jersey: AFIPS Press, 1973. [244]
17. Noam Chomsky, Syntactic Structures, ’s-Gravenhage: Mouton & Co., 1957. [244]18. James Thorne, Paul Bratley, and Hamish Dewar, “The Syntactic Analysis of English by
Machine,” in D. Michie (ed.), Machine Intelligence 3, pp. 281–309, New York: American
Elsevier Publishing Co., 1968; Hamish Dewar, Paul Bratley, and James Thorne, “A Program
for the Syntactic Analysis of English Sentences,” Communications of the ACM, Vol. 12, No.
8, pp. 476–479, August 1969. [245]
19. Daniel Bobrow and Bruce Fraser, “An Augmented State Transition Network Analysis
Procedure,” Proceedings of the International Joint Conferenece on Artificial Intelligence,
pp. 557–567, Washington, DC, 1969. [245]20. William A. Woods, “Augmented Transition Networks for Natural Language Analysis,”
Report CS-1, Aiken Computation Laboratory, Harvard University, Cambridge, MA,
December 1969; William A. Woods, “Transition Network Grammars for Natural Language
Analysis,” Communications of the ACM, Vol. 13, No. 10, pp. 591–606, 1970. The ACM
article has been reprinted in Yoh-Han Pao and George W. Ernest (eds.), Tutorial:
Context-Directed Pattern Recognition and Machine Intelligence Techniques for Information
Processing, Silver Spring, MD: IEEE Computer Society Press, 1982, and in Barbara Grosz,
Karen Sparck Jones, and Bonnie Webber (eds.), Readings in Natural Language Processing,
San Mateo, CA: Morgan Kaufmann, 1986. [245]21. William A. Woods, op. cit., pp. 602ff. [245]
22. Daniel G. Bobrow et al., “GUS, A Frame-Driven Dialog System,” Artificial Intelligence,Vol. 8, pp. 155–173, 1977. [246]
23. Ronald Kaplan, “A General Syntactic Processor,” in R. Rustin (ed.), Natural Language
Processing, New York: Algorithmics Press, 1973. [248]250
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14.0

Chapter 14

Game Playing
I have already mentioned attempts to program computers to play board
games, such as chess and checkers. The most successful of these was Arthur
Samuel’s checker-playing program. In 1967, Samuel published a paper
describing an improved version of his program.1 He had refined the program’s
search procedure and incorporated better “book-learning” capabilities, andinstead of calculating the estimated value of a position by adding up weighted
feature values, he used hierarchically organized tables. According to Richard
Sutton, “This version learned to play much better than the 1959 program,though still not at a master level.”2
Between 1959 and 1962, a group of MIT students, advised by John
McCarthy, developed a chess-playing program. It was based on earlier
programs for the IBM 704 written by McCarthy. One of the group members,
Alan Kotok (1941–2006) described the program in his MIT bachelor’s thesis.3
The program was written in a combination of FORTRAN and machine(assembly) code and ran on the IBM 7090 computer at MIT. It used thealpha–beta procedure (as discussed earlier) to avoid generating branches of thesearch tree that could be eliminated without altering the final result. Kotok
claimed that his program did not complete any games but “played four longgame fragments in which it played chess comparable to an amateur with about
100 games experience. . . . Most of the machine’s moves are neither brilliant
nor stupid. It must be admitted that it occasionally blunders.”4 When
McCarthy moved to Stanford, he took the program along with him andcontinued to work on it.
In the meantime, a computer chess program was being developed by
Georgi Adelson-Velskiy and colleagues in Alexander Kronrod’s laboratory at
the Institute for Theoretical and Experimental Physics (ITEP) in Moscow.5
During a visit to the Soviet Union in 1965, McCarthy accepted a challenge tohave the Kotok–McCarthy program play the Soviet program. Beginning on
November 22, 1967, and continuing for about nine months, the251
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14Game Playing

Kotok–McCarthy program (running on a DEC PDP-6 at Stanford) played theRussian program (running on the Russian M-20 computer at ITEP) – the firstmatch to be played by a computer against a computer. In each of the first twogames, the Stanford program eked out a draw (by surviving until theagreed-upon limit of 40 moves) against a weak version of the Russian program.
However, it lost the last two games against a stronger version of the ITEP
program. McCarthy later claimed that, although Stanford had the better
computer, ITEP had the better programs.6 The ITEP program was theforerunner of the much improved Kaissa program developed later by Misha
Donskoy, Vladimir Arlazarov, and Alexander Ushkov at the Institute ofControl Science in Moscow.
Richard Greenblatt, an expert programmer at the AI Lab at MIT, thought
he could improve on Kotok’s chess program. His work on computer chesseventually led to a program he called MAC HACK VI.7 Being an expert chessplayer himself, he was able to incorporate a number of excellent heuristics forchoosing moves and for evaluating moves in his program. Running on the AI
Lab’s DEC PDP-6 and written in efficient machine code, MAC HACK VI was
the first program to play in tournaments against human chess players. In an
April 1967 tournament, it won two games and drew two, achieving a rating of1450 on the U.S. Chess Federation rating scale, about the level of an amateur
human player. (According to the international rating system for human chessplayers, the highest level is that of Grand Masters. Then come International
Masters, National Masters, Experts, Class A, Class B, and so on. MAC HACK
VI played at the high Class C to low Class B level, which is still quite far frommaster play.) It became an honorary member of the U.S. Chess Federationand of the Massachusetts Chess Association. In a famous match at MIT in
1967,8 Greenblatt’s program beat Hubert Dreyfus (1929– ), an AI critic who
had earlier observed that “no chess program could play even amateur chess.”9
Although Dreyfus’s observation was probably true in 1965, Greenblatt’s MAC
HACK VI was playing at the amateur level two years later.
Perhaps encouraged by MAC HACK’s ability, in 1968 Donald Michie andJohn McCarthy made a bet of £250 each with David Levy (1945– ), a Scottish
International Master, that a computer would be able to beat him within ten
years. (The following year Seymour Papert joined in, and in 1971 Ed
Kozdrowicki of the University of California at Davis did also, bringing thetotal bet to £1000. In 1974, Donald Michie raised the total to £1250.) In
1978, Levy collected on his bet – as we shall see later.10
Around 1970, three students at Northwestern University in Illinois, David
Slate, Larry Atkin, and Keith Gorlen, began writing a series of chessprograms. The first of these, CHESS 3.0, running on a CDC 6400 computer,won the first Association for Computing Machinery’s computer chess
tournament (computers against computers) in New York in 1970. There were
six entries – MAC HACK VI not among them. According to David Levy, “CHESS
3.0 evaluated approximately 100 positions per second and played at the 1400

252
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NOTES

level on the U.S. Chess Federation rating scale.” Subsequent Northwestern
programs, up through CHESS 4.6, achieved strings of wins at this annual event.
Meanwhile, however, CHESS 4.2 was beaten in an early round of the first World
Computer Chess Championship tournament held at the International
Federation of Information Processing Societies (IFIPS) meeting in Stockholm
in 1974. The Russian program, Kaissa, won all four games in that tournament,
thereby becoming the world computer chess champion.11
These years, the late 1960s through the mid-1970s, saw computer chessprograms gradually improving from beginner-level play to middle-level play.
Work on computer chess during the next two decades would ultimately achieve
expert-level play, as we shall see in a subsequent chapter. Despite this rapid
progress, it was already becoming apparent that there was a great difference
between how computers played chess and how humans played chess. As Hans
Berliner, a chess expert and a chess programming expert, put it in an article in
Nature,12[A human] uses prodigious amounts of knowledge in thepattern-recognition process [to decide on a good maneuver] and asmall amount of calculation to verify the fact that the proposedsolution is good in the present instance. . . . However, the computer
would make the same maneuver because it found at the end of avery large search that it was the most advantageous way toproceed out of the hundreds of thousands of possibilities it looked
at. CHESS 4.6 has to date made several well known maneuvers
without having the slightest knowledge of the maneuver, theconditions for its applications, and so on; but only knowing thatthe end result of the maneuver was good.
Berliner summed up the difference by saying that “The basis of humanchess strength, by contrast [with computers], is accumulated knowledge” (myitalics). Specific knowledge about the problem being solved, as opposed to theuse of massive search in solving the problem, came to be a major theme ofartificial intelligence research during this period. (Later, however, massive
search regained some of its importance.) Perhaps the most influential
proponents of the use of knowledge in problem solving were Edward
Feigenbaum and his colleagues at Stanford. I’ll turn next to their seminal
work.

Notes
1. Arthur L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers II –Recent Progress,” IBM Journal of Research and Development, Vol. 11, No. 6, pp. 601–617,
1967. [251]2. http://www.cs.ualberta.ca/∼sutton/book/11/node3.html. [251]253
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14

NOTES

3. Alan Kotok, “A Chess Playing Program for the IBM 7090 Computer” MIT bachelor’s
thesis in Electrical Engineering, June 1962. Online versions of the thesis are available at
http://www.kotok.org/AK-Thesis-1962.pdf and http://www.kotok.org/AI Memo 41.html.
(The latter is an MIT memo in which Kotok pointed out that “. . . this report, while written
by me, represents joint work of ‘the chess group,’ which consisted of me, Elwyn R.
Berlekamp (for the first year), Michael Lieberman, Charles Niessen, and Robert A. Wagner(for the third year). We are all members of the MIT [undergraduate] Class of 1962.) [251]4. The Computer History Museum has a video “oral history” of Kotok available at
http://www.computerhistory.org/chess/alan kotok.oral history highlight.102645440/index.
php?iid=orl-433444ecc827d. [251]5. G. M. Adelson-Velskiy, V. L. Arlazarov, A. R. Bitman, A. A. Zhivotovskii and A.V.
Uskov, “Programming a Computer to Play Chess,” Russian Mathematical Surveys 25,
March–April 1970, pp. 221–262, London: Cleaver-Hume Press. (Translation of Proceedingsof the 1st Summer School on Mathematical Programming, Vol. 2, pp. 216–252, 1969.) [251]6. See the oral presentation about the history of computer chess at
http://video.google.com/videoplay?docid=-1583888480148765375. [252]7. Richard D. Greenblatt, Donald E. Eastlake III, and Stephen D. Crocker, “TheGreenblatt Chess Program,” AI Memo 174, April 1969. Available online athttps://dspace.mit.edu/bitstream/1721.1/6176/2/AIM-174.pdf. [252]8. See an account in the SIGART Newsletter, December 1968. [252]9. Hubert L. Dreyfus, “Alchemy and Artificial Intelligence,” RAND paper P-3244, p. 10,
The RAND Corporation, Santa Monica, CA, December 1965. Available online at
http://www.rand.org/pubs/papers/2006/P3244.pdf. [252]10. For first-hand details about the bet, see David Levy, Robots Unlimited: Life in a VirtualAge, p. 83, Wellesley, MA: A K Peters, Ltd., 2006. [252]11. See the Computer History Museum’s exhibits on the history of computer chess at
http://www.computerhistory.org/chess/index.php. For a concise timeline of computer chesshistory compiled by Bill Wall, visit
http://www.geocities.com/SiliconValley/Lab/7378/comphis.htm. [253]12. Hans J. Berliner, “Computer Chess,” Nature, Vol. 274, p. 747, August 1978. [253]254
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15.0

Chapter 15

The Dendral Project
After Ed Feigenbaum moved from UC Berkeley to Stanford in 1965, hebecame interested in “creating models of the thinking processes of scientists,
especially the processes of empirical induction by which hypotheses andtheories were inferred from data.” As he put it, “What I needed was a specifictask environment in which to study these issues concretely.”1 Feigenbaum
recalls attending a Behavioral Sciences workshop at Stanford and hearing atalk by Joshua Lederberg (1925–2008; Fig. 15.1), a Nobel Prize–winning
geneticist and founder of the Stanford Department of Genetics. Lederberg
talked about the problem of discerning the structure of a chemical compoundfrom knowledge of its atomic constituents and from its mass spectrogram.
This sounded like the kind of problem Feigenbaum was looking for, and he andLederberg soon agreed to collaborate on it.2

Figure 15.1: Edward Feigenbaum (left), Joshua Lederberg (middle), and Bruce
Buchanan (right). (Photographs courtesy of Edward Feigenbaum.)

255
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15

The Dendral ProjectChemical molecules are described by formulas that give their atomic
constituents. For example, the formula for propane is C3 H8 , indicating that it
consists of three carbon atoms and eight hydrogen atoms. But there is more toknow about a compound than what atoms it is made of. The atoms
composing a molecule are arranged in a geometric structure, and chemists
want to know what that structure is. The three carbon atoms in propane, forexample, are attached together in a chain. The two carbon atoms at the endsof the chain each have three hydrogen atoms attached to them, and the single
carbon atom in the middle of the chain has two hydrogen atoms attached to it.
Chemists represent this structure by the diagram shown in Fig. 15.2.

Figure 15.2: The structure of the propane molecule.
Chemists have found that it is not too difficult to discern the structure of
simple compounds like propane. However, it is more difficult for more complex
compounds, such as 2-methyl-hexan-3-one, a ketone with chemical formulaC7 H140 . One method that chemists have used to infer the structure of acompound is to bombard it with high-energy electrons in a mass spectrometer.
The electron beam of a mass spectrometer breaks the compound intofragments, and the resulting fragments are sorted according to their masses bya magnetic field within the spectrometer. A sample mass spectrogram isshown in Fig. 15.3.
The fragments produced by the mass spectrometer tend to be composedof robust substructures of the compound, and the masses of these reveal hints
about the main structure. An experienced chemist uses “accumulated
knowledge” (to use Berliner’s phrase) about how compounds tend to break up
in the mass spectrometer to make good guesses about a compound’s structure.
Feigenbaum and Lederberg, together with their colleague Bruce Buchanan(1940– ), who had joined Stanford in 1966 after obtaining a Ph.D. in
Philosophy at the University of Michigan, set about attempting to constructcomputer programs that could use mass spectrogram data, together with thechemical formula of a compound, to “elucidate” (as they put it) the structureof the compound.
Lederberg had already developed a computer procedure called Dendral(an acronym for Dendritic Algorithm) that could generate all topologicallypossible acyclic structures given the chemical formula and other basic chemical
information about how atoms attach to other atoms. (An acyclic structure is
one that does not contain any rings. You might recall, for example, that
256
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Figure 15.3: A mass spectrogram. (Illustration used with permission of Edward
Feigenbaum.)benzene contains six carbon atoms arranged in a hexagon, which chemists calla ring. Each of the carbon atoms has a hydrogen atom attached to it.)
Lederberg’s algorithm proceeded incrementally by generating partial
structures from the main formula, then generating more articulated partial
structures from these and so on in a treelike fashion. The tips or leaves of thetree would contain the final, fully articulated topologically possible structures.
Finding the actual structure of a compound (or at least the most plausible
actual structures) can be likened to a search down the tree to the appropriate
tip or tips.
Feigenbaum and colleagues proposed using the knowledge that skilled
chemists used when interpreting mass-spectral data. The chemists knew thatcertain features of the spectrograms implied that the molecule under study
would contain certain substructures and would not contain other ones. Thisknowledge could be used to limit the possible structures generated by
Lederberg’s Dendral algorithm. Knowledge of this sort was represented as“rules.” Here is one example of a Dendral rule:
Rule 74:IF
The spectrum for the molecule has two peaks
at masses X1 and X2 such that:
X1 + X2 = M + 28
andX1 - 28 is a high peakand257
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15

The Dendral Project
X2 - 28 is a high peakandat least one of X1 or X2 is high
THEN The molecule contains a ketone group

The first program to employ this kind of knowledge was called HEURISTICDENDRAL. (The adjective “heuristic” was used because knowledge from thechemists was used to control search down the Dendral tree.) It used as input
the chemical formula and mass-spectrometer data (and sometimes
nuclear-magnetic-resonance data) and produced as output an ordered set ofchemical structure descriptions hypothesized to explain the data. Early workwith HEURISTIC DENDRAL was limited to elucidating the structure of acyclic
compounds because these were the only ones that Lederberg’s algorithm could
handle. These included saturated acyclic ethers, alcohols, thioethers, thiols,
and amines. Here is one example of the power of their early program: There
are 14,715,813 possible structures of N,N-dimethyl-1-octadecyl amine. Using
the mass spectrum of that compound, HEURISTIC DENDRAL reduced thenumber to 1,284,792. Using the mass spectrum andnuclear-magnetic-resonance data, just one structure survived.3
The name “DENDRAL” came to describe a whole collection of programsfor structure elucidation developed during the Dendral project, which
continued to the end of the 1970s. Many of these programs are used bychemists today. Computer scientists and chemists working on the project wereable to extend Lederberg’s algorithm to handle cyclic compounds. After
Lederberg persuaded Stanford chemist Carl Djerassi to join the project,
performance was expanded greatly in both breadth and depth.4
An important innovation made during the Dendral project was asimulation of how a chemical structure would break up in a mass
spectrometer. After HEURISTIC DENDRAL produced some candidate structures
for a particular compound, these structures were subjected to analysis in thesimulated mass spectrometer. The outputs were then compared with theactual mass spectrometer output. That structure whose simulated
spectrogram was closest to the actual spectrogram was likely to be the actual
structure of the compound. This process of “analysis by synthesis” came to be
widely used in artificial intelligence, especially in computer vision.
From his experience during the DENDRAL years, Feigenbaum went on tochampion the importance of specific knowledge about the problem domain in
AI applications (as opposed to the use of general inference methods). He
proposed what he called the “knowledge-is-power” hypothesis, which he latercalled the “knowledge principle.”5 Here is how he later described it:6
We must hypothesize from our experience to date that the problem
solving power exhibited in an intelligent agent’s performance isprimarily a consequence of the specialist’s knowledge employed by
the agent, and only very secondarily related to the generality and258
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NOTES
power of the inference method employed. Our agents must be
knowledge-rich, even if they are methods-poor.

Embedding the knowledge of experts in AI programs led to the development ofmany “expert systems,” as we shall see later. It also led to increased
concentration on specific and highly constrained problems and away fromfocusing on the general mechanisms of intelligence, whatever they might be.

Notes
1. The quotation taken from “Comments by Edward A. Feigenbaum” in Edward H.
Shortliffe and Thomas C. Rindfleisch, “Presentation of the Morris F. Collen Award to
Joshua Lederberg,” Journal of the American Medical Informatics Association, Vol. 7, No.
3, pp. 326–332, May–June 2000. Available online at
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=61437. [255]
2. For an interesting account of the history of their collaboration, see “How DENDRALWas Conceived and Born,” by Joshua Lederberg, a paper presented at the Association for
Computing Machinery (ACM) Symposium on the History of Medical Informatics at the
National Library of Medicine on November 5, 1987. Later published in Bruce I. Blum andKaren Duncan (eds.), A History of Medical Informatics, pp. 14–44, New York: Association
for Computing Machinery Press, 1990. Typescript available online at
http://profiles.nlm.nih.gov/BB/A/L/Y/P/ /bbalyp.pdf. [255]
3. Robert K. Lindsay, Bruce G. Buchanan, Edward A. Feigenbaum, and Joshua Lederberg,
Applications of Artificial Intelligence for Organic Chemistry: The Dendral Project, p. 70,
New York: McGraw-Hill Book Co., 1980. [258]4. For a thorough account of achievements of the Dendral project, see ibid. [258]
5. The hypothesis seems to have been implicit in Edward A. Feigenbaum, “Artificial
Intelligence: Themes in the Second Decade,” Supplement to Proceedings of the IFIP 68
International Congress, Edinburgh, August 1968. Published in A. J. H. Morrell (ed.),Information Processing 68, Vol. II, pp. 1008–1022, Amsterdam: North-Holland, 1969. [258]
6. Edward A. Feigenbaum, “The Art of Artificial Intelligence: Themes and Case Studies ofKnowledge Engineering,” Proceedings of the Fifth International Joint Conference on
Artificial Intelligence, pp. 1014–1029, 1977. See also Edward A. Feigenbaum, “The Art ofArtificial Intelligence: I. Themes and Case Studies of Knowledge Engineering,” Stanford
Heuristic Programming Project Memo HPP-77-25, August 1977, which is available online at
http://infolab.stanford.edu/pub/cstr/reports/cs/tr/77/621/CS-TR-77-621.pdf. [258]259
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15

NOTES

260
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16.0

Chapter 16

Conferences, Books, andFunding
Accompanying the technical progress in artificial intelligence during thisperiod, new conferences and workshops were begun, textbooks were written,
and financial support for basic research grew and then waned a bit.
The first large conference devoted exclusively to artificial intelligence washeld in Washington, DC, in May 1969. Organized by Donald E. Walker(1928–1993) of the MITRE Corporation and Alistair Holden (1930–1999) of
the University of Washington, it was called the International Joint Conferenceon Artificial Intelligence (IJCAI). It was sponsored by sixteen different
technical societies (along with some of their subgroups) from the UnitedStates, Europe, and Japan. About 600 people attended the conference, andsixty-three papers were presented by authors from nine different countries.
The papers were collected in a proceedings volume, which was made available
at the conference to all of the attendees.
Because of the success of this first conference, it was decided to hold asecond one in London in 1971. During the early years, organization of theconferences was rather informal, decisions about future conferences being
made by a core group of some of the leaders of the field who happened to show
up at organizing meetings. At the 1971 meeting in London, I left the room fora moment while people were discussing where and when to hold the next
conference. When I returned, I was informed that I had been selected to be
the “czar” of the next meeting – to be held at Stanford University in 1973.
Later, a more formal arrangement was instituted for managing the“International Joint Conferences on Artificial Intelligence,” with a President, aBoard of Trustees, and a Secretariat.1 Since the first meeting, IJCAI
conferences are held biennially (on odd-numbered years) with the venue
alternating (loosely) between North America and the rest of the world. As at
261
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16

Conferences, Books, and Funding

the first conference, proceedings are distributed at the conferences. (Some ofthese can be obtained from various booksellers, and they are available online
at the Digital Library of India Web site, http://202.41.92.139/.)
One of the oldest “special interest groups” of the Association for
Computing Machinery (ACM) is SIGART (the Special Interest Group for
ARTificial intelligence). It began publishing a Newsletter in 1966, which (asthe SIGART Web site says) “continued in various incarnations (the SIGART
Bulletin, Intelligence Magazine) until 2001.” Today, SIGART supports various
conferences and workshops, and it organizes meetings in which AI doctoral
students can present their nearly finished work to their peers and to seniorresearchers for comments and critiques.
As the field began to develop its techniques and methods, graduate
courses in artificial intelligence were offered at some universities. Accordingly,
some of us who were teaching these courses thought it would be worthwhile towrite or edit books about AI. In 1963, Edward Feigenbaum and Julian
Feldman, then assistant professors at UC Berkeley, published a collection ofearly AI and cognitive science papers that had previously appeared in manydifferent places. The volume was called Computers and Thought and was
required reading for early students of AI (including me).2 As Feigenbaum
wrote in the 1995 edition, “Some of the papers are as important today for
their fundamental ideas as they were in the late 1950s and early 1960s when
they were written. Others are interesting as early milestones of fields thathave expanded and changed dramatically.”
In 1965, I published a book about neural-network and statistical methods
in pattern recognition.3 That book was followed in 1971 by a book about AI
search strategies.4 Around the same time, other texts were published byJames Slagle5 and by Bertram Raphael,6 both former Ph.D. students ofMarvin Minsky at MIT.
In 1969 Marvin Minsky and Seymour Papert published an influential
book in which they proved, among other things, that some versions of
Rosenblatt’s perceptrons had important limitations.7 Some have claimed that
the Minsky–Papert book was the cause of a fading interest in neural-network
research, but I doubt this. First, Rosenblatt himself began concentrating onother topics well before 1969,8 and the success of heuristic programming
methods caused a shift of attention (including my own) away from neuralnetworks during the mid-1960s.
In 1965, Donald Michie at the University of Edinburgh organized the firstof several invitation-only “Machine Intelligence” workshops. This first one washeld in Edinburgh and was attended by American and European researchers.
Attendees gave papers at the workshop, and these were all published in a bookedited by N. L. Collins and Donald Michie in 1967. A second workshop washeld in September 1966, also at the University of Edinburgh. Subsequent
workshops were held annually in Edinburgh through 1971. Thereafter, the262
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16.0
workshops were held every few years at various venues. Each workshop
resulted in a book with the title Machine Intelligence N, where N denotes theworkshop and volume number.9 The last few volumes have been published
online by the Electronic Transactions on Artificial Intelligence.10 These books
contain some of the most cited and important papers in the early history of
the field.
These years saw the United States engaged in war in Vietnam, andCongress wanted to make sure that research supported by the U.S. Defense
Department was relevant to military needs. Responding to these pressures, on
November 19, 1969, Congress passed the “Mansfield Amendment” to the
Defense Procurement Authorization Act of 1970 (Public Law 91-121), which
required that the Defense Department restrict its support of basic research toprojects “with a direct and apparent relationship to a specific military
function or operation.” On March 23, 1972, the Advanced Research Projects
Agency was renamed the Defense Research Advanced Projects Agency(DARPA) to reflect its emphasis on projects that contributed to enhanced
military capabilities. (The name reverted to the Advanced Research Projects
Agency in 1993 and then back to the Defense Advanced Research Projects
Agency in 1996.)11
On the other side of the Atlantic, British AI researchers experienced theirown funding crisis. One of the U.K.’s main funding bodies for university
research, the Science Research Council, asked Professor James Lighthill, afamous hydrodynamicist at Cambridge University, to undertake an evaluative
study of artificial intelligence research. Lighthill’s report, titled “Artificial
Intelligence: A General Survey,” somewhat idiosyncratically divided AI
research into three categories, namely, advanced automation, computer-based
studies of the central nervous system, and the bridges in between. He called
these categories A, C, and B, respectively. Although he came out in favor ofcontinued work in categories A and C, he was quite critical of most AI basic
research, including robotics and language processing, which he lumped into
category B. He wrote that “In no part of the field have the discoveries made so
far produced the major impact that was then [around 1960] promised.” Heconcluded that AI’s existing search techniques (which worked on toy
problems) would not scale to real problems because they would be stymied by
the combinatorial explosion.12
Lighthill’s report resulted in a substantial curtailment of AI research in
the United Kingdom. In particular, one of its casualties was work on
FREDDY the robot and other AI work under Donald Michie at Edinburgh.
Here is one of Michie’s later comments about the effects of the report:13
Work of excellence by talented young people was stigmatised as
bad science and the experiment killed in mid-trajectory. This
destruction of a co-operative human mechanism and of the carefulcraft of many hands is elsewhere described as a mishap. But to263
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16

NOTES
speak plainly, it was an outrage. In some later time, when thevalues and methods of science have further expanded, and those ofadversary politics have contracted, it will be seen as such.

DARPA’s shift to shorter term applied research, together with theLighthill report and criticisms from various onlookers, posed difficulties forbasic AI research during the next few years. Nevertheless, counter toLighthill’s assessment, many AI techniques did begin to find application toreal problems, launching a period of expansion in AI applications work, as
we’ll see in the next few chapters.

Notes
1. See http://www.ijcai.org/IJCAItrustees.php. [261]
2. Edward A. Feigenbaum and Julian Feldman, Computers and Thought, New York:
McGraw-Hill Book Co., 1963. (The McGraw-Hill volume is now out of print; it is now
available through AAAI Press/MIT Press, 1995 edition.) [262]3. Nils J. Nilsson, Learning Machines: Foundations of Trainable Pattern-Classifying
Systems, New York: McGraw-Hill Book Co., 1965; republished as The Mathematical
Foundations of Learning Machines, San Francisco: Morgan Kaufmann Publishers, 1990.
[262]
4. Nils J. Nilsson, Problem-Solving Methods in Artificial Intelligence, New York:
McGraw-Hill Book Co., 1971. [262]5. James R. Slagle, Artificial Intelligence: The Heuristic Programming Approach, New
York: McGraw-Hill Book Co., 1971. [262]
6. Bertram Raphael, The Thinking Computer: Mind Inside Matter, New York: W. H.
Freeman, 1976. [262]
7. Marvin Minsky and Seymour Papert, Perceptrons: An Introduction to Computational
Geometry, Cambridge, MA: MIT Press, 1969. [262]8. See Frank Rosenblatt, J. T. Farrow, and S. Rhine, “The Transfer of Learned Behavior
from Trained to Untrained Rats by Means of Brain Extracts. I,” Proceedings of the National
Academy of Sciences, Vol. 55, No. 3, pp. 548–555, March 1966. [262]
9. The series maintains a Web page at http://www.cs.york.ac.uk/mlg/MI/mi.html. [263]
10. See http://www.etai.info/mi/. [263]
11. See http://www.darpa.mil/body/arpa darpa.html. [263]
12. The text of the report, along with commentary and criticism by leading British AI
researchers, was published in 1972 in James Lighthill et al. (eds.), Artificial Intelligence: A
Paper Symposium, London: Science Research Council of Great Britain, 1972. [263]
13. Donald Michie, Machine Intelligence and Related Topics: An Information Scientist’s
Weekend Book, p. 220, New York: Gordon and Breach Science Publishers, 1982. [263]264
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16.0Part IV

Applications andSpecializations: 1970s to
Early 1980s

Until about the early 1970s, most AI research dealt with what Seymour
Papert called “toy” problems – programs that solved puzzles or games – or the
researchers pursued projects that were staged in highly controlled laboratory
settings. (Of course, there were some notable exceptions – machinetranslation, DENDRAL, and LUNAR, for example.) However, soon after, AI
efforts began a definite shift toward applications work, confronting problems ofreal-world importance. Inevitably, successful applications work encouraged
specialization into subdisciplines such as natural language processing, expert
systems, and computer vision.
One reason for the increasing interest in applications was that the powerof AI methods had increased to the point where realistic applications seemedwithin reach. But perhaps more importantly, the sponsors of AI research in
the U.S. Department of Defense (DoD) had to deal with the constraints
imposed on them by the 1969 “Mansfield Amendment,” which required thatbasic research be relevant to military needs. As one example of the increased
emphasis on applications, the Information Sciences Institute (USC-ISI) was
formed in 1972 specifically to pursue them. Located in Marina Del Rey,
California, it is affiliated with the University of Southern California andreceived much of its initial support from DARPA. Other large corporations
also began to explore AI’s commercial potential.
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16
Of course, theoretical and basic research continued also, and several new
university groups joined the existing ones. A short list of the new ones would
include those at the Universities of Toronto, Rochester, Texas, Maryland,
British Columbia, California, and Washington. Other groups started as well inEurope and Asia. (In 1981, I was invited to give lectures on AI in China,
which was newly recovering from its “Cultural Revolution” and beginning its
program of “Opening and Reform.”) But even at the universities, much of
their basic research was motivated by specific applications. In this part of thebook, I’ll describe some of the AI applications work undertaken during the1970s to the early 1980s.

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17.1

Chapter 17

Speech Recognition andUnderstanding Systems
17.1

Speech Processing

The NLP systems I have already described required that their English input
be in text format. Yet, there are several instances in which speaking to acomputer would be preferable to typing at one. People can generally speak
faster than they can type (about three words per second versus about one
word per second), and they can speak while they are moving about. Also,
speaking does not tie up hands or eyes.
In discussing the problem of computer processing of speech, it isimportant to make some distinctions. One involves the difference between
recognizing an isolated spoken word versus processing a continuous stream ofspeech. Most AI research has concentrated on the second and harder of these
problems. Another distinction is between speech recognition and speech
understanding.
By speech recognition is meant the process of converting an acoustic
stream of speech input, as gathered by a microphone and associated electronicequipment, into a text representation of its component words. This process isdifficult because many acoustic streams sound similar but are composed ofquite different words. (Consider, for example, the spoken versions of “There
are many ways to recognize speech,” and “There are many ways to wreck anice beach.”) Speech understanding, in contrast, requires that what is spoken
be understood. An utterance can be said to be understood if it elicits anappropriate action or response, and this might even be possible without
recognizing all of its words.
Understanding speech is more difficult than understanding text because
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17

Speech Recognition and Understanding Systems

there is the additional problem of processing the speech waveform to extract
the words being uttered. Speech, as it is captured by a microphone, isconverted into an electronic signal or waveform, which can be displayed on an
oscilloscope. In Fig. 17.1, I show a waveform generated by a person saying“This is a test.” This diagram shows the amplitude (voltage) of the speech
signal plotted against time. The sections of the waveform corresponding to thewords are demarcated by the boxes at the top of the diagram. The boxes atthe bottom show acoustical elements of these words, which are called “phones.”

Figure 17.1: A speech waveform. (Used with permission of Gunish Rai Chawla.)
In general, phones are the sounds that correspond to vowels or
consonants. English speech is thought to be composed of forty or so different
phones. Special alphabets have been devised to represent phones. One is theInternational Phonetic Alphabet (IPA), which contains the phones of all
known languages. IPA uses several special characters that do not havestandard computer (ASCII) codes. Another, containing just the phones usedin American English and using only standard characters, is ARPAbet, which
was developed during speech-processing research sponsored by DARPA. The
phones boxed in Fig. 17.1 use the ARPAbet notation. The table in Fig. 17.2
shows the ARPAbet phones and sample words containing them.
Early speech recognition systems attempted first to segment the speech
waveform into its constituent phones and then to assemble the phones intowords. To do so, the speech signal was first digitized, and various parameters,such as the frequency or pitch, were extracted. The ways in which the valuesof these parameters change in time were used to segment the waveform into
units containing phones. Using dictionaries that associate the values ofwaveform parameters with phones and phones with words, the waveform was
finally converted into text. The process sounds simple but it is actually quite
complex because, among other things, the beginnings and endings of spoken
words and their component phones overlap in complex patterns, and people
often pronounce the same words in different ways. For example, the word
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17.2

Speech Processing

Figure 17.2: Consonants and vowels in the ARPAbet phonetic alphabet.
“you” might be pronounced differently in “are you” [aa r y uw] and “did you”
[d ih d jh uh].
Attempts to recognize speech began at Bell Laboratories as far back as
the 1930s. In 1952, engineers at Bell Labs built a system for recognizing thenumbers “zero” through “nine” uttered by a single speaker.1 Other work wasdone in the 1950s and 1960s at RCA Laboratories, at MIT, in Japan, in
England, and in the Soviet Union.2 Work accelerated in the 1970s, some ofwhich I’ll describe next.

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Speech Recognition and Understanding Systems

17.2

The Speech Understanding Study Group

Larry Roberts, who went to DARPA in late 1966 as “chief scientist” in the
Information Processing Techniques Office (IPTO) and later became its
director, became intrigued with the idea of building systems that could
understand speech. Cordell Green, by then serving as a lieutenant in the U.S.
Army, was assigned to IPTO under Roberts in early 1970 and was put incharge of funding and monitoring AI research projects. According to Green,
Roberts told him “Do a feasibility study on a system that can recognize
speech.”3
So, at the end of March 1970, Green organized a meeting at Carnegie
Mellon University of several of the DARPA contractors and others interested
in speech processing to discuss the feasibility of speech understanding bycomputer. Among those attending the meeting were researchers from SDC,
Lincoln Laboratory, MIT, CMU, SRI, and BBN. It was decided at the meeting
to form a “study group” to assess the state of the art and to makerecommendations concerning the launching of a major DARPA-supported
project in speech understanding. The group was to be chaired by Allen Newellof CMU.4
During the March meeting, Roberts was persuaded to talk about the kindof speech-understanding system that he had in mind. According to the study
group’s rendition of his remarks, Roberts was thinking about a system that
could accept continuous speech from many cooperative users, over a telephone,using a vocabulary of 10,000 words, with less than 10% semantic error, in a
few times real time, and be demonstrable in 1973.
The study group held its first meeting at BBN on May 26 and 27, 1970.
At that meeting, the group considered some specific tasks that theunderstanding system would be able to engage in. Among these wereanswering questions about data management, answering questions about theoperational status of a computer, and consulting about a computer operating
system.
A final meeting of the group was held at SDC in Santa Monica on July
26–28, 1970. The recommendation of the group (in brief) was to aim for asystem that could accept continuous speech, from many cooperative speakersof the “general American dialect,” over a good quality microphone (not atelephone), using a selected vocabulary of 1,000 words (not 10,000 words),
with a “highly artificial syntax,” involving tasks such as data management orcomputer status (but not consulting), with less than 10% error, in a few times
real time, and be demonstrable in 1976 (not 1973) with a moderate chance of
success. A final report of the group was drafted after the meeting, delivered to
DARPA, and eventually published in 1973.5
Although there had been much prior research in speech processing bycomputer (nicely summarized in the study group’s report), not everyone was270
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17.3

The DARPA Speech Understanding Research Program

optimistic about success. One naysayer was John R. Pierce, a researcher at
Bell Laboratories, where much speech-recognition work had already takenplace. In 1969, Pierce wrote a letter6 to the Journal of the Acoustical Societyof America in which he claimed that most people working on speech
recognition were acting like “mad scientists and untrustworthy engineers. The
typical recognizer gets it into his head that he can solve ‘the problem.’” In thesame letter, though, he also wrote that. . . performance would continue to be very limited unless therecognizing device understands what is being said with somethingof the facility of a native speaker (that is, better than a foreigner
who is fluent in the language). If this is so, should people continue
work toward speech recognition? Perhaps this is for people in thefield to decide. [My italics.]

17.3

The DARPA Speech Understanding
Research Program

In fact, people in the field did decide. In October 1971, Roberts established at
DARPA a five-year Speech Understanding Research (SUR) program basedlargely on the study group’s report. Its budget was about $3 million per year.
CMU, Lincoln Laboratory, BBN, SDC, and SRI were contracted to build
systems. Complementary research would be performed at Haskins
Laboratories, the Speech Communications Research Laboratory, the Sperry
Univac Speech Communications Department, and the University of California
at Berkeley.
In 1976 some of these efforts resulted in systems that were demonstrated
and tested against the program’s goals. CMU developed two of these, HARPYand HEARSAY-II. BBN produced HWIM (Hear What I Mean). SRI and SDC
formed a partnership in which SDC developed the acoustic processing
components and SRI developed the parsing and semantic components.
However, the SDC effort ran into difficulties with computer access, so the
combined SRI/SDC system was never formally tested. I’ll briefly summarize
the BBN work and then describe the CMU work in more detail.7

17.3.1Work at BBN

SPEECHLIS was the first speech understanding system developed at BBN. It

was designed to answer spoken questions about the moon rocks database (theone used in BBN’s earlier LUNAR system). It was rather slow and was not
systematically tested.8

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Speech Recognition and Understanding Systems

HWIM was designed to be a travel budget manager’s automated assistant
and was able to respond to spoken questions such as “How much is left in the
speech understanding budget?”9 In its final version, HWIM was tested on two
versions, each of sixty-four different utterances by three male speakers.
Thirty-one of these sentences had previously been used by the system as it was
being designed, so there might have been some implicit (if unintentional)
built-in extra capability for dealing with those sentences. The sentences
ranged in length from three to thirteen words. HWIM was able to respondcorrectly to 41% of the sentences and “close” to correctly to 23% more ofthem. The system did not respond at all to 20% of the sentences. Althoughboth SPEECHLIS and HWIM pioneered new and important methods in speech
understanding, HWIM’s performance was generally regarded as not meeting theoriginal DARPA objectives. (Their designers claimed that the test was notindicative of HWIM’s potential and that they could have done better withmore time.)

17.3.2Work at CMU

In 1969 Raj Reddy left Stanford to become a faculty member at Carnegie
Mellon University. One of the first speech systems he and colleagues worked
on at CMU was called HEARSAY (later renamed HEARSAY-I).10 It used anumber of independent computational processes to recognize spoken moves inchess from a given board position, such as “king bishop pawn moves to bishop
four.” It was during the early stages of this work, that DARPA formed the
Speech Understanding Study Group and initiated work in speech
understanding. A public demonstration of HEARSAY-I recognizing connectedspeech was given in June 1972.
Three different speech recognition and understanding systems weredeveloped at CMU under the umbrella of the DARPA speech understanding
research effort. These were DRAGON, HARPY, and HEARSAY-II, and they allcontributed important AI ideas. Work on these systems was led by Allen
Newell, Raj Reddy, James Baker, Bruce Lowerre, Lee Erman, Victor Lesser,
and Rick Hayes-Roth.11
A. DRAGON
During the early days of CMU’s speech understanding research, a Ph.D.
student, James K. Baker, began work on a speech understanding system hecalled “DRAGON.”12 (According to Allen Newell, the name DRAGON was
meant “to indicate that it was an entirely different kind of beast from the AI
systems being considered in the rest of the speech effort.”13 ) Like HEARSAY-I,
DRAGON was designed to understand sentences about chess moves.
DRAGON introduced powerful new techniques for speech processing –elaborations of which are used in most modern speech recognition systems. It

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17.3

The DARPA Speech Understanding Research Programused statistical techniques to make guesses about the most probable strings ofwords that might have produced the observed speech signal. It was an earlyexample of the importation of probabilistic representations and associatedcomputational methods into AI. We’ll see a good deal more of these in later
chapters.
I’ll try to explain the main ideas without using much mathematics. Using
the notation introduced in Section 2.3.2, suppose we let x stand for a string ofwords and y stand for the speech waveform that is produced when x is spoken.
(Actually, we’ll let y be some information-preserving representation of the
waveform in terms of its easily measurable properties such as the amounts ofenergy the waveform contains in various frequency bands. For simplicity, I’ll
continue to call y a waveform, even though I mean its representation, whichmight be different for different speech understanding systems.)
Because the same speaker may say the same words somewhat differently
on different occasions, and different speakers certainly will say themdifferently, the word string x does not completely determine what the speech
waveform y will be. That is, given any x, we can only say what theprobabilities of the different y’s might be. As described in Chapter 2, theseprobabilities are written in functional form as p(y | x) (read as the“probability of y given x”). In principle, the actual values of p(y | x) for someparticular x, say x = X, could be estimated, for example, by having a numberof speakers utter the word string X many different times and tabulating how
frequently different speech waveforms y occur. This process would have to berepeated for many different word strings. DRAGON avoided this tedious
tabulation in a way to be explained shortly.
For speech recognition, however, we want to know the probability of aword string x, given the speech signal y, so that we can select the mostprobable x. That is, we want p(x | y) rather than p(y | x). We could use
Bayes’s rule as before, to produce the desired probability as follows:p(x | y) = p(y | x)p(x)/p(y).
Upon observing a particular waveform, say y = Y , here is how we would
use the quantities in this formula to decide what word string x was mostprobably uttered:
1. Look up all the values of p(Y | x) for all of the values of x we areconsidering. (We don’t have to do this for all possible strings of words,
but only for those allowed by the vocabulary and syntax of thespecialized area appropriate to the speech understanding task – chessmoves in the case of DRAGON.)
2. Multiply each of these values by p(x). (The decision should be biased infavor of likely word strings.)273
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17

Speech Recognition and Understanding Systems
3. Select that x, say X, for which the product is the largest. [We can ignore
dividing by p(y) because its value does not affect which p(x | Y ) islargest.]Although this process would work in principle, it is quite impracticalcomputationally. Instead, DRAGON and other modern speech-recognition
systems exploit the hierarchical structure involved in what is presumed to be
the way a speech waveform is generated. There are various levels in thishierarchy that could be identified. To oversimplify a bit, at the top of thehierarchy a given semantic idea is expressed by a string of words obeying thesyntactic rules of the language. The string of words, in turn, gives rise to astring of phones – the phonetic units. Finally, the phone string is expressed bya speech waveform at the bottom of the hierarchy.
At each level, we have a sequence of entities, say, x1 , x2 , . . . xn , producing
a sequence of other entities, say, y1 , y2 , . . . , yn . We can diagram the process asshown in Fig. 17.3.

Figure 17.3: Two hierarchical levels in speech generation.
The DRAGON system made some simplifying assumptions. It assumed
that each xi in the sequence of x’s is influenced only by its immediate
precedent, xi−1 , and not by any other of the xi . This assumption is called theMarkov assumption. [Andrey Andreyevich Markov (1856–1922) was a Russianmathematician. He used (what was later called) a Markov model to analyze
the statistics of a sequence of 20,000 Russian letters taken from Pushkin’s
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17.3

The DARPA Speech Understanding Research Program

novel Eugene Onegin.14 Markov models are used extensively in physics andengineering. Google uses the Markov assumption, for example, in its
computation of page rank.] Of course, we know that each word in a sequence
depends on more than just the immediately preceding word. Even so, theMarkov assumption makes computations simpler and still allows good
performance.
Further, it was assumed that each yi was influenced only by xi and xi−1 .
All of these “influences” are probabilistic. That is, given quantities like x3 andx4 , for example, the value of y4 is not completely determined. One can only
say what the probabilities of the values of y4 might be; these are given by thefunctional expression p(y4 | x3 , x4 ). Probability values for the y’s are thus
given by what is called a “probabilistic function of a Markov process.” To
produce estimates of these probabilities, statistics can be gathered during a“learning process” (in which a speaker utters a training set of sentences).
DRAGON combined these separate levels into a network consisting of ahierarchy of probabilistic functions of Markov processes. Entities representing
segments of the speech waveform were at the bottom, entities representing
phones were in the middle, and entities representing words were at the top. At
each level, Bayes’s rule was used to compute probabilities of the x’s given they’s. Because only the speech waveform at the bottom level was actuallyobserved, the phones and words were said to be “hidden.” For this reason, theentire network employed hidden Markov models (HMMs). DRAGON was thefirst example of the use of HMMs in AI. They had been developed previously
for other purposes.15

Using this network, recognition of an utterance was then achieved byfinding the highest probability path through the network. Computing theprobabilities for syntactically valid word sequences, given the sequence ofsegments of the observed speech waveform, is a problem that is similar to one
I described earlier, namely, computing the confidences of strings of characterson FORTRAN coding sheets (see p. 101). Again, a method based on dynamic
programming was used. As Baker wrote, “The optimum path is found by analgorithm which, in effect, explores all possible paths in parallel.”16 At the endof the process, the most probable syntactically legal string of words is
identified. The mathematical operations for making these computations are
too complex to explain here, but they can be performed efficiently enough tomake speech recognition practical.
Although the DRAGON system was not among those that were finallytested against DARPA’s speech understanding system objectives, Baker
claimed that its initial results were “very promising” and that in “its first testwith live speech input, the system correctly recognized every word in all nine
sentences in the test.”17 DRAGON became the basis for a commercial product,“Dragon Naturally Speaking,” first developed and marketed by Dragon
Systems, a company founded by Baker and his wife, Janet.

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17

Speech Recognition and Understanding Systems

B. HARPYHARPY was a second system produced at CMU under DARPA’s speech

understanding research effort. Bruce T. Lowerre designed and implemented
the system as part of his Ph.D. research.18 HARPY combined some of the ideas
of HEARSAY-I and DRAGON. Like DRAGON, it searched paths through a
network to recognize a spoken sentence, but it did not annotate the links
between nodes in the network with transition probabilities like DRAGON did.
Like HEARSAY-I, HARPY used heuristic search methods.
Versions of HARPY were developed for understanding spoken sentences
about several different task areas. The main one involved being able to answer
questions about, and to retrieve documents from, a database containing
summaries (called “abstracts”) of AI papers. Here are some examples:“Which abstracts refer to theory of computation?”“List those articles.”“Are any by Feigenbaum and Feldman?”“What has McCarthy written since nineteen seventy-four?”HARPY could handle a vocabulary of 1,011 words. Instead of using a grammar

with the conventional syntactic categories such as Noun, Adjective, and so on,
HARPY used what is called a “semantic grammar,” one that has expandedcategories such as Topic, Author, Year, and Publisher that were
semantically related to its subject area, namely, data about AI papers.
HARPY’s grammar was limited to handle just the set of sentences aboutauthors and papers that HARPY was supposed to be able to recognize.
The network was constructed from what were called “knowledge sources”(KSs), which consisted of information needed for the recognition process.19
The first of these encoded syntactic knowledge about the grammar.
A second knowledge source used by HARPY described how each word in
HARPY’s vocabulary might be pronounced. And, because in spoken language
word boundaries overlap in ways that depend on the words involved, successfulrecognition requires a third knowledge source dealing with such phenomena. A
fourth knowledge source specified the phones involved in the pronunciation ofwords and transitions between words.
HARPY combined all of this knowledge into a giant network of phones
representing all the possible ways that syntactically legal sentences might bespoken. Each “phone node” in the network was paired with a representation ofa segment of a speech waveform, called a “spectral template,” expected to be
associated with that particular phone. These templates were obtained initiallyby having a speaker read about 700 sentences. They could be “tuned” for a
new speaker by having the speaker read about 20 selected sentences during a“learning” session. A partial network of phones is shown in Fig. 17.4 to276
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17.3

The DARPA Speech Understanding Research Programillustrate the general idea. HARPY’s actual network had 15,000 nodes. The
network is for those parts of the sentences that begin with “Tell me. . . ” and“Give me. . . .” The symbols inside the nodes represent phones, using
DRAGON’s notation for them. Arrows represent possible transitions from one
phone to the next. Note that there are multiple paths, corresponding todifferent ways to pronounce the words.

Figure 17.4: A partial network of the phones that might occur in a spoken
sentence.
To recognize the words in a spoken sentence, the observed speech
waveform was first divided into variable-length segments that were guessed tocorrespond to the sequence of phones in the waveform. A spectral template
was computed for each of these segments. The recognition process thenproceeded as follows: The spectral template corresponding to the first spectral
segment in the speech waveform was compared against all of the templates
corresponding to the phones at the beginning of the network. In reference toFig. 17.4, these would include comparisons against templates for –, T, G, andIH2 because they were among the nodes in the network that could be reachedin one step from the start node, namely, [. (Of course, in using the complete
network rather than just the partial example just illustrated, several morecomparisons would be made against templates of additional phone nodes
reachable in one step from the start node.) The best few matches were noted,
and the paths to these nodes were designated to be the best one-step partial
paths through the network. At the next stage, the spectral template of the277
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Speech Recognition and Understanding Systems

next waveform segment was compared against the templates of all of thosephone nodes reachable by extending the best one-step paths one more step.
Using the values of the comparisons computed so far, a set of best two-steppartial paths was identified. This process continues until the end of the
network was reached. At that time the very best path found so far could be
associated with the words associated with the nodes along that path. This
word sequence was then produced as HARPY’s recognition decision.
HARPY’s method of searching for a best path through the network can becompared with the A∗ heuristic search process described earlier. Whereas A∗
kept the entire search “frontier” available for possible further searching,
HARPY kept on its frontier only those nodes on the best few paths found so
far. (The number of nodes kept on the frontier was a parameter that could be
set as needed to control search.) HARPY’s designers called this technique“beam search” because the nodes visited by the search process were limited toa narrow beam through the network. Because nodes not in the beam were
eliminated as the process went on, it is possible that the best complete pathfound by HARPY might not be the overall best one in the network. (One of theeliminated nodes might be on this overall best path.) Even so, the path foundusually corresponded to a correct interpretation of the spoken sentence.

At the end of the DARPA speech understanding project, HARPY wastested on 100 sentences spoken by three male and two female speakers. It wasable to understand over 95% of these sentences correctly, thereby meeting
DARPA’s goal of less that 10% error. On average, HARPY executed about 30
million computer instructions to deal with one second of speech. Using a 0.4million instructions per second (0.4 MIPS) machine (a DEC PDP-KA10), it
would take over a minute to process a second of speech; although this is quite
a bit worse than real-time performance, it achieved DARPA’s goal of “a few
times real time” (if we interpret “a few” somewhat accommodatingly). To put
the real-time matter in perspective, today’s computers process billions of
instructions per second. HARPY was the only system to meet DARPA’s goals.
C. HEARSAY-II
Finally, HEARSAY-II, a redesigned and improved version of HEARSAY-I, wasperhaps the most ambitious of CMU’s speech projects.20 Like HARPY,HEARSAY-II was designed to answer questions about, and to retrieve documents
from, a database containing abstracts of AI papers. (An earlier task considered
was to retrieve wire-service news stories.) It too was limited to a vocabulary of
1,011 words and used a semantic grammar specialized to its subject area.
The first steps in HEARSAY’s processing of an utterance involved
segmenting the speech waveform and labeling the phones estimated to bepresent in each segment. HEARSAY then used a novel method of graduallybuilding these components into syllables, the syllables into words, the words
into word sequences, and finally word sequences into phrases. The phrases
278
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17.3

The DARPA Speech Understanding Research Program

were then converted into appropriate routines for accessing the database of AI
papers.21
The processing method used by HEARSAY involved a layered structure
called a “Blackboard.” The labels of the phones estimated to be present,along with numbers related to their probabilities of occurrence, were “written”
in one of the lower layers of the Blackboard. Specialized knowledge-source
routines that “knew about” how syllables were constructed from phones“read” these labels and computed guesses about what syllables were in theutterance. These guesses, along with numbers measuring their confidences or
likelihoods, were then written in the syllable layer of the Blackboard. Other
knowledge-source routines that knew about how words were constructed fromsyllables read information already on the Blackboard and wrote guesses aboutwords in the word layer of the Blackboard. And so on. HEARSAY-II had around40 of these knowledge sources. The general idea is illustrated in Fig. 17.5.

Figure 17.5: The Blackboard architecture.
In principle, a knowledge source could read or write information on any
layer of the Blackboard that was relevant to it. Moreover, it could do so inwhat is called an “asynchronous” manner – not dependent on when other279
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17

Speech Recognition and Understanding Systems

knowledge sources were doing their reading and writing. There were someknowledge sources that could write predictions about new words based onwords already written in the word layer and on information in other layers.
Knowledge sources could even write guesses about words in the word layer
based on word sequences already written (with high confidence) in thesequence layer. This process of inferring what must be present in a lower layer(even though missed by initial processing) from what (from other evidence) ispresent in a higher layer is a theme that recurs often in later AI research. As
far as I know, this extremely important AI innovation was first manifest in the
HEARSAY-II system.
According to Raj Reddy,22 one of the inventors of the Blackboard
architecture (along with Victor Lesser, Lee Erman, and Frederick
Hayes-Roth), Herbert Simon often used the word “blackboard” to describe the“working memory” component of the production system architecture he andAllen Newell were working with (see p. 577). A production system used
IF–THEN rules (called productions), which were triggered by contents of theworking memory and wrote new data in it. Reddy and team, recognizing thevariety of different sources of knowledge relevant to speech processing,
generalized the production system idea, extending the production rules intolarger programs, renaming them “knowledge sources,” and elaborated working
memory into the layered Blackboard structure.
At the end of the DARPA speech understanding project, HEARSAY-II wastested on twenty-three spoken sentences, brand new to the system, having an
average of seven words per sentence, and 81% of these were recognized
word-for-word correctly, although 91% led to the same database query as
would have a word-for-word correct sentence. HEARSAY’s designers claimed
that this performance “comes close to meeting the ambitious
goals. . . established for the DARPA program in 1971.” Although HEARSAY-II
came close the results were not quite as good as those of HARPY.
Although the Blackboard architecture is no longer used in modern speech
recognition systems, it was adopted by several other AI programs. (We’ll see
one of these later in the book.) According to Russell and Norvig, “Blackboard
systems are the foundation of modern user interface architectures.”23

17.3.3

Summary and Impact of the SUR Program

CMU’s HEARSAY-II and HARPY were demonstrated at CMU on September 8,
1976, and BBN’s HWIM was demonstrated at BBN on September 10. In asummary report of the projects, MIT’s Dennis Klatt wrote that “it is unclear
whether there are large differences in ability among [these] three systems.
However, only [HARPY] was able to meet the ARPA goals.”24
The developers of HEARSAY-II attributed HARPY’s superior performanceto three factors: its more thorough search of potential solutions (permitted by
280
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17.4

Subsequent Work in Speech Recognition

its precomputed network of all the sentences that might have been spoken), its
more thorough built-in knowledge of transition phenomena between adjacent
words, and its more thorough testing, tuning, and debugging.25
Some researchers and DARPA program managers, however, argued about
the way in which the tests were carried out and claimed that none of thesystems met the SUR program objectives. In any case, DARPA decided not tofund a proposed follow-on program. The program did show, however, thatspeech understanding was a reasonable technical goal and stimulated progressin speech processing technologies, notably in system organization, syntax andsemantics, and acoustic processing. A National Research Council reportconcluded that “DARPA’s funding of research on understanding speech has
been extremely important. . . . the results of this research have been
incorporated into the products of established companies, such as IBM and
BBN, as well as start-ups such as Nuance Communications (an SRI spinoff)and Dragon Systems. . . . The leading commercial speech-recognition programon the market today, the Dragon “NaturallySpeaking” software [now sold by
Nuance], traces its roots directly back to the work done at CMU between 1971
and 1975 as part of SUR. . . .”26

17.4

Subsequent Work in Speech Recognition

Speech recognition research was also being carried out in other laboratories
besides those that were directly involved with DARPA’s SUR program. For
example, Frederick Jelinek of the Speech Processing Group in IBM’s
Computer Sciences Department at the Thomas J. Watson Research Center in
Yorktown Heights, New York, is credited with being an early proponent of theuse of statistical methods (including hidden Markov models) in speech
recognition.27 The HMM approach was ultimately adopted by all the leading
speech recognition companies.
In 1984, DARPA began funding speech recognition work again as part ofits “Strategic Computing” program (a program that will be described in alater chapter). Participants included CMU, SRI, BBN, MIT, IBM, andDragon Systems. Among the systems developed at CMU over the next several
years, for example, were SPHINX by Kai-Fu Lee and others and JANUS, amultilingual speech recognition and translation system, by Alex Waibel andothers. (These and other systems are available as open-source software fromthe “Speech at CMU” Web page, http://www.speech.cs.cmu.edu/. The pagealso has links to many other speech recognition laboratories.)
Based on their work on DRAGON at CMU, James and Janet Baker
founded Dragon Systems in 1982. In 1997, Dragon introduced “Dragon
NaturallySpeaking,” a speech recognition program for personal computers. It
had a vocabulary of 23,000 words.28 IBM followed with ViaVoice, and othercompanies, including Microsoft, also have speech recognition software.
281
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17

NOTES

The transcription of spoken sentences to their textual equivalents is now
largely a solved problem. For example, high-quality speech recognition iscommonly employed today in many automated telephone response systems.
However, understanding natural language speech (or text) to permit generaldialogs with computer systems, for example, remains a long-term research
problem. I’ll continue my discussion of work on that problem in a later
chapter.

Notes
1. K. H. Davis, R. Biddulph, and S. Balashek, “Automatic Recognition of Spoken Digits,”
Journal of the Acoustical Society of America, Vol. 24, No. 6, pp. 627–642, 1952. [269]2. For a history of early work see B. H. Juang and Lawrence R. Rabiner, “Automatic
Speech Recognition – A Brief History of the Technology Development,” available online at
http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/
354 LALI-ASRHistory-final-10-8.pdf; or Sadaoki Furui, “50 Years of Progress in Speech andSpeaker Recognition,” available online at
http://www.furui.cs.titech.ac.jp/publication/2005/SPCOM05.pdf. [269]
3. C. Cordell Green, “AI During IPTO’s Middle Years,” in Thomas C. Bartee (ed.), Expert
Systems and Artificial Intelligence: Applications and Management, p. 240, Indianapolis:
Howard W. Sams & Co., 1988. [270]4. Other members of the group were Jeffrey Barnett of the Systems Development
Corporation, James Forgie of Lincoln Laboratory, C. Cordell Green, then a lieutenant in the
U.S. Army stationed at DARPA, Dennis Klatt of MIT, J. C. R. Licklider, then at MIT,John Munson of SRI, Raj Reddy of CMU, and William Woods of BBN. [270]5. The report was published as a special issue of the journal Artificial Intelligence: Allen
Newell et al., Speech Understanding Systems: Final Report of a Study Group, New York:
American Elsevier Publishing Co., Inc., 1973. A draft of the report is available online in the
Newell collection at http://diva.library.cmu.edu/webapp/newell/item.jsp?q=box00105/fld08162/bdl0001/doc0001/.
[270]
6. J. R. Pierce, “Whither Speech Recognition?,” Journal of the Acoustical Society ofAmerica, Vol. 46, No. 4, pp. 1049–1051, Part 2, 1969. Also see a rebuttal by Arthur Samueland Pierce’s response to Samuel and to other rebuttals in Journal of the Acoustical Societyof America, Vol. 47, No. 6, Part 2, pp. 1616–1617, 1970. [271]7. For a description of the SRI work, see Donald E. Walker (ed.), Understanding Spoken
Language, New York: Elsevier North-Holland, Inc., 1978. [271]8. For more details, see William A. Woods, “Motivation and Overview of BBN

SPEECHLIS: An Experimental Prototype for Speech Understanding Research,” IEEE
Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, pp. 2–9,
February 1975. [271]
9. See J. Wolf and William A. Woods, “The HWIM Speech Understanding System,”Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP ’77,
Vol. 2, pp. 784–787, May 1977; also (for full details) William A. Woods et al., Speech
Understanding Systems – Final Report, BBN Report No. 3438, Vols. I–V, Bolt, Beranek,
and Newman, Inc., Cambridge, MA, 1976. [272]
10. D. Raj Reddy, Lee D. Erman, and Richard B. Neely, “A Model and a System for
Machine Recognition of Speech,” IEEE Transactions on Audio and Electroacoustics, Vol.

282
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17.4

NOTES

AU-21, No. 3, pp. 229–238, June 1973; and D. Raj Reddy, Lee D. Erman, R. D. Fennell,and Richard. B. Neely, “The HEARSAY Speech Understanding System: An Example of the
Recognition Processes, in Proceedings of the 3rd International Joint Conference on Artificial
Intelligence, pp. 185–183, Stanford, CA, August 1973. [272]11. For background on the speech processing work at CMU during this period, see Lee D.
Erman, “Overview of the HEARSAY Speech Understanding Research,” SIGART Newsletter,
No. 56, pp. 9–16, February 1976. [272]12. James K. Baker, “Stochastic Modeling as a Means of Automatic Speech Recognition,”
doctoral dissertation, Computer Science Department, Carnegie Mellon University,
Pittsburgh, PA, 1975, and James K. Baker, “The DRAGON System – An Overview,” IEEE
Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, February
1975. [272]
13. Allen Newell, “Harpy, Production Systems and Human Cognition, in Ronald A. Cole(ed.), Perception and Production of Fluent Speech, Hillsdale, NJ: Lawrence Erlbaum
Associates, 1980. Available online as Carnegie Mellon University Technical Report
CMU-CS-78-140 at http://diva.library.cmu.edu/webapp/newell/item.jsp?q=box00089/fld06145/bdl0001/doc0001/.
[272]
14. For a translation see A. A. Markov, “An Example of Statistical Investigation of the Text
Eugene Onegin Concerning the Connection of Samples in Chains,” Science in Context, Vol.
19, No. 4, pp. 591-600, 2006. [275]15. See L. E. Baum and J. A. Eagon, “An Inequality with Applications to Statistical
Estimation for Probabilistic Functions of a Markov Process and to a Model for Ecology,”
Bulletin of the American Medical Society, Vol. 73, pp. 360–363, 1967. Baker credits Baumwith introducing him to the theory of a probabilistic function of a Markov process. [275]16. James K. Baker, “The DRAGON System – An Overview,” IEEE Transactions on
Acoustics, Speech, and Signal Processing, Vol. ASSP-23, No. 1, p. 24, February 1975. [275]17. Ibid, p. 29. [275]18. Bruce T. Lowerre, “The HARPY Speech Recognition System,” doctoral dissertation,
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, April 1976.
[276]
19. I am basing my description of HARPY on Bruce Lowerre and Raj Reddy, “The HARPY
Speech Understanding System,” Trends in Speech Recognition, Prentice Hall. Reprinted in
A. Waibel and K. Lee (eds.), Readings in Speech Recognition, pp. 576–586, San Mateo, CA:
Morgan Kaufmann Publishers, Inc., 1990. [276]
20. Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, and D. Raj Reddy, “TheHEARSAY-II Speech-Understanding System: Integrating Knowledge to Resolve
Uncertainty,” Computing Surveys, Vol. 12, No. 2, June 1980. [278]21. For a detailed summary of how HEARSAY processed an example sentence, see ibid.
[279]
22. Telephone conversation, August 14, 2008. [280]
23. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second
edition, p. 580, Upper Saddle River, NJ: Prentice Hall, 2003. [280]
24. Dennis H. Klatt, “Review of the ARPA Speech Understanding Project,” Journal of the
Acoustical Society of America, Vol. 62, No. 2, pp. 1345–1366, December 1977. [280]
25. Lee D. Erman, Frederick Hayes-Roth, Victor R. Lesser, and D. Raj Reddy, op. cit..
[281]
26. Funding a Revolution: Government Support for Computing Research, Chapter 9,
Committee on Innovations in Computing and Communications: Lessons from History,

283
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17

NOTES

Computer Science and Telecommunications Board, Commission on Physical Sciences,
Mathematics, and Applications, National Research Council, Washington, DC: National
Academy Press, 1999. Available online at
http://books.nap.edu/openbook.php?record id=6323&page=15. [281]27. See, for example, Frederick Jelinek, “Continuous Speech Recognition by Statistical
Methods, Proceedings of the IEEE, Vol. 64, No. 4, pp. 532–556, April 1976. [281]
28. Dragon NaturallySpeaking is now available through Nuance. [281]284
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18.1

Chapter 18

Consulting Systems
18.1

The SRI Computer-Based Consultant

As my colleagues and I at SRI cast about for ways to continue our planningand vision research we had been doing under the “Shakey the Robot” project,while satisfying DARPA’s interest in militarily relevant applications, we hit
upon the problem of equipment maintenance, repair, and training. We pointed
out that any technology that could reduce expenditures for these items andlessen the need for utilizing scarce human experts would be extremely
important to the military. Furthermore, we said, this need “cannot be satisfied
merely by writing more and better manuals. A sophisticated computer systemseems to us essential.”
We proposed to begin work on this problem by developing anenvironment in which an apprentice repair person would be given advice about
how to take apart and reassemble a small air compressor.1 The advice would
be generated (as needed by the apprentice) by a planning and execution
system that would give suggestions about what to do next. The apprentice
could ask for advice, using a predefined small vocabulary, which the system
would analyze with state-of-the art speech recognition equipment, and thenthe system would answer, using a speech generation module. The system
would also employ a machine-vision system to help keep track of progress andto observe components pointed to by the apprentice. The proposed setup is
shown in Fig. 18.1.
Although the project did not last long enough to achieve all of its goals,
an early version of a “Computer-Based Consultant” (CBC) system wasdemonstrated in which the apprentice (played by Georgia Sutherland, one ofthe project team members) was asked to reassemble an air compressor (a Sears
Model 17209, 1/2 HP compressor) that had previously been partially taken
apart. Requests and responses by both the system and the apprentice were285
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18Consulting Systems

Figure 18.1: Proposed arrangement for helping an apprentice. (Used with permission of SRI International.)
spoken. Here is a sample of one of the interactions with the system (with mycommentary in parentheses):2
System: Please assemble air compressor.
Apprentice: How?
(The apprentice asks for advice, and the system, using Sacerdoti’s
NOAH system I described previously, creates a hierarchicalassembly plan.)
S: Install pump.
(The system mentions the first action in the high-level plan NOAH
created.)
A: OK.
(The apprentice knows how to install the pump and indicates thatshe does not need further advice about how to do it. At this stage
286
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18.1

The SRI Computer-Based Consultantof the CBC project, the system assumed, without visual checking,that the apprentice had completed this first step.)
S: Install pump brace.
(The system mentions the second action in the high-level plan.)
A: How?
(The apprentice does not know how to achieve this step and asks
for advice.)
S: Connect pump brace to pump.
(The system mentions the first of the more detailed steps in thehigh-level action for installing the pump brace.)
A: OK.
(The apprentice knows how to achieve this step.)
S: Connect pump brace to belt-housing frame.
(The system mentions the next of the more detailed steps in thehigh-level action for installing the pump brace.)

This type of interaction continues until the task is completed.
The system kept track of the current state of assembly by using theprocedural net created by the NOAH system (see p. 230). If the apprenticewere to have asked “Why?” at any step, the system would have used theprocedural net to construct an answer. The answer might have beensomething like “you must do x because it is one of the actions involved indoing y,” or “you must do x because you must do it first in order to do thenext action, z.” Also, the apprentice could have asked for help in locating oneof the major parts of the air compressor, and the system would have used its
laser pointer to show her. Part of the procedural net computed for thisexample is shown in Fig. 18.2.
The CBC project also provided an opportunity for SRI’s NLP group to
try out some ideas they were developing about generating and understanding
the sentences used in conversations. In the CBC project, the apprentice andthe person giving advice are participating in a dialog about a task, namely, thetask of working on an air compressor. The structure of the task, as modeledby the procedural network generated by NOAH, provided important pragmatic
information useful for sentence understanding. This information was exploited
in a system called TDUS (an acronym for Task Dialog Understanding System),
which could engage in more complex dialogs than the spoken one justillustrated as it guided an apprentice through an assembly task.3 TDUS
integrated the NOAH planning system with a natural language understanding
system (having syntactic, semantic, and pragmatic components) to allow
text-based conversations with the apprentice.
I’ll use an example taken from a paper about TDUS to illustrate the rolethat the task structure plays in sentence understanding.4 Consider thefollowing sentences:287
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18Consulting Systems

Figure 18.2: Part of a procedural net for assembling an air compressor. (Usedwith permission of SRI International.)
Speaker 1: Why did John take the pump apart?
Speaker 2: He did it to fix it.
Interpreting the referents of the italicized words in the second sentence isaided by referring to the task context established by the first sentence. “He”
refers to John, “did it” refers to the disassembly task, and the second “it”
refers to the pump. TDUS makes extensive use of the shifting “context” andgoals of the dialog. As the developers of TDUS wrote,5As a dialog progresses, the participants continually shift their focus
of attention and thus form an evolving context against whichutterances are produced and interpreted. A speaker provides ahearer with clues of what to look at and how to look at it – what
to focus upon, how to focus upon it, and how wide or narrow thefocusing should be. We have developed a representation for
discourse focusing, procedures for using it in identifying objects
referred to by noun phrases, and procedures for detecting andrepresenting shifts in focusing.
288
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18.1

The SRI Computer-Based Consultant(The words“utterance,” “speaker,” and “hearer” are not to be taken literally.
TDUS processed text-based language, not spoken language. In NLP research,these words are often used in a generalized sense to refer to sentences, sentence
generators, and sentence receivers, whatever the medium.)
Focus was the main interest of Barbara J. Grosz (1948– ; Fig. 18.3), who
continued work on that topic and its role in NLP as a professor at Harvard
University. Besides the mechanisms for dealing with contexts, goals, and focus,
TDUS contained a grammar, called DIAGRAM,6 for recognizing many of thesyntactic structures of English, means for representing and reasoning aboutprocesses and goals, and a framework for describing how different types ofknowledge interact as the dialog unfolds.

Figure 18.3: Barbara J. Grosz. (Photograph courtesy of photographer Tony
Rinaldo.)A demonstration of the CBC system, like the one I described a few
paragraphs ago, was given at SRI on April 23, 1975, for J. C. R. Licklider [who289
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18Consulting Systems

had returned to head IPTO in 1973]. Recollecting impressions of his visit,
Licklider later said7
The second time I was in DARPA, there were very impressive AI
systems dealing with maintenance of equipment. I remember SRI
had a program that described how to take a pump apart and put itback together. That’s not a terribly complicated device, but it waspretty impressive to see a computer that obviously understood all
the parts of the pump and how they worked together.
Because of Licklider’s encouragement, we were optimistic about
continuing the CBC project and made plans for a system that would diagnose
and give advice about repairing a military jeep engine. Unfortunately, one of
DARPA IPTO’s new program managers, Colonel David Russell, was notbuying it. After visiting SRI a few days before Licklider’s April 23 visit,
Russell sent an e-mail to Licklider saying8
I must admit to considerable concern over the SRI program,particularly in light of the management pressures on the AI
program. Looking at the projected program plan that Nils has
been working on, I see a 2.2M dollar program over the next three
years with the aim of developing an experimental CBC for a jeep.
. . . I can’t see how it can be defended as a near-term application. . .
While it may be difficult, I would suggest that you give serious
thought to terminating the CBC program when it completes the
air-compressor phase and redirect SRI to more Defense oriented
applications or pass their work to NSF. I appreciate that this isheresy, but that is how I saw the situation.
I didn’t directly discuss these comments with Nils although I did
ask what he would do if the program were terminated. I may haveformed a negative view based on an incorrect understanding of the
program, and I didn’t want to upset the SRI group without yourviews of the program.
Later that year, Russell replaced Licklider as Director of DARPA IPTO
and terminated the CBC project. (Work on TDUS, however, continued under
NSF support.) DARPA support for the SRI group was subsequently“redirected” to natural language interfaces to databases (which I’ll describe
later) and to “image understanding” to aid photo interpreters. Some of us
chose instead to seek non-DARPA support to work on computer-based
consulting systems. Ongoing work at Stanford University on so-called expert
systems encouraged us in that direction.

290
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18.2

Expert Systems

18.2

Expert Systems

18.2.1MYCIN

Stanford’s HEURISTIC DENDRAL project demonstrated the power of endowing
computers with expert knowledge about chemistry and spectroscopy.
Feigenbaum, Lederberg, and Buchanan, the senior members of the project,believed that a similar approach might work on a medical problem. In theearly 1970s Buchanan began talking with Stanley Cohen, Chief of ClinicalPharmacology at Stanford’s Medical School, about Cohen’s computerized druginteraction warning system called MEDIPHOR. Around the same time, Edward(Ted) Shortliffe (1947– ; Fig. 18.4), a Stanford Medical School student, took a
Stanford course on AI and also became an assistant on Cohen’s project.
Together, Shortliffe, Buchanan, and Cohen conceived the idea of building acomputer program that would consult with physicians about bacterial
infections and therapy. Shortliffe named the program MYCIN, a common suffix
for antibacterial agents. Such a program would need to contain diagnostic andtreatment knowledge of experts in infectious diseases.

Figure 18.4: Bruce Buchanan (left) and Ted Shortliffe (right). (Photographcourtesy of Ed Feigenbaum.)
The first question in developing MYCIN was how to represent expert
knowledge. Shortliffe and Buchanan thought that something similar to the291
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18Consulting Systems“IF–THEN rules” used in DENDRAL would be appropriate. When diagnosing
what disease might be causing certain symptoms, as well as in prescribing
therapy, physicians appear to be using a kind of IF–THEN reasoning: IF thesymptoms are such-and-such, THEN the cause is likely to be so-and-so. The
knowledge behind this sort of reasoning is based on experience with cases aswell as on scientific knowledge about diseases. It was believed that the
IF–THEN knowledge needed by the program could be obtained byinterviewing the appropriate medical experts who already thought in thoseterms.
Interestingly, IF–THEN reasoning about medical matters has a long
history. Summarizing part of a book by J. H. Breasted9 about surgicalknowledge contained in an ancient Egyptian papyrus, Robert H. Wilkins wrote“The Edwin Smith Surgical Papyrus, dating from the seventeenth century
B.C., is one of the oldest of all known medical papyri.”10 (The papyrus wasbought in a Luxor antique shop by Edwin Smith in 1882.) Wilkins goes on tomention several rules from the papyrus, one of which is the following:
Case Thirty
Title: Instructions concerning a sprain in a vertebra of his neck.
Examination: If thou examinest a man having a sprain in avertebra of his neck, thou shouldst say to him: “look at thy twoshoulders and thy breast.” When he does so, the seeing possible tohim is painful.
Diagnosis: Thou shouldst say concerning him: “One having a
sprain in a vertebra of his neck. An ailment which I will treat.”
Treatment: Thou shouldst bind it with fresh meat the first day.
Now afterward thou shouldst treat [with] ywrw (and) honey every
day until he recovers.
Two other experts who joined in the development of the nascentdiagnostic and treatment system were Thomas Merigan, Chief of the
Infectious Disease Division at Stanford, and Stanton Axline, a physician inthat division. In their summary11 of the history of the project, Buchanan andShortliffe credit Axline with coming up with the name MYCIN for the program.
The team submitted a successful grant application to the National
Institutes of Health in October of 1973. Shortliffe decided to combine his
medical studies with work toward a Computer Science Ph.D. based on MYCIN.
Since the version of LISP he wanted to use (BBN-LISP, soon to become
INTERLISP) was not available at Stanford, he used the SRI AI group’s PDP-10
computer.
The IF–THEN rules elicited from the medical experts usually were
hedged with uncertainty. Buchanan and Shortliffe mention that “Cohen and
Axline used words such as ‘suggests’ or ‘lends credence to’ in describing the292
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Expert Systems

effect of a set of observations on the corresponding conclusion. It seemed clearthat we needed to handle probabilistic statements in our rules. . . ”
After wrestling with various ways to use probabilities to qualify MYCIN’s
IF–THEN rules, Shortliffe finally decided on using the somewhat ad hoc
notion of “certainty factors.”12Here, for example (in both its internal LISP form and its English
translation), is one of MYCIN’s rules:
RULE036
PREMISE: ($AND (SAME CNTXT GRAM GRAMNEG)(SAME CNTXTM MORPH ROD)(SAME CNTXT AIR ANAEROBIC))ACTION: (CONCLUDE CNTXT IDENTITY BACTEROIDES TALLY 0.6)
IF: 1) The gram stain of the organism is gramneg, and
2) The morphology of the organism is rod, and
3) The aerobicity of the organism is anaerobic
THEN: There is suggestive evidence (0.6) that the identityof the organism is bacteroides
The 0.6 in this rule is meant to measure the expert’s “degree of belief” inor “certainty” about the conclusion. Shortliffe thought that a degree of belief
was not the same as a probability assessment because, among other things, henoted that the experts who provided Rule 036 did not necessarily think thatthe probability of the organism not being bacteroides would be 0.4. The
original MYCIN system had 200 such rules. By 1978, it had almost 500.
MYCIN’s rules were usually evoked in a backward-reasoning fashion. For
example, a rule of the form “IF x1 and x2, THEN y” would be used if thesystem’s overall goal was to conclude y. The use of this rule would lead to theuse of rules whose “THEN” parts were either x1 or x2. At the end of a chainof rules, a physician user of the system (or a database) would be asked tosupply information about the “IF” part. So, if MYCIN were trying to establishthat the identity of an organism was bacteroides, RULE036 would be used andthe physician (or database) would be asked if the gram stain of the organism isgramneg and so on.13
MYCIN was configured as a “consulting system.” That is, it interactedwith a physician user who supplied information about a specific patient. The
use of rules and rule-chaining allowed the system to provide “explanations” forits reasoning. For example, after a query to the user evoked by Rule 036, if theuser asked “Why did you ask whether the morphology of the organism is rod,”the system would reply (in English) something like “because I am trying todetermine whether the identity of the organism is bacteroides.”So, how did MYCIN do at its primary task of recommending therapy?
293
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18Consulting Systems

Shortliffe and colleagues conducted several evaluations in which physicians
were asked to compare MYCIN’s recommendations with their own for several
patients. Their major conclusion was that “Seventy percent of MYCIN’s
therapies were rated as acceptable by a majority of the evaluators.” They also
noted, by the way, that “75% is in fact better than the degree of agreementthat could generally be achieved by Stanford faculty being assessed under thesame criteria.”14
One of MYCIN’s innovations (as contrasted with DENDRAL, say) was thatits reasoning process (using the rules) was quite separate from its medical
knowledge (the rules themselves). Thus, it became common to divide theprogram into two parts, namely, the “inference engine” for applying rules andthe “knowledge base” of rules. In principle, new rules could be added without
having to change the inference engine. This division is shown in Fig. 18.5.
This separation suggests that one could construct expert systems for otherapplications simply by replacing the medical knowledge with some otherknowledge base without having to change the inference engine. William van
Melle implemented a system he called EMYCIN (“E” for “empty”) for doing
just that.15 A system designer along with experts in some field, X, could
interact with EMYCIN to produce IF–THEN rules for field X. Using its built-in
inference engine, EMYCIN could then use these rules to provide advice to a userof the system during a consultation. EMYCIN was used to build several
different expert systems in fields as diverse as tax planning and mechanicalstructural analysis.

Figure 18.5: The structure of a MYCIN-style expert system.
Researchers soon discovered that a minor variation of the certainty factors
used by MYCIN and EMYCIN was equivalent to using probabilities instead.
This linkage to probability theory implied consequences that neither MYCIN
nor EMYCIN could escape. In particular, their reasoning was consistent withprobability theory only under some rather restrictive assumptions about how
rules were used. As Russell and Norvig point out, if these assumptions aren’t
294
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Expert Systems

met “certainty factors could yield disastrously incorrect degrees of belief
through overcounting of evidence. As rule sets became larger, undesirable
interactions between rules became more common, and practitioners found thatthe certainty factors of many other rules had to be ‘tweaked’ when more rules
were added.”16 Modern methods use more sophisticated probabilistic
techniques, as we shall see in a later chapter.
Even so, the success of MYCIN and the various EMYCIN programs led tothe development of many more expert systems, some based on EMYCIN andsome using their own specific approaches. As Allen Newell wrote in his
introduction to a book by Buchanan and Shortliffe, “MYCIN is the original
expert system that made it evident to all the rest of the world that a new niche
had opened up. . . . MYCIN epitomized the new path that had been created.
Thus, gathering together the full record of this system and the internal historyof its development serves to record an important event in the history of AI.”17

18.2.2PROSPECTOR

Inspired by Shortliffe’s work with MYCIN, some of us at SRI began
investigating nonmedical applications of expert systems. One area weconsidered was “integrated pest management” in which knowledge about crops
and their insect pests could be used to mitigate the effects of insect predation
with minimal use of chemical insecticides. Although proposals were written
and some interest was shown by scientists in the U.S. Department ofAgriculture and at the Environmental Protection Agency, the idea wasabandoned when the proposals went unfunded.
Peter Hart and Richard Duda eventually focused on systems for providing
advice to explorationists about possible “hard-rock” mineral deposits.18 Hart
had some early discussions with John Harbaugh, a petroleum engineering
professor at Stanford, and with Alan Campbell, one of Harbaugh’s graduate
students. (Alan Campbell was the son of the late Neal Campbell, aworld-famous explorationist who had discovered what was possibly the largest
lead–zinc deposit in the world. Alan spent much of his youth in mining
camps.) Through Campbell, Hart and Duda met Charles Park, the former
Dean of Stanford’s School of Earth Sciences and an authority on hard-rockmineral deposits. Park helped Hart and Duda codify knowledge about
lead–zinc deposits in the form of IF–THEN rules. Further work with Marco
Einaudi, a professor in Stanford’s Department of Economic Geology, led toadditional rules and rule-organizing ideas. Ultimately the U.S. Geological
Survey provided funding for the development of what became the
PROSPECTOR expert system for consultation about mineral deposits.19
A large group of people participated in the design and writing of the
PROSPECTOR program. Duda and Hart led the effort. I joined the projectsometime after work had begun and after hearing from DARPA that the CBC
project was not going to be continued. Other contributors were John Gaschnig
295
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18Consulting Systems(1950–1982), Kurt Konolige, René Reboh, John Reiter, Tore Risch, andGeorgia Sutherland. MYCIN was a dominant influence on the technology being
developed – “primarily through its use of rules to represent judgmental
knowledge, and its inclusion of formal mechanisms for handling uncertainty.”20
Other important influences came from another medical diagnosis system,
INTERNIST-1, which I’ll describe shortly. These were its use of taxonomic
information and its ability to handle volunteered (rather than only queried)
information.
PROSPECTOR used rules to make inferences and to guide the consultation
process. Two examples of these rules are

Rule 3: “Barite overlying sulfides suggests the possible presence of a
massive sulfide deposit.”
andRule 22: “Rocks with crystal-shaped cavities suggest the presence ofsulfides.”The rules were encoded as “partitioned semantic networks” – a format
originated by Gary Hendrix (1948– ) in his University of Texas Ph.D. thesis
for use in representing knowledge needed by natural language processing
systems.21 Semantic networks were also used to represent the taxonomic
knowledge used by PROSPECTOR. An example of such a network is shown in
Fig. 18.6. The rules could be linked together in what was called an “inference
network.” A simplified example for reasoning about a Kuroko-type massive
sulfide deposit is shown in Fig. 18.7. Note how Rule 22 helps to establish oneof the premises for Rule 3. Note also that the taxonomy is used to infer thepresence of sulfides when galena, sphalerite, or chalcopyrite is known to bepresent.
Inferences from rule premises to rule conclusions in the network depended
on probabilities and Bayes’s rule – not on ad hoc numbers such as “certainty
factors.” The geological experts were asked to quantify their uncertainty about
a rule by giving the designers two numbers. One is the factor by which theodds favoring the conclusion would be increased if the premises were true. The
other is the factor by which the odds favoring the conclusion would bedecreased if the premises were false. Bayes’s rule was used in association withthese numbers to derive the probability of the conclusion given theprobabilities of the premises.22 PROSPECTOR’s inference methods, eventhough they were an improvement over those of MYCIN, gave probabilisticallyvalid results only for certain kinds of inference-net structures. As Glenn Shafer
and Judea Pearl explain, “Probabilities could not simply tag along as numbers
attached to IF–THEN rules. The results of probability calculations would besensible only if these calculations followed principles from probability
theory.”23 Modern expert systems use the more general framework of Bayesian
networks, which will be described later.
296
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Expert Systems

Figure 18.6: A partial geologic taxonomy. (Used with permission of SRI International.)

Figure 18.7: Simplified version of a PROSPECTOR inference network. (Usedwith permission of SRI International.)
The usual format for a PROSPECTOR consultation involved a session witha geologist interested in evaluating a certain site. The geologist mightvolunteer some information, which would evoke some of PROSPECTOR’s rules.
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18Consulting Systems

The system then calculated what additional information would be mosteffective in altering the probability of whatever it was the geologist wanted tofind out. PROSPECTOR would then ask a question to elicit that information(and its probability). Throughout the process, the user could volunteer
additional information at any time.
Because PROSPECTOR could use volunteered information, a run of the
program need not be part of a question-and-answer consultation session.
Instead, a user could input a whole set of data about “findings in the field” to
PROSPECTOR, which would then draw its conclusions. These findings could befrom a database or, perhaps more usefully, from a map that indicated contoursof regions in which various kinds of minerals were found to be present. (Kurt
Konolige of SRI joined the PROSPECTOR team around this time and wrote a
program that allowed PROSPECTOR to use map data as an input.)
The most dramatic instance of PROSPECTOR’s use of map data occurred
when it successfully identified the location of a porphyry molybdenum deposit
at Mount Tolman in the state of Washington.24 Results of previous
exploration of the Mount Tolman site were used to produce maps outlining
important geological data relevant to potential molybdenum deposits.
PROSPECTOR processed these maps using rules obtained primarily from Victor
F. Hollister, an expert on porphyry molybdenum deposits, and Alan
Campbell. The result of the processing was another map indicating therelative “favorability” of a mineral deposit. Computer displays of some of the
input maps are shown in Fig. 18.8. I won’t explain the geological details ofwhat these maps depict, but they represent the kind of data thought to beimportant by experts such as Campbell and Hollister.
From data of this sort, PROSPECTOR produced favorability maps, one ofwhich is shown in Fig. 18.9. The scale on the right of the map (when rendered
in color) indicates favorability from +5 (highly favorable) through −5 (highly
unfavorable). Based on previous extensive drilling in the largest of thefavorable areas, a mining company had planned an open-pit mine there(outlined by the contour labeled “proposed pit”).
One must be careful in evaluating this result. It is not the case thatPROSPECTOR discovered an ore deposit in a site previously unexplored. As

was pointed out in a letter to the editor of the journal Artificial Intelligence,25
A large mining company had already found a molybdenum ore
body by drilling over 200 exploration holes in one region. . . and we
knew that they intended to do further drilling for their own
information.
...
[This further drilling] showed a remarkable congruence withPROSPECTOR’s favorability map, including both verification of
PROSPECTOR’s prediction of a large, previously unknown region of298
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Expert Systems

Figure 18.8: Some of the Mount Tolman input maps. (Photographs courtesy of
Richard Duda.)
ore-grade mineralization, and verification of PROSPECTOR’s
predictions for the barren areas.
...
Unfortunately, prolonged depressed economic conditions in theminerals industry have made this area unprofitable tomine. . . Thus, PROSPECTOR’s success to date has been scientificrather than economic.
Readers interested in more details should see the Science article previously
cited and a summarizing final report on the PROSPECTOR project.26
The computer code for PROSPECTOR was delivered to the U.S. Geological
Survey where Richard B. McCammon developed a successor system he called
PROSPECTOR II. Summarizing his system, McCammon wrote27
PROSPECTOR II, the successor to PROSPECTOR, was developed at

299
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18Consulting Systems

Figure 18.9: Favorability map. (Photograph courtesy of Richard Duda.)
the US Geological Survey. Currently, the knowledge base contains
86 deposit models and information on more than 140 mineraldeposits. Within minutes, the geologist can enter the observed
data for an area, select the types of deposit models to be evaluated,
receive advice on those models that best match the observed data,
and, for a particular model, find out which of the data can be
explained, which of the data are unexplained, and which criticalattributes of the model are not observed in the data.

18.2.3

Other Expert Systems

Several other expert systems followed the MYCIN and PROSPECTOR work.
Some, like MYCIN, were for medical diagnosis and therapy.28 Of these, I’ll
mention the INTERNIST-1 program by computer scientists Randolph A. Miller
and Harry E. Pople and physician Jack D. Myers at the University of300
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18.2

Expert Systems

Pittsburgh and the CASNET (Causal-ASsociational NETwork) program by
Casimir A. Kulikowski and Sholom M. Weiss of Rutgers University.
The INTERNIST-1 series of diagnosis programs contained expertise about
internal medicine.29 Part of this knowledge was represented in a kind of
semantic network or taxonomy of disease states (called a nosology inmedicine). In an article in the New England Journal of Medicine, Miller,
Pople, and Myers state that the performance of INTERNIST-1 “on a series of 19
clinicopathological exercises (Case Records of the Massachusetts General
Hospital) published in the Journal appeared qualitatively similar to that of thehospital clinicians but inferior to that of the case discussants.” However, they
concluded that “the present form of the program is not [yet] sufficiently
reliable for clinical applications.”30 Later, much of the diagnostic knowledge
assembled in INTERNIST-1 was repackaged in QMR (Quick Medical Reference),a diagnostic decision support system for internists.31 (It has since been
discontinued by its eventual purchaser First DataBank.)
CASNET also used networks.32 In those, “inference rules” linked

observations, patho-physiological states, diagnostic states, and treatment
states. Their primary application was to the glaucomas, for which they had
good physical models on which the inference rules could be based.
At Carnegie Mellon University, John McDermott (1942– ) helped in thedevelopment of a rule-based system called XCON (for eXpert CONfigurer) toassist in the ordering and configuring of Digital Equipment Corporation’s VAX
computer systems. XCON grew out of an earlier system by McDermott called
R1.33 R1 and XCON were written in a special rule-processing language called
OPS5, one of the OPS family of languages developed by Charles Forgy (1949– )at CMU.34 (OPS is said to be an acronym for Official Production System.)
The OPS languages used Forgy’s “Rete” algorithm for efficiently stringing
IF–THEN rules together.35 XCON first went into use in 1980 in DEC’s plant in
Salem, New Hampshire.36
The problem with how to deal with uncertain information was avoided in
XCON because it almost never encountered a configuration issue that it did nothave enough certain knowledge to handle. By 1989, according to a paper
about XCON and related configuration systems at DEC,37 these systems had atotal of about 17,500 rules. The paper went on to say that. . . overall the net return to Digital is estimated to be in excess of$40 million per year.
The use of the configuration systems insures that complete,consistently configured systems are shipped to the customer.
Incomplete orders do not get through the process. In addition,
XCON generates configurations which optimize system performance,
so customers consistently get the best view of our products. Before
the configuration systems, we would often ship the same parts
configured differently.
301
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18Consulting Systems

In addition to XCON and its DEC siblings, several expert systems werebuilt and put in use by companies and research laboratories during the 1980s.
In 1983, General Electric developed the Diesel Electric Locomotive
Troubleshooting Aid (DELTA), a prototype system to assist railroad personnel
in the maintenance of General Electric’s diesel-electric locomotives. The
developers stated that it “can diagnose multiple problems with the locomotive
and can suggest repair procedures to maintenance personnel.” It had 530 rules“partially representing the knowledge of a Senior Field Service Engineer.”38
Another example is JETA (Jet Engine Troubleshooting Assistant),developed by engineers at the National Research Council in Canada.
According to a paper about JETA, it “has been applied to troubleshoot theGeneral Electric J85-CAN-15 jet engine that powers the CF-5 trainer fightersused by the Canadian Air Force.”39 Knowledge about jet engines and their
possible faults and symptoms are encoded in frames. Rules are used solely for“specific control functions embedded in a frame and for asynchronous user
input.”An expert system called CCH-ES for credit analysis was put in use at theCredit Clearing House (CCH) division of Dun & Bradstreet (D&B) in July
1989. It contained approximately 800 rules and could handle onlinetransactions when CCH customers called in for service or when analysts
wanted to review cases. Batch cases were run when there were updates in therelevant databases. According to a paper about the system, “Analyst
agreement with CCH-ES continues to be at approximately 98.5 percent on an
ongoing basis. . . . [It] has been a major success at D&B. It has provided CCH
with an automated credit analyst expert system that can provide expert-level
credit analysis decisions consistently and at a high-quality level. Customers
have uniformly praised the system.”40
More expert systems are described in the book The Rise of the Expert
Company.41 In an appendix to that book, Paul Harmon lists over 130 expert
systems in use during the mid- to late 1980s, including• Grain Marketing Advisor for helping farmers choose marketing or storage
strategies for their grain crops,• ACE for helping telephone operating companies reduce the incidence ofphone cable failures,• IDEA for helping technicians diagnose trouble situations in the Infotron
IS4000 Local Area Network,• Diag 8100 for helping with the diagnosis of problems and failures in IBM
8100 computers at the Travelers Corporation,• Intelligent Peripheral Troubleshooter for helping to troubleshootHewlett-Packard disk drives,

302
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18.2

Expert Systems• SNAP for helping shoppers at Infomart (a Dallas computer store) assess
their personal computer needs,• Pile Selection for helping designers at the Kajima Construction Company
select piling material to be used in the foundations of buildings,• ExperTAX for helping to evaluate the application of new U.S. tax laws
for clients of Coopers and Lybrand, and• Dipmeter Advisor for helping in the analysis of geological formations
encountered in oil-well drilling.

18.2.4

Expert Companies

New companies and divisions of established companies were started to develop
and field these applications. The first of these was Teknowledge, organized bya group of Stanford faculty and researchers to market expert systems and to
consult about expert systems. Teknowledge used EMYCIN as its basic
technology. Another was Syntelligence, founded by Peter Hart and Richard
Duda (along with some of the PROSPECTOR researchers) to market expert
systems for insurance underwriting and loan credit analysis. At Syntelligence,expert systems were written in the SYNTEL language, developed by René
Reboh and Tore Risch and based on ideas from PROSPECTOR. After leaving
CMU, Charles Forgy founded Production Systems Technologies in 1983 “todevelop and market state of the art rule-based tools.”42 Among othercompanies formed during this period were Aion Corporation, Helix Expert
System, Ltd., Exsys, Inc., Inference Corporation, and IntelliCorp.43 Because it
was not too difficult for clients who wanted expert systems to develop theirown versions (which were able to run on low-cost workstations and personal
computers), many of the expert systems companies ceased to exist, werebought by larger companies, or had to reorient their businesses to provide
additional or related services.
After the flurry of excitement over expert systems died down a bit in the
1980s and 1990s, some developers concentrated on systems for acquiring anddeploying “business rules.” According to an organization called the Business
Rules Group, a business rule is “a statement that defines or constrains some
aspect of the business. It’s intended to assert business structure, or to control
or influence the behavior of the business.”44 For example, a business rulemight state “when our widget inventory is below 200, notify widget
production.” Business rules take the form of IF–THEN statements, just likeexpert-system rules. In business applications, expert-system inference engines
metamorphosed into business rule engines (BREs). They are used either toanswer questions about business practices or to take actions such as placing
orders or sending alerts.45 Some of the people who had been involved inproviding expert systems software switched to business-rule software. For
example, in 2002, Charles Forgy founded RulesPower, Inc., whose business303
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18

NOTES

rules management systems (BRMSs) used later versions of the Rete algorithm.
(In 2005, RulesPower sold some of its assets to Fair Isaac Corporation, ananalytics and decision management technology company, which has sincechanged its name to FICO.)

Notes
1. See Nils J. Nilsson et al., “Plan for a Computer-Based Consultant System,” SRI AI
Center, Technical Note 94, May 1974. (Available online at
http://www.ai.sri.com/pubs/files/1298.pdf.)[285]
2. From Peter E. Hart, “Progress on a Computer Based Consultant,” SRI AI Center
Technical Note 99, p. 23, January 1975. (Available online at
http://www.ai.sri.com/pubs/files/1389.pdf.) [286]3. Ann E. Robinson, Douglas E. Appelt, Barbara J. Grosz, Gary G. Hendrix, and Jane J.
Robinson, “Interpreting Natural-Language Utterances in Dialogs About Tasks,” SRI AI
Center Technical Note 210, March 15, 1980. (Available online at
http://www.ai.sri.com/pubs/files/709.pdf.) [287]4. Ibid, p. 11. [287]5. Ibid, p. 11. [288]6. Jane J. Robinson, “DIAGRAM,” SRI AI Center Technical Note No. 205, 1980; available
online as SRI AI Center Technical Note 205, February 1980, at
http://www.ai.sri.com/pubs/files/712.pdf. [289]7. J. C. R. Licklider, “The Early Years: Founding IPTO,” in Thomas C. Bartee (ed.),Expert Systems and Artificial Intelligence: Applications and Management, p. 223,
Indianapolis, IN: Howard W. Sams & Co., 1988. [290]8. A copy of this e-mail is in my files. [290]9. J. H. Breasted, The Edwin Smith Surgical Papyrus, two volumes., Chicago: Universityof Chicago Press, 1980. [292]
10. See http://www.neurosurgery.org/cybermuseum/pre20th/epapyrus.html for a copy of
the Wilkins article. [292]11. Bruce G. Buchanan and Edward H. Shortliffe (eds.), Rule-Based Expert Systems: The

MYCIN Experiments of the Stanford Heuristic Programming Project, Reading, MA:
Addison-Wesley, 1984. The book is now out of print but is available online at
http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/RuleBasedExpertSystems.
Shortliffe’s dissertation has been reprinted as a book: Edward H. Shortliffe, Computer-Based
Medical Consultations: MYCIN, New York: Elsevier, 1976. [292]12. Others too attempted to use ideas not strictly based on probability theory. Among thesewere Arthur Dempster and Glenn Shafer (see Glenn Shafer, A Mathematical Theory of
Evidence, Princeton, NJ: Princeton University Press, 1976) and Lotfi Zadeh, who developed“fuzzy logic” (see, as just one of many sources, Lotfi A. Zadeh, “A Fuzzy-Algorithmic
Approach to the Definition of Complex or Imprecise Concepts,” International Journal of
Man-Machine Studies, Vol. 8, pp. 249–291, 1976, available online at
http://www-bisc.cs.berkeley.edu/ZadehFA-1976.pdf.) I’ll mention these alternatives later inthe book. [293]13. For a full description of how MYCIN’s rules were acquired and used see Bruce G.
Buchanan and Edward H. Shortliffe, op. cit.. [293]304
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18.2

NOTES

14. Bruce G. Buchanan and Edward H. Shortliffe, op. cit., Chapters 30 and 31. [294]
15. EMYCIN is described in Bruce G. Buchanan and Edward H. Shortliffe, op. cit., Chapter
15. EMYCIN was the subject of van Melle’s Ph.D. dissertation: William van Melle, “A
Domain-Independent System That Aids in Constructing Knowledge-Based Consultation
Programs,” Stanford University Computer Science Department; see also Stanford Report
Nos. STAN-CS-80-820 and HPP-80-22, 1980. [294]
16. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second
edition, p. 525, Upper Saddle River, NJ: Prentice Hall, 2003. [295]
17. Bruce G. Buchanan and Edward H. Shortliffe, op. cit. [295]18. Economic geologists distinguish mineral deposits from ore deposits. An ore is a mineralthat can be profitably extracted. Hard-rock minerals include copper, lead, zinc, and so on,
but not hydrocarbons. [295]19. PROSPECTOR was first described in Peter E. Hart, “Progress on a Computer-Based
Consultant,” Proceedings of the International Joint Conference on Artificial Intelligence,Vol. 2, pp. 831–841, 1975. [295]
20. Richard O. Duda et al., “Semantic Network Representations in Rule-Based Inference
Systems,” in D. A. Waterman and Frederick Hayes-Roth (eds.), Pattern-Directed Inference
Systems, Orlando, FL: Academic Press, Inc., 1978. Available online at
http://www.ai.sri.com/pubs/files/751.pdf. [296]
21. Gary G. Hendrix, “Partitioned Networks for the Mathematical Modeling of Natural
Language Semantics,” Ph.D. thesis, University of Texas Computer Science Department,
1975. For a short paper, see Gary G. Hendrix, “Expanding the Utility of Semantic Networks
Through Partitioning,” Proceedings of the Fourth International Conference on Artificial
Intelligence, pp. 115–121, 1975. This paper also appeared as SRI AI Center Technical Note
105 and is available online at http://www.ai.sri.com/pubs/files/1380.pdf. [296]22. For a description of PROSPECTOR’s inference methods see Richard O. Duda, Peter E.
Hart, and Nils J. Nilsson, “Subjective Bayesian Methods for Rule-Based Inference Systems,”
in Proceedings of the AFIPS National Computer Conference, Vol. 45, pp, 1075–1082, 1976.
Reprinted in G. Shafer and J. Pearl (eds.), Readings in Uncertain Reasoning, pp. 274–281,
San Francisco: Morgan Kaufmann Publishers, 1990. A version appears as SRI AI Center
Technical Note 124 and is available online at http://www.ai.sri.com/pubs/files/755.pdf.
[296]
23. Glenn Shafer and Judea Pearl (eds.), Readings in Uncertain Reasoning, San Francisco:
Morgan Kaufmann Publishers, 1990. The book is no longer in print, but some of thechapters are available online at http://www.glennshafer.com/books/rur.html. [296]
24. Alan N. Campbell, Victor F. Hollister, Richard O. Duda, and Peter E. Hart,“Recognition of a Hidden Mineral Deposit by an Artificial Intelligence Program,” Science,Vol. 217, No. 4563, pp. 927–929, September 3, 1982. [298]
25. Richard O. Duda, Peter E. Hart, and René Reboh, letter to the editor, Artificial
Intelligence, Vol. 26, pp. 359–360, 1985. [298]
26. Richard O. Duda, “The PROSPECTOR System for Mineral Exploration,” Final Report
prepared for the Office of Resource Analysis, U.S. Geological Survey, Reston, VA 22090,
April 1980. [299]
27. Richard B. McCammon, “ PROSPECTOR II – An Expert System for Mineral Deposit
Models,” International Journal of Rock Mechanics and Mining Sciences and Geomechanics
Abstracts, Vol. 33, No. 6, pp. 267A–267A(1), September 1996. See also Richard B.
McCammon, “PROSPECTOR II,” in H. J. Antonisse, J. W. Benoit, and B. G. Silverman(eds.), Proceedings of the Annual AI Systems in Government Conference, pp. 88–92, March
1989, Washington, DC. [299]28. See Peter Szolovits (ed.), Artificial Intelligence in Medicine, Boulder, CO: Westview

305
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18

NOTES

Press, 1982. Available online at http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch0.html.
[300]
29. Harry E. Pople Jr., “Heuristic Methods for Imposing Structure on Ill-Structured
Problems: The Structuring of Medical Diagnostics,” Chapter 5 in Peter Szolovits (ed.),Artificial Intelligence in Medicine, Boulder, CO: Westview Press, 1982. Available online at
http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch5.html. [301]
30. Randolph A. Miller et al., “INTERNIST-1: An Experimental Computer-BasedDiagnostic Consultant for General Internal Medicine,” New England Journal of Medicine,Vol. 307, pp. 468–76, August 19, 1982. [301]
31. Randolph A. Miller et al., “The INTERNIST-1/Quick Medical Reference Project –Status Report,” The Western Journal of Medicine, Vol. 145, No. 6, pp. 816–822, 1986.
Available online at
http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1307155&blobtype=pdf. [301]32. Casimir A. Kulikowski and Sholom M. Weiss, “Representation of Expert Knowledge forConsultation: The CASNET and EXPERT Projects,” Chapter 2 in P. Szolovits (ed.),Artificial Intelligence in Medicine, Boulder, CO: Westview Press, 1982. Available online at
http://groups.csail.mit.edu/medg/ftp/psz/AIM82/ch2.html. [301]33. John McDermott, “R1: A Rule-Based Configurer of Computer Systems,” Artificial
lntelligence, Vol. 19, No. 1, pp. 39–88, 1980. [301]34. Charles Forgy, “OPS5 User’s Manual,” Technical Report CMU-CS-81-135, Carnegie
Mellon University, 1981. See also Lee Brownston et al., Programming Expert Systems in
OPS5, Reading, MA: Addison-Wesley, 1985. [301]35. Charles Forgy, “Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern
Match Problem,” Artificial Intelligence, Vol. 19, pp. 17–37, 1982. [301]36. See http://en.wikipedia.org/wiki/Xcon. [301]37. Virginia E. Barker and Dennis E. O’Connor, “Expert Systems for Configuration at
Digital: XCON and Beyond,” Communications of the ACM, Vol. 32, No. 3, pp. 298–318,
March 1989. [301]38. Piero P. Bonissone and H. E. Johnson Jr., “ DELTA: An Expert System for Diesel
Electric Locomotive Repair,” Proceedings of the Joint Services Workshop on Artificial
Intelligence in Maintenance, Boulder, CO, October 4–6, 1983, AD-A145349, pp. 397–413,
June 1984 (Defense Technical Information Center Accession Number ADA145349.) [302]39. Phillippe L. Davidson et al., “Intelligent Troubleshooting of Complex Machinery,”
Proceedings of the Third International Conference on Industrial Engineering Applications ofArtificial Intelligence Expert Systems, pp. 16–22, Charleston, South Carolina, USA, July
16–18, 1990. See also M. Halasz et al., “ JETA: A Knowledge-Based Approach to Aircraft
Gas Turbine Engine Maintenance,” Journal of Applied Intelligence, Vol. 2, pp. 25–46, 1992.
[302]
40. Roger Jambor et al., “The Credit Clearing House Expert System,” IAAI-91 Proceedings,
pp. 255–269, 1991. [302]41. Edward Feigenbaum, Pamela McCorduck, and H. Penny Nii, The Rise of the Expert
Company: How Visionary Companies Are Using Artificial Intelligence to Achieve Higher
Productivity and Profits, New York: Times Books, 1988. [302]42. http://www.pst.com/. [303]
43. Harmon’s appendix, just cited, lists several companies as does
http://dmoz.org/Computers/Artificial Intelligence/Companies/. [303]44. From http://www.businessrulesgroup.org/defnbrg.shtml. [303]45. I thank Paul Harmon, now Executive Editor of Business Process Trends

306
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18.2

NOTES

(www.bptrends.com), for enlightening me about business rules. [303]307
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18

NOTES

308
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19.1

Chapter 19

Understanding Queries andSignals
19.1

The Setting

Up until about the mid-1970s, DARPA managers were able to cushion theimpact of the Mansfield Amendment (which required that Defense Department
research be relevant to military needs) by describing computer research
programs in a way that emphasized applications. Larry Roberts, the Directorof DARPA’s IPTO during the late 1960s and early 1970s, wrote1
The Mansfield Amendment created a particular problem during mystay at DARPA. It forced us to generate considerable paperwork
and to have to defend things on a different basis. It made us havemore development work compared to the research work in order to
get a mix such that we could defend it. I don’t think I had to dropa project in our group due to the Mansfield Amendment, however.
We could always find a way to defend computer science. . .
The formal submissions to Congress for AI were written so that thepossible impact was emphasized, not the theoretical considerations.
Cordell Green, working under Roberts at IPTO, wrote2
Generally speaking, anything that came along in the AI field that
we thought looked good was supported. . .
One of my jobs was to defend the AI budget but that wasn’t
terribly difficult. . . . all sorts of computer science is relevant
because it will have a high impact on any large
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19Understanding Queries and Signals
information-processing organization, and the Defense Department
is certainly such an organization. . . . all of this research should be
kept alive because it had potential military relevance.

By the mid-1970s, however, the pressure to produce militarily usefulsystems became much more intense. DARPA, which had been generously
supporting rather undirected basic AI research, started to focus instead on
solving “pressing DoD problems.” Although the director of DARPA’s IPTO at
the time, J. C. R. Licklider, was as sympathetic as ever to basic research inAI, DARPA’s top management had entirely different attitudes. Licklider was
having difficulties explaining his AI program to DARPA’s “front office.” TheDARPA Director during the early 1970s, Stephen Lukasik, was (according to
Licklider3 )neither for nor against AI. He was for good management and hegot the idea that maybe some of the AI stuff wasn’t being very wellmanaged. . . . [He] had a fixed idea that a proposal is not a proposal
unless it’s got milestones. I think that he believed that the more
milestones, the better the proposal. . . . I think he was notdeveloping a distaste for AI but a conviction that this is such animportant field that the researchers have got to learn to live in abigger, more rigid, more structured bureaucracy.”
Lukasik’s view about how projects should be managed had a direct effecton DARPA-supported basic research in AI. For example, a “Quarterly
Management Report” that I submitted in February 1975 describing progresson the SRI computer-based consultant caused Licklider to ask how the reportmight be recast to emphasize progress along certain paths in a “PERT Chart.”“What I would like to have,” he wrote me in a letter dated March 3, 1975, “isthe PERT Chart – so that I can mark the accomplishments in red and see
where you stand with respect to the overall pattern. . . . Do you have such achart? If so, please send me a copy. If not, how about making one? It would
really help us greatly here at ARPA.”4
Of course, in basic research, although one can describe generally the
problems one is trying to solve, one can’t describe (ahead of time) what thesolutions are going to be. In fact, as exploratory research progresses, newproblems become apparent, so one can’t even describe all the problems aheadof time. One can’t make the kind of detailed plan for basic research that one
can make for applying already developed technology to specific applications.
Unfortunately, the management of DARPA was shifting from people who
understood how to initiate and manage basic research to people who knew
how to manage technology applications.
The shift toward shorter term, intensely managed research became more
pronounced when George Heilmeier (Fig. 19.1) replaced Stephen Lukasik as
DARPA Director in 1975. Heilmeier came from RCA, where he had headed
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19.1

The Setting

the research group that invented the first liquid crystal display. Licklider later
wrote that Heilmeier “wanted to understand AI in the way he understoodliquid crystal displays. . . ”5

Figure 19.1: George Heilmeier. (Photograph courtesy of DARPA.)
One of the tasks that Heilmeier gave IPTO was to produce a “roadmap”(that is, a detailed plan) for its AI research program (and its other computerscience programs too). This roadmap should summarize pastaccomplishments, indicate areas where existing technology could be applied tomilitary problems, and show milestones along the way. This “guidance” fromDARPA management caused great difficulties for Licklider, some of whichwere explained in an e-mail he sent to some leaders of AI research in April of1975. (I was among the recipients of his “Easter Message,” e-mailed on April2, 1975.) Here are some excerpts:
The purpose of this Easter note is to bring you up to date on a311
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19Understanding Queries and Signals
development in ARPA that concerns me greatly – and will, I think,also concern you. . . .
. . . the prevailing direction in ARPA is to do research within thespecific contexts of military problems and not to do research that
does not have a military ‘buyer’ ready to take it over as soon as theconcept gets well formulated. . . .
[there are] strong pressures from the new Director, George
Heilmeier, that IPTO ‘redirect’ the university AI efforts to work on
problems that have real DoD validity. . .
. . . the situation is complicated by the fact that ARPA has been
supporting basic research at a rather high level for more than ten
years (has spent more than $50 million on it), and it is natural fora new director, or even an old one, to ask, “What have we gotten
out of it in terms of improvements in national defense?”

According to Licklider’s Easter note, some of the things that Heilmeier
thought IPTO could do for the Defense Department were the following:• get computers to read Morse code in the presence of other code andnoise,• get computers to identify/detect key words in a stream of speech,• solve DoD’s “software problem,”• make a real contribution to command and control, and• do a good thing in sonar.
Even though one of the items on Heilmeier’s list involved speechprocessing, one of the casualties of his tenure as Director of DARPA was the
SUR Program. None of the systems that had been developed under the
program could respond in real time, nor could they deal with large enough
vocabularies. Heilmeier believed (probably with good reason) that speech
understanding was still a basic research activity. Thus, he thought, it should
be supported, say, by the National Science Foundation (NSF), and he rejected
proposals for DARPA to continue it.
Unfortunately, most of the research areas that were on Licklider’s own list
(which was also mentioned in his Easter note) were not explicitly on
Heilmeier’s. (I can’t resist mentioning one of the items on Licklider’s list:“Develop a system that will guide not-sufficiently-trained maintenance men
through the maintenance of complex equipment.”) One of Heilmeier’s items
was sufficiently vague, however, to justify work both in NLP and in computer
vision. That was “command and control,” an activity that involves getting
and presenting relevant information to commanders so that they can control
military forces effectively.
312
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19.2

Natural Language Access to Computer Systems

DARPA program officers Floyd Hollister and Col. David Russell wereable to persuade DARPA management that text-based, natural language
access to large, distributed databases would be an important component ofcommand and control systems. They argued that the technology for suchaccess was sufficiently far along for it to be applied in what they called“command-and-control test-bed systems.” After all, Bill Woods and colleagues
at BBN had already demonstrated LUNAR, a natural language “front end” todatabases about moon rocks. Several other researchers had also begun workon the problem of how to communicate with computers using English or someother natural language. (For example, there were over forty papers on NLP
presented at the Fifth IJCAI in 1977 at MIT, and the February 1977 issue ofthe ACM’s SIGART Newsletter published 52 summaries of ongoing researchon “Natural Language Interfaces.”) In the next part of this chapter, I’ll
describe some of the accomplishments during this period on communicatingwith computers using natural language.
A second area of great importance in command and control was
automating the analysis of aerial photos. Spotting targets of military interestin these photos, such as roads, bridges, and military equipment, typicallyrequired hours of effort by intelligence analysts. Because techniques being
developed by researchers in computer vision might provide tools to help
human analysts, DARPA had good reasons to continue funding computer
vision research. In 1976, it began the “Image Understanding ”(IU) program todevelop the technology required for automatic and semiautomaticinterpretation and analysis of military photographs and related images.
Although initially conceived as a five-year program, it continued (with broader
objectives) for well over twenty years. I’ll summarize the image understanding
work, along with other computer vision research, in a subsequent chapter.
Doing something about sonar was one of the items on Heilmeier’s list. In
fact, in his Easter note Licklider wrote “One of [Heilmeier’s] main silver-bullet
areas is underwater sound and sonar, and IPTO is in the process of ‘buying in’on the HASP project (Ed Feigenbaum’s AI approach).” I’ll describe HASP and
how DARPA “bought in” to the project toward the end of the chapter.

19.2

Natural Language Access to Computer
Systems

19.2.1LIFER

At SRI, Gary Hendrix (Fig. 19.2) had been developing a system called LIFER(an acronym for Language Interface Facility with Elliptical and Recursive
Features), programmed in INTERLISP, for rapid development of natural
language “front ends” to databases and other software. LIFER allowed anontechnical user to specify a subset of a natural language (for example,
313
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19Understanding Queries and Signals

Figure 19.2: Gary Hendrix. (Photograph courtesy of Gary Hendrix.)
English) for interacting with a database system or other software. A parser
contained within LIFER could then translate sentences and requests in thislanguage into appropriate interactions with the software. LIFER had
mechanisms for handling elliptical (that is, incomplete) inputs, for correcting
spelling errors, and for allowing novices to extend the language through theuse of paraphrases.
An interesting feature of LIFER was that the language it could handle wasdefined in terms of “patterns,” which used semantic concepts in the domain of
application. One such pattern, for example, might be
WHAT IS THE <ATTRIBUTE> OF <PERSON>
where the words WHAT, IS, THE, and OF are actual words that might occur inan English query and <ATTRIBUTE> and <PERSON> are “wild cards” that could
match any word in predefined sets. <ATTRIBUTE> might be defined to matchwords such as AGE, WEIGHT, HEIGHT, etc., and <PERSON> might match JOHN,SUSAN, TOM, etc. This pattern would then “recognize” a sentence such as
WHAT IS THE HEIGHT OF SUSAN
This method of defining a “grammar” is to be contrasted with the usual
syntactic phrase-structure rules such as S <= NP VP. As I mentioned earlier,
314
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19.2

Natural Language Access to Computer Systems

grammars based on concepts in the domain of application are called “semantic
grammars.”
LIFER used a simplified augmented transition network (like those I
described in a previous chapter) to analyze an input sentence. Each pattern
defined by the grammar corresponded to a possible “path” in the transition
network. An input sentence was analyzed by attempting to match it with oneof these paths, noting which specific instance of a wild card, such as<ATTRIBUTE>, was used in the match. Depending on the path taken and on
the values of wild cards in the path, software was automatically created thatwas then used to make the appropriate database query or to carry out anappropriate command.6 In 1982, Hendrix left SRI to form Symantec, acompany that planned to develop and market a natural language
question-answering system based on semantic grammars such as LIFER.
[Perhaps natural language processing (or the intended market) was not quite
ready, because Symantec was later reorganized to market computer security
and anti-virus software.]
LIFER was used at SRI as the natural language component of a systemcalled “LADDER” for accessing multiple, distributed databases.7 LADDER (anacronym for Language Access to Distributed Data with Error Recovery)translated the English query into a hypothetical database query that assumed
a very simple database organization. Using a system called IDA (an acronym
for Intelligent Data Access), that hypothetical query was transformed into a
series of actual database queries that took into account the actual organizationof the database. It also took account of syntactic and semantic knowledge toattempt to produce very efficient queries and to detect any erroneous updates
to the database content. (More research on systems similar to IDA was
performed in a joint program between Stanford University and SRI, named
KBMS, an acronym for Knowlege Based Management System, with support
from DARPA.)

Consistent with DARPA’s focus on military applications, LADDER wasable to answer questions about naval ships using information about ship sizes,
types, locations, and so on from various databases. Some sample interactions
with an early version of LADDER are shown in Fig. 19.3. Note the ability ofthe system to correct spelling errors, to deal with incomplete questions, and toaccept paraphrases.8

19.2.2CHAT-80

Between 1979 and 1982, Fernando Pereira (1952– ) and David H. D. Warren(circa 1950– ) developed a system called CHAT-80 at the University of
Edinburgh as part of Pereira’s Ph.D. dissertation there. CHAT-80 was able toanswer rather complex questions, posed in English, about a database ofgeographical facts.

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19Understanding Queries and Signals

Figure 19.3: Sample interactions with LADDER. (Used with permission of SRI
International.)
According to Pereira’s dissertation,9 work on CHAT-80 started as “anattempt to clarify and improve some previous NL work of Colmerauer.”
CHAT-80 was written in PROLOG, the logic-based programming language
developed originally by Alain Colmerauer. In fact, the grammar used by
CHAT-80 consisted of logical formulas stated in the PROLOG language. For
example,sentence(s(NP,VP), S0,S) :- noun phrase(NP, N, S0,S1),
316
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19.2

Natural Language Access to Computer Systems
verb phrase(VP, N, S1,S)

is CHAT-80’s way of stating that “there is a sentence between points S0 and S
in a string (of words) if there is a noun phrase with number N (that is,
singular or plural) between points S0 and S1, and a verb phrase with number
N between points S1 and S.” Grammars defined by PROLOG clauses of thiskind are called Definite Clause Grammars (DCGs). Several clauses of this sort
were used by CHAT-80 to parse English sentences. The actual parsing was doneby the PROLOG program consisting of these clauses.
In CHAT-80, computation of the meaning (that is, the semantics) of an
English query was guided by the syntactic structure of the query (as computedby the PROLOG program) and was expressed as a logical formula. This formula
was then transformed into the individual queries of the database needed toanswer the original question.10 (For information about how to get a runningversion of CHAT-80, see http://www.cis.upenn.edu/∼pereira/oldies.html.)
Here are a few examples (from Chapter 5 of Pereira’s dissertation) of
queries that CHAT-80 was able to answer:
Q: What is the capital of Upper Volta?
A: Ouagadougou
Q: Which country’s capital is London?
A: united kingdom
Q: What is the ocean that borders African countries and thatborders Asian countries?
A: indian ocean
Q: What are the capitals of the countries bordering theBaltic?
A: denmark:copenhagen; east germany:east berlin;
finland:helsinki; poland:warsaw; soviet union:moscow;
sweden:stockholm; west germany:bonn
Q: What is the total area of countries south of the Equatorand not in Australasia?
A: 10,228 ksqmiles
Q: What are the continents no country in which contains morethan two cities whose population exceeds 1 million?
A: africa, antarctica, australasia

317
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19Understanding Queries and Signals
Q: Which country bordering the Mediterranean borders acountry that is bordered by a country whose population
exceeds the population of India?
A: turkey

Although these examples indicate rather impressive performance, CHAT-80s
abilities were constrained by its limited vocabulary and grammar. Theselimitations are described in detail in Pereira’s dissertation.

19.2.3

Transportable Natural Language Query Systems

As I have described it, CHAT-80 was implemented as a system for querying a
database of geographical facts. However, since much of its design was not
specific to geography, it could rather easily be modified to be able to deal withother databases. CHAT-80 was just one of several query systems that were“transportable” in the sense that they could be adapted to serve as natural
language front ends to a variety of different databases. Other such systems
were ASK developed at Caltech,11 EUFID developed at SDC,12 IRUS developed
at BBN,13 LDC-1 developed at Duke University,14 NLP-DBAP developed at Bell
Laboratories,15 and TEAM developed at SRI.16
Since I know more about TEAM than I do about the others, I’ll say a few
things about it as representative of its class. TEAM (an acronym forTransportable English Database Access Medium) was supported by DARPA
and was designed to acquire information about a database from a databaseadministrator and to interpret and answer questions of the database that are
posed in a subset of English appropriate for that database. TEAM, like manyother transportable systems, was built so that the information needed toadapt it to a new database and its corresponding subject matter could beacquired from an expert on that database even though he or she might know
nothing about natural language interfaces.
To illustrate the operation of TEAM, its designers used a databaseconsisting of four “files” (or “relations”) of geographic data. Partial versions ofthese files are shown in Fig. 19.4.
I’ll trace through some of the steps TEAM used to answer the query“Show each continent’s highest peak.”
TEAM used a subsystem called DIALOGIC17 to convert the English query
into a logical expression. Within DIALOGIC, a subsystem based on DIAMOND18
performed syntactic analysis using the DIAGRAM grammar.19 The highest

scoring parse tree is shown in Fig. 19.5.
Based on this parse tree and knowledge about the concepts used in the
database, a semantic analysis system converted the query into the following
logical expression (here restated in an English-like form for better
understandability):
318
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19.3

HASP/SIAP

Figure 19.4: Files used in a TEAM database. (Used with permission of SRI
International.)
FOR EVERY CONTINENT
WHAT IS EACH PEAK
SUCH THAT THE PEAK IS THE HIGHEST PEAK SUCH THAT
THE CONTINENT IS CONTINENT OF THE PEAK?
TEAM then used its knowledge about the structure of the database andabout how components of this logical expression are associated with relationsin the database to generate the actual database query and construct ananswer.

19.3

HASP/SIAP

In 1972, while Larry Roberts was still the Director of IPTO, he asked Ed
Feigenbaum at Stanford to think about applying the AI ideas so successfully
used in DENDRAL to the problem of identifying and tracking ships andsubmarines in the ocean using acoustic data from concealed hydrophonearrays.
Some of the acoustic data picked up by the hydrophone arrays come fromrotating shafts and propellors and reciprocating machinery on board ships.
Different ships emit sounds with their own characteristic identifying
fundamental frequencies and harmonics. Human specialists who analyze thissort of surveillance data look at the sonogram displays of ocean sounds and, by
matching sound spectra to stored references, attempt to identify and locate
ships that might be present (if any). Making these decisions often requires
using information not present in the signals themselves, information such as
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19Understanding Queries and Signals

Figure 19.5: A parse tree for “Show each continent’s highest peak.” (Used withpermission of SRI International.)
reports from other sensor arrays, intelligence reports, and general knowledge
about the characteristics of ships and common sea lanes.
The analysis problem is complicated by several factors:20
The background noise from distant ships is mixed withstorm-induced and biological noises. Sound paths to the arrays
vary with diurnal and seasonal cycles. Arrival of sound energy over
several paths may suddenly shift to no arrivals at all, or arrivals
only of portions of vessel radiation. Sound from one source canappear to arrive from many directions at once. Characteristics ofthe receivers can also cause sound from different bearings to mix,
appearing to come from a single location. Finally, the submarine
targets of most interest are very quiet and secretive.
320
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19.3

HASP/SIAP

Supported by DARPA, work on this problem began in 1973 at Systems
Control Technology, Inc. (SCI), a Palo Alto company with expertise in thisarea that could work on classified military projects. (SCI was later acquired by
British Petroleum.) Feigenbaum, and his colleagues at SCI, soon realized thatthe “generate-and-test” strategy of DENDRAL would not work for the problemof ocean surveillance because there was no “legal move generator” that could
produce candidate ship positions and their tracks given the surveillance data.
However, noting that the overall analysis problem could be divided into levels
similar to those used in the Blackboard architecture of HEARSAY-II (a system
shown to be good at dealing with signals in noise), the team thought thatsomething similar would work for their problem. The team developed a systemcalled HASP (an acronym for Heuristic Adaptive Surveillance Program) basedon the Blackboard model. Follow-on work that would process actual ocean
data began at SCI with SIAP (an acronym for Surveillance Integration
Automation Program) in 1976. I’ll give a brief description of the HASP/SIAP
system design and then summarize how it performed.
The top level of the Blackboard was a “situation board” – a symbolicmodel of the unfolding ocean situation, built and maintained by the program.
It described all the ships hypothesized to be out there with a confidence levelassociated with each of them.
Just below the situation board level was a level containing the individual
hypothesized vessels. Each vessel element had information about its class,location, current speed, course, and destination, each with a confidence
weighting. Below the vessel level was a level containing hypothesized soundsources: engines, shafts, propellers and so on with their locations andconfidence weightings. Spectral features abstracted from the acoustic data
were at the lowest level.
The levels were linked by knowledge sources (KSs) that were capable ofinferring that if certain elements were suspected to be present at one level thenother elements could be inferred to be present at another level (or if they werealready present at that level, their confidence could be adjusted). Just as in
HEARSAY-II, the links could span multiple levels and make inferences upward,
downward, or within a level. An inference caused by one KS might allow
another KS to draw an additional inference, and so on in cascade, until all
relevant information had been used. In this manner, new information could be
assimilated and expectations concerning possible future events could beformulated.
One type of KS was composed of IF–THEN rules. (Other types were used
also.) For example, here is an IF–THEN rule (translated into English forreadability) that acted within the source level:
IF: a source was lost due to fade-out in the near-past, and a similarsource started up in another frequency, and the locations of thetwo sources are relatively close,
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19Understanding Queries and Signals
THEN: they are the same source with confidence of 3.

HASP/SIAP had several kinds of knowledge sources, each represented in a
way appropriate to the level(s) involved. Some KSs were based on information
about the environment, such as common shipping lanes, location of arrays, andknown maneuver areas. Others had information about vessels and vessel types,
their speeds, component parts, acoustic characteristics, home bases, and so on.
In addition to KSs dealing with knowledge appropriate to the various levels,
there were “meta” KSs that had information about how to use other KSs.

Figure 19.6: A network structure linking data at different levels. (Illustrationfrom H. Penny Nii, Edward A. Feigenbaum, John J. Anton, and A. J. Rockmore,“Signal-to-Symbol Transformation: HASP/SIAP Case Study,” AI Magazine,
Vol. 3, No. 2, p. 26, Figure 2, c 1982, Association for the Advancement of
Artificial Intelligence. Used with permission.)
The actions of the KSs in linking information at the various levels can be
represented as a network, such as the one shown schematically in Fig. 19.6. At
the end of an analysis session, when all KSs have had a chance to participate
and the action dies down, the resulting network is called the “current best
hypothesis” (CBH) about the current ocean situation. Here is a partial sample(translated into English) of how a CBH for a particular run of HASP/SIAP
might be described:21
The class of Vessel-l, located in the vicinity of Latitude 37.3 and322
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19.3

NOTES
Longitude 123.1 at time day 2, 4 hours, 55 minutes, can be either
Cherry, Iris, Tulip, or Poppy class. Two distinct acoustic sources,
supported by respective harmonic sets, have been identified for
Vessel-l. Source-l could be due to a shaft or propeller of vessel class
Cherry or Poppy. Similar source possibilities exist for Source-5.
These two sources were assimilated into Vessel-l because of thepossibility of a known mechanical ratio that exists between the twosources.

The MITRE Corporation conducted several experiments to compare theperformance of HASP/SIAP against that of two expert sonar analysts. In oneof these experiments, MITRE concluded that “HASP/SIAP has been shown toperform well on ocean derived data. . . For this restricted ocean scene, theprogram is not confused by extraneous data and gives results comparable toan expert analyst.” In another experiment, it concluded that “HASP/SIAP
understood the ocean scene more thoroughly than the second analyst and as
well as the first analyst. . . The program can work effectively with more than
one acoustic array. SIAP classified an ocean scene over a three hour time
period indicating the plausibility of SIAP efficacy in an evolving ocean
situation.” The third experiment led to the conclusions that “with the
exception that the SIAP program obtained significantly more contacts than thehuman analysts, the descriptions of the ocean scene are very similar.”Moreover, “SIAP can perform vessel classification in increasingly difficult ocean
scenes without large increases in the use of computer resources.”22
As mentioned earlier, the Blackboard model has been applied in several
other areas as well. Examples include protein crystallographic analysis,23
image understanding,24 and dialog comprehension.25 Interestingly, theBlackboard architecture has impacts beyond technology. Donald Norman, acognitive psychologist, has said that HEARSAY-II has been a source of ideas fortheoretical psychology and that it fulfills his “intuitions about the form of ageneral cognitive processing structure.”26 Also, as I’ll mention in a later
chapter, several models of the neocortex involve interacting layers resembling
both the form and the mechanisms of Blackboard systems.

Notes
1. Lawrence G. Roberts, “Expanding AI Research and Founding Arpanet,” in Thomas C.
Bartee (ed.), Expert Systems and Artificial Intelligence: Applications and Management, pp.
229–230, Indianapolis, IN: Howard W. Sams & Co., 1988. [309]
2. C. Cordell Green, “AI During IPTO’s Middle Years,” ibid, pp. 238–240. [309]
3. J. C. R. Licklider, “The Early Years: Founding IPTO,” ibid, pp. 225–226. [310]4. Licklider letter in my file. [310]5. J. C. R. Licklider, op. cit., p. 226. [311]323
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19

NOTES

6. For technical details about LIFER, see Gary G. Hendrix, “LIFER: A Natural Language
Interface Facility,” SRI AI Center Technical Note 135, December 1976 (available online at
http://www.ai.sri.com/pubs/files/1414.pdf); Gary G. Hendrix, “The LIFER Manual: A
Guide to Building Practical Natural Language Interfaces,” SRI AI Center Technical Note
138, February 1977 (available online at http://www.ai.sri.com/pubs/files/749.pdf); andGary G. Hendrix, “Human Engineering for Applied Natural Language Processing,”
Proceedings of the 5th IJCAI, pp. 183–191, 1977 (which also appeared as SRI AI Center
Technical Note 139, available online at http://www.ai.sri.com/pubs/files/748.pdf). [315]7. Earl D. Sacerdoti, “Language Access to Distributed Data with Error Recovery,”
Proceedings of the 5th IJCAI, pp. 196–202, 1977, and reprinted as SRI AI Center Technical
Note 140, February 1977 (available online at http://www.ai.sri.com/pubs/files/747.pdf);
Earl D. Sacerdoti, “A LADDER User’s Guide (Revised),” SRI AI Center Technical Note
163R, March 1980 (available online at http://www.ai.sri.com/pubs/files/735.pdf); and Gary
G. Hendrix et al. “Developing a Natural Language Interface to Complex Data,” ACM
Transactions on Database Systems, Vol. 3, No. 2, pp. 105-147, June 1978 (available online
as SRI AI Center Technical Note 152, August 1977, at
http://www.ai.sri.com/pubs/files/741.pdf). [315]8. For a more extensive interaction with a later version of LADDER, see Appendix A of
Earl D. Sacerdoti, “A LADDER User’s Guide (Revised),” SRI AI Center Technical Note
163R, March 1980. [315]9. Fernando Pereira, “Logic for Natural Language Analysis”, Ph.D. dissertation, Universityof Edinburgh, 1982. A slightly revised version of the dissertation was published as Technical
Note 275 of the SRI AI Center and is available online at
http://www.ai.sri.com/pubs/files/669.pdf. [316]
10. Readers interested in the details of these rather technical processes might refer to
Pereira’s dissertation or to David H. D. Warren and Fernando Pereira, “An Efficient Easily
Adaptable System for Interpreting Natural Language Queries,” Computational Linguistics,
Vol. 8 , Nos. 3–4, pp. 110–122, July–December 1982. [317]11. Bozena H. Thompson and Frederick B. Thompson, “Introducing ASK, A Simple
Knowledgeable System,” Conference on Applied Natural Language Processing, pp. 17–24,
1983. Available online at http://ucrel.lancs.ac.uk/acl/A/A83/A83-1003.pdf. [318]12. Marjorie Templeton and John Burger, “Problems in Natural Language Interface to
DBMS with Examples from EUFID,” Proceedings of the First Conference on Applied
Natural Language Processing, pp. 3–16, 1983. Available online at
http://www.aclweb.org/anthology-new/A/A83/A83-1002.pdf. [318]
13. Madeleine Bates and Robert J. Bobrow, “A Transportable Natural Language Interface,”
Proceedings of the 6th Annual International ACM SIGIR Conference on Research andDevelopment in Information Retrieval, pp. 81–86, 1983. [318]
14. Bruce Ballard, John C. Lusth, and Nancy L. Tinkham, “LDC-1: A Transportable,
Knowledge-Based Natural Language Processor for Office Environments,” ACM Transactions
on Information Systems, Vol. 2, No. 1, pp. 1–25, January 1984. [318]15. Jerrold M. Ginsparg, “A Robust Portable Natural Language Data Base Interface,”
Conference on Applied Natural Language Processing, pp. 25–30, 1983. Available online at
http://ucrel.lancs.ac.uk/acl/A/A83/A83-1004.pdf. [318]
16. Barbara J. Grosz et al., “TEAM: An Experiment in the Design of Transportable
Natural-Language Interfaces,” Artificial Intelligence, Vol. 32, No. 2, pp. 173–243, May
1987. Available online as SRI Technical Note 356R, October 20, 1986, at
http://www.ai.sri.com/pubs/files/601.pdf. [318]17. Barbara Grosz et al., “DIALOGIC: A Core Natural-Language Processing System,”
Proceedings of Ninth International Conference on Computational Linguistics, pp. 95–100,
1982. Available online at http://www.aclweb.org/anthology-new/C/C82/C82-1015.pdf.
[318]

324
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19.3

NOTES

18. DIAMOND was developed at SRI by William Paxton and is described in Ann E.
Robinson et al., “Interpreting Natural-Language Utterances in Dialogs About Tasks,” AI
Center Technical Note 210, SRI International, March 1980. Available online at
http://www.ai.sri.com/pubs/files/709.pdf. [318]19. Jane J. Robinson, “DIAGRAM: A Grammar for Dialogs,” Communications of the ACM,
Vol. 25, No. 1, pp. 27–47, January 1982. Available online as SRI AI Center Technical Note
205, February 1980, at http://www.ai.sri.com/pubs/files/712.pdf. [318]
20. H. Penny Nii, Edward A. Feigenbaum, John J. Anton, and A. J. Rockmore,“Signal-to-Symbol Transformation: HASP/SIAP Case Study,” AI Magazine, Vol. 3, No. 2,
pp. 23–35, 1982. [320]21. From ibid, p. 28. [322]22. From ibid, p. 34. [323]
23. Robert S. Engelmore and H. Penny Nii, “A Knowledge-Based System for theInterpretation of Protein X-Ray Crystallographic Data,” Stanford Computer Science
Department Technical Report CS-TR-77-589, 1977; available online at
ftp://reports.stanford.edu/pub/cstr/reports/cs/tr/77/589/CS-TR-77-589.pdf. [323]
24. A. R. Hanson and E. M. Riseman, “VISIONS: A Computer System for Interpreting
Scenes,” in A. Hanson and E. Riseman (eds.), Computer Vision Systems, pp. 303– 333, New
York: Academic Press, 1978. [323]25. W. C. Mann, “Design for Dialogue Comprehension,” in Proceedings of the 17th Annual
Meeting of the Association of Computational Linguistics, pp. 83–84, La Jolla, CA, August
1979; available online at http://ucrel.lancs.ac.uk/acl/P/P79/P79-1020.pdf. [323]
26. Donald A. Norman, “Copycat Science or Does the Mind Really Work by Table
Look-up?,” in R. Cole (ed.), Perception and Production of Fluent Speech, Chapter 12,
Hillsdale, NJ: Lawrence Erlbaum Associates, Inc., 1980. [323]325
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19

NOTES

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20.1

Chapter 20

Progress in Computer
VisionBeginning around 1970 computer vision research grew into a highly developed
subspecialty of AI, joining other specialized areas such as natural language
processing, robotics, knowledge representation, and reasoning (to name just a
few of them). In this chapter I’ll describe some of the important advances incomputer vision during this period. Some of these were made in pursuit of
specific applications in several fields such as aerial reconnaissance,
cartography, robotics, medicine, document analysis, and surveillance.1

20.1

Beyond Line-Finding

In an earlier chapter, I described some filtering techniques for enhancing image
quality and for extracting edges and lines in images. But much more can bedone to extract properties of a scene using specific information about theconditions under which images are obtained and general information about theproperties of objects likely to be in the scene.

20.1.1

Shape from Shading

In what has been called a “back-to-basics” movement, researchers began
investigating how information about the physics and geometry of lightreflection from surfaces could be used to reveal three-dimensional properties ofa scene from a single two-dimensional image. A leader in this study was
Berthold K. P. Horn (1943– ; Fig. 20.1). His MIT Ph.D. dissertation derived
mathematical methods for determining the shape of an object from its
shading.2 Just as humans perceive an appropriately shaded image of a circle
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20Progress in Computer Vision

as a sphere, a computer vision system can be made to do so also. Making it doso, using information about the reflective properties of surfaces and thegeometry of the imaging process, is what Horn did.

Figure 20.1: Berthold Horn (left) and a shaded circle (right). (Photographcourtesy of Berthold Horn.)
The basic idea of Horn’s technique can be explained by referring to Fig.
20.2 in which an infinitesimal piece of surface receives illumination from a lightsource at an angle equal to i relative to the direction that points
perpendicularly away from the surface piece. Suppose a light sensor (such as a
TV camera), at an angle g relative to the direction of the light source and at
an angle e relative to the direction of the surface, gathers the light reflectedfrom the surface. The amount of light gathered from this surface patch
depends on these three angles, the amount of illumination, and the reflectanceproperties of the surface. (Horn assumed what we would call a “matte”
surface.) Because the amount of light gathered does vary in this manner, theimage appears “shaded.” Under certain circumstances, and with quite a bit ofmathematical manipulation, the direction of the surface can be calculated if
the other quantities are known. Then, by knowing the direction for many,
many infinitesimal pieces of surface, the overall shape of the surface can becalculated (under the assumption that the surface is relatively smooth with noabrupt discontinuities).
Horn is now a professor of computer science and electrical engineering at
MIT and continues to work on several topics related to computer vision. His
thesis elicited a flurry of activity in the area of “shape from shading.”3 Several
people extended the idea of shape from shading to attempt to calculate shapebased on things other than shading, such as from multiple images (stereo),
motion, texture, and contour. And, as we shall see in the next few pages,
important work was done in extracting more than just the shape of objects.

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20.1

Beyond Line-Finding

Figure 20.2: Light incident on and reflected by a small piece of a surface. (Illustration used with permission of Berthold Horn.)

20.1.2The 2 12 -D Sketch

Even though a viewer sees only a two-dimensional image of athree-dimensional scene, David Marr (augmenting Horn’s ideas) observed that,nevertheless, a viewer is able to infer (and thus perceive) from image shading
and other depth cues some of the scene’s three-dimensional attributes, such as
surface shapes, shapes occluding other shapes, abrupt changes between smoothsurfaces, and other depth information. Marr called the representation of these
attributes a “2 12 -D sketch” (because it was not fully three dimensional).
According to Marr’s theory of vision (described in his book4 ), the next step ofvisual processing, after producing the primal sketch (see p. 180) of blobs andedges, is to produce this 2 21 -D sketch. An example sketch is shown in Fig. 20.3
in which arrows pointing perpendicularly away from surfaces are superimposedon the primal sketch of an image from which they are inferred.
Finally, according to Marr, the information in the 2 12 -D sketch, along withstored information about object shapes, would be used to locate specificobjects in the image and thus produce a 3-D model of the scene. I’ll describe
what he had to say about that process shortly.

20.1.3Intrinsic Images

Two researchers at SRI, Jay Martin Tenenbaum (1943– ; Fig. 2.4) and Harry
Barrow (recently relocated from Edinburgh), developed some image-processing
techniques quite similar to those used in producing the 2 21 -D sketch.5 They
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20Progress in Computer Vision

Figure 20.3: A 2 12 -D sketch. (From David Marr and H. K. Nishihara, “Representation and Recognition of the Spatial Organization of Three-Dimensional
Shapes,” Proceedings of the Royal Society of London, Series B, Biological Sciences, Vol. 200, No. 1140, p. 274, February 23, 1978.)noted that the intensity value at each pixel of an image resulted from atangled combination of several factors, including properties of the ambient
illumination and reflective and geometric properties of objects in the scene.
They thought that these factors could be untangled to recover important
three-dimensional information about the scene.
Barrow and Tenenbaum proposed that each of these factors (all of which
influenced intensity) could be represented by imaginary images that theycalled “intrinsic images.” These images were to consist of a grid of “pixels”
overlaying a projection of the scene and in registration with the intensity
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20.1

Beyond Line-Finding

Figure 20.4: Jay Martin Tenenbaum (left) and Harry Barrow (right). (Photographs courtesy of J. Martin Tenenbaum and of Harry Barrow.)
image. One intrinsic image, for example, was an illumination image. It
consisted of pixels whose values were the amounts of illumination falling on
the pixels of the projected scene. These values, of course, were not known, but
Barrow and Tenenbaum proposed that they could be estimated from theintensity image and from the other intrinsic images.
As examples, I show a set of such intrinsic images in Fig. 20.5. The actual
image of intensity values is shown at the top. The known value of a pixel inthat image depends on the unknown values of pixels in the intrinsic images
below. In fact, the values of the pixels in all of the images, intrinsic andactual, are interdependent. The arrows in the figure reflect that fact. (There
should also be some arrows going up.) Based on the values of pixels in some of
the images, the values of others can be computed by using known physicalrelationships, constraints among the images, and other reasonableassumptions. These values, in turn, allow the computation of others. In
essence, these computations “propagate” pixel values throughout the set of
intrinsic images (much like how levels in the Blackboard architecture affectother levels). As Barrow and Tenenbaum later summarized their method, “Weenvisaged this recovery process as a set of interacting parallel local
computations, more like solving a system of simultaneous equations byrelaxation than like a feed forward sequence of stages.”6 Barrow andTenenbaum also used some of their ideas about intrinsic images to work on the
problem of interpreting line drawings as three-dimensional surfaces.7

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20Progress in Computer Vision

Figure 20.5: Intrinsic images. (Used with permission of Harry Barrow and Jay
M. Tenenbaum.)
Barrow and Tenenbaum intended their work to be useful not only incomputer vision but also as a potential model of “precognitive” vision
processes in humans. However, in a 1993 “retrospective” about their work
they wrote8
Despite the maturity of computational vision and the rapid
developments in neural systems, we still have a long way to go
before we can come close to our goal of understanding visual
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20.2

Finding Objects in Scenes
perception. To do so we will need to draw upon what we havelearned in many fields, including neuroscience, neural networks,
experimental psychology and computational vision.

20.2

Finding Objects in Scenes

20.2.1

Reasoning about Scenes

Even before the development of shape-from-shading and other methods forrecovering depth information from scenes, a number of researchers had worked
on methods for finding objects in scenes. I described many of these techniques
in Section 9.3.
During the early 1970s, Thomas Garvey completed a Stanford Ph.D.
thesis on a system for locating objects, such as desks, chairs, andwastebaskets, in images of office scenes.9 As Garvey wrote in his summary,
The system uses information about the appearances of objects,
about their interrelationships, and about available sensors to
produce a plan for locating specified objects in images of room
scenes.
In related work, Barrow and Tenenbaum developed a system, called
MSYS, for reasoning about scenes “in which knowledge sources compete andcooperate until a consistent explanation of the scene emerges by consensus.”10
MSYS analyzed images of office scenes and attempted to find the most likelyinterpretation for the regions in an image (desk top, back of chair, floor,
doorway, and so on) given a number of candidate interpretations and theirprobabilities. Knowing relationships between regions (such as “chair backs areusually adjacent to chair seats”), MSYS tried to find the most likely overall setof region interpretations.
An example of a scene considered by MSYS is shown in Fig. 20.6. Some of
the regions in the scene have been detected and labeled with possible
interpretations.
As Barrow and Tenenbaum wrote, MSYS’s reasoning might proceed asfollows:
Regions PIC, WBSKT, and CBACK cannot be WALL or DOOR,
because their brightnesses are much less than that along the topedge of the image vertically above them, which violates [knowledge
about the brightness of walls and doors]. Consequently, region PIC
must be the PICTURE, WBSKT must be WASTEBASKET, and
CBACK must be CHAIRBACK.
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20Progress in Computer Vision

Figure 20.6: An MSYS scene with some regions detected and labeled. (Illustration used with permission of SRI International.)
Region LWALL and RWALL must then be WALL, since they are
adjacent to region PIC, and DOOR cannot be adjacent to
PICTURE.
Region DR cannot be WALL because all regions labeled WALL arerequired to have the same brightness. Therefore, region DR must
be DOOR.

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20.2

Finding Objects in Scenes

20.2.2Using Templates and Models

Much of the early work on object recognition was based on using object“templates” that could be matched against images. Martin A. Fischler andRobert A. Elschlager elaborated this idea by using “stretchable templates”
that permitted more powerful matching techniques. They used these to findobjects such as faces or particular terrain features in photographs containing
such objects.11 The process depended on having a general representation forthe object being sought and then a process for matching that representation
against the photograph. Their representations were based on breaking an
object down into a number of primitive parts and “specifying an allowable
range of spatial relations which these ‘primitive parts’ must satisfy for theobject to be present.” For the object to be present in a picture, “it is requiredthat [the] primitives occur (or at least that some significant subset of themoccurs), and also that they occur within a certain spatial relationship one tothe other. . . ” As Fischler and Elschlager pointed out, it is usually the casethat determining whether or not some of the parts occur depends on whether
or not the whole object occurs, and vice versa. The main contribution of theirpaper was the development of a dynamic-programming-style method for
dealing with this circularity.
Earlier I had described David Marr’s work on processes for producing a
primal sketch and a 2 21 -D sketch. These were the first two stages in Marr’s
theory of vision. He argued that these stages could uncover important shapeinformation without specific knowledge of the shapes of objects likely to be ina scene. He had written:12
Most early visual processes extract information about the visible
surfaces directly, without particular regard to whether they happen
to be part of a horse, or a man, or a tree. . . . As for the question ofwhat additional knowledge should be brought to bear, generalknowledge must be enough – general knowledge embedded in theearly visual processes as general constraints, together with thegeometrical consequences of the fact that the surfaces co-exist in
three-dimensional space.
Specific knowledge about shapes, he argued, should be utilized in a third
stage. It is this stage that uses three-dimensional models of objects. He
proposed using a hierarchy of models in which a gross model is decomposed
into subparts and these into subsubparts and so on. For example, the shape ofa human might be modeled as in Fig. 20.7. Each box corresponds to a 3-D
model and its submodel. On the left side of the box is an axis-oriented model;on the right side is how that model is represented as submodels. (Directions of
the axes can be adjusted to fit matching parts of the image.)In this third stage, comparing models of this sort with shape informationand other 3-D information contained in the 2 12 -D sketch helps to identify and335
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20Progress in Computer Vision

Figure 20.7: An example of one of Marr’s 3-D model hierarchies. (From David
Marr, Vision, San Francisco: W. H. Freeman and Co., p. 306, 1982.)
locate objects in a scene. For Marr, vision was “the process of discovering fromimages what is present in the world and where it is.”13
Marr was not the first to suggest the use of cylinders as models of parts ofobjects. In a 1971 IEEE conference paper, Thomas O. Binford (1936– )
introduced the idea of “generalized cylinders” (sometimes called “generalized
cones”).14 A later paper defined them as follows: “A generalized cone isdefined by a planar cross section, a space curve spine, and a sweeping rule. It
represents the volume swept out by the cross section [not necessarily a circularone] as it is translated along [an axis called a spine], held at some constantangle to the spine, and transformed according to the sweeping rule.”15
Binford had several Stanford Ph.D. students who used models to help
identify objects in scenes. Of these I might mention Gerald J. Agin,16
Ramakant Nevatia,17 and Rodney A. Brooks (1954– ),18 all of whom
contributed to what came to be called “model-based vision.” (Brooks later
became a professor at MIT, where he worked on other topics. His subsequent
work will be discussed later.)
Brooks’s ACRONYM system19 used generalized cones to model several
different kinds of objects. ACRONYM used these models to help identify andlocate objects in images. Some examples of the kinds of generalized cones thatcan be used as building blocks of models and model objects are shown in Fig.
20.8.
Other views regarding what vision is all about competed with those ofMarr and others who were attempting to use vision to reconstruct entire
scenes. Some, especially those involved in robotics, claimed that the purposeof vision was to perceive just what was required to guide action. Many of the336
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20.2

Finding Objects in Scenes

Figure 20.8: Primitive generalized cones and piston models constructed fromgeneralized cones. (From Rodney A. Brooks, “Symbolic Reasoning among 3-D
Models and 2-D Images,” Artificial Intelligence, Vol. 17, Nos. 1–3, pp. 285–348,
1981.)
vision routines in Shakey were embedded in its action programs. Professor
Yiannis Aloimonos at the University of Maryland is one of the researchers
advocating this “purposive” or “interactive” approach. He claims that the goalof vision is action. When vision is “considered in conjunction with action, it
becomes easier.” He goes on to explain that “the descriptions of space-timethat the system needs to derive are not general purpose, but are purposive.
This means that these descriptions are good for restricted sets of tasks, suchas tasks related to navigation, manipulation and recognition.”20 In theneuroscience community, to which Marr wanted to make a contribution, there
were Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski,
who later wrote “What is vision for? Is a perfect internal recreation of thethree-dimensional world really necessary? Biological and computational
answers to these questions lead to a conception of vision quite different frompure vision [as advocated by Marr]. Interactive vision. . . includes vision with

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20Progress in Computer Vision

other sensory systems as partners in helping to guide actions.”21
In any case, models still play an important role in computer vision.
(However, one prominent vision researcher told me that the “residue ofmodel-based vision is close to zero,”22 and another told me that “most current
robotic systems use vision hacks” instead of general-purpose, science-based
scene-analysis methods.23 )20.3

DARPA’s Image Understanding Program

Much of the computer vision work in the United States was being funded by
DARPA, and there were concerns among vision researchers (as always) about
continuing support. Tenenbaum recalls attending a DARPA meeting in 1974
where the future of computer vision research was being discussed. The
program officer monitoring DARPA-supported vision work, Air Force Major
David L. Carlstrom, was at the meeting and was interested in pulling together
the various efforts in the field. Because DARPA had been supporting work inthis area for some years, Carlstrom needed a new name that would indicatethat DARPA was starting something new. Tenenbaum told me that herecommended to Carlstrom that the new initiative be called “the image
understanding program.”24 (Recall that there was already an ongoing
DARPA-supported effort in speech understanding, so the phrase sounded“DARPA-friendly.”)
In 1976, DARPA launched its Image Understanding (IU) program. It
grew to be a major effort composed of the leading research laboratories doing
work in this area as well as “teams” pairing a university with a company. The
individual labs participating were those at MIT, Stanford, University ofRochester, SRI, and Honeywell. The university/industry teams were
USC–Hughes Research Laboratories, University of Maryland–Westinghouse,
Inc., Purdue University–Honeywell, Inc., and CMU–Control Data Corporation.
Regular workshops were held to report progress. The proceedings of one
held in April 1977 stated the goals of the program: “The Image Understanding
Program is planned to be a five year research effort to develop the technology
required for automatic and semiautomatic interpretation and analysis ofmilitary photographs and related images.”25 DARPA’s ultimate goal for the
IU program was well captured by the illustration on the cover of thatproceedings, shown in Fig. 20.9.
As the diagram implies, military commanders would like computer visionsystems to be able to analyze a photograph and to produce a written
description of its important components and their relationships.
Some of the computer vision research that I have already described, suchas work on the 2 12 -D sketch, intrinsic images, generalized cylinders, and
ACRONYM, was supported by the IU Program. But there was always some
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20.3

DARPA’s Image Understanding Program

Figure 20.9: An illustration of IU goals. (Illustration used with permission of
SAIC.)
tension between DARPA’s goals and those of people doing computer visionresearch. DARPA wanted the program to produce “field-able” systems. J. C.
R. Licklider emphasized this point at a preliminary IU workshop in March
1975:26
At the end of the five year period the technology developed must
be in a state in which it can be utilized by the DoD components tosolve their specific problems without requiring a significant
research effort to figure our how to apply the technology to thespecific problems. For this reason, the program must result in ademonstration at the end of the five year period that an important
DoD problem has been solved.
Air Force Major Larry Druffel at DARPA assumed leadership of the IU
program in 1978. In November 1978, he advised “The prudent approach is toconsolidate those techniques which are sufficiently mature to transfer to DoD
agencies.”27 By 1979, the program’s goals had expanded to include
cartography and mapping. A “memorandum of understanding” (MOU)
between DARPA and the Defense Mapping Agency (DMA) was concluded tosupport automatic mapping efforts through the development of a
DARPA/DMA “testbed.” In November 1979, Druffel wrote28
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20Progress in Computer Vision
Plans are progressing for a demonstration system to evaluate thematurity of IU technology by automating mapping, charting, andgeodesy functions. While focussing on specific cartographic
photointerpretation functions, the system should offer the entire
image exploitation community an opportunity to assess the futureapplication of Image Understanding methodologies to their specificproblem.

The “five-year” program did not end in 1981. It continued under the
DARPA leadership of Navy Commander Ron Ohlander, Air Force Lt. Col.
Robert L. Simpson Jr., and others until approximately 2001. In 1985 Simpson
summarized some of its accomplishments:29
Originally conceived as a five year program in 1975 by Lt. Col.
David Carlstrom, the first several years of IU established thestrong base of low-level vision techniques and knowledge-based
subsystems that began to differentiate computer vision from what
is usually called “image processing.” In the late 1970s and early
1980s, under the direction of Lt. Col. Larry Druffel, the program
saw the development of model-based vision systems such as
ACRONYM and demonstration of IU techniques in more meaningfulconcept demonstrations such as the DARPA/DMA image
understanding testbed. These demonstrations and their potential
for future military use warranted the continuation of the IU
program beyond its initial five year lifespan. Under Cmd. Ron
Ohlander, IU technology continued to mature to the point that the
DARPA Strategic Computing Program could justify a major
application, the autonomous land vehicle.
As Ohlander said, the IU program was extended beyond its projectedfive-year lifetime. It is said that even as early as 1984, DARPA had spent over
$4 million on this effort.30 One potential application was computer vision for
robot-controlled military vehicles – a component of DARPA’s “Strategic
Computing” program. I’ll describe that application and others in more detailin later chapters.
As a growing subspecialty of artificial intelligence, papers on computer
vision began to appear in new journals devoted to the subject including
Computer Vision and Image Understanding and IEEE Transactions on
Pattern Analysis and Machine Intelligence. The field’s textbooks around thistime included Pattern Classification and Scene Analysis31 and two books
titled Computer Vision.32

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20.3

NOTES

Notes
1. For an extensive list of computer vision applications see the CVonline Web site at
http://homepages.inf.ed.ac.uk/rbf/CVonline/applic.htm. [327]2. Berthold K. P. Horn, “Shape from Shading: A Method for Obtaining the Shape of a
Smooth Opaque Object from One View,” MIT Department of Electrical Engineering Ph.D.
thesis, MIT Artificial Intelligence Laboratory Technical Report 232, November 1970;
available online at http://people.csail.mit.edu/bkph/AIM/AITR-232-OCR-OPT.pdf. In histhesis, Horn credits Thomas Rindfleisch’s 1966 work on using image brightness in studies oflunar topography. [327]3. For a modern discussion of the problem, see, for example, Emmanuel Prados and Olivier
Faugeras, “Shape from Shading,” in N. Paragios, Y. Chen, and O. Faugeras (eds.), Handbookof Mathematical Models in Computer Vision, pp. 375–388, New York: Springer-Verlag,
2006; available online at
http://perception.inrialpes.fr/Publications/2006/PF06a/chapter-prados-faugeras.pdf. [328]4. David Marr, Vision: A Computational Investigation into the Human Representation
and Processing of Visual Information, San Francisco: W. H. Freeman and Co., 1982. [329]
5. Harry G. Barrow and Jay Martin Tenenbaum, “Recovering Intrinsic Scene
Characteristics from Images,” in A. Hanson and E. Riseman (eds.), Computer Vision
Systems, pp. 3–26, New York: Academic Press, 1978. Available online at
http://web.mit.edu/cocosci/Papers/Barrow-Tenenbaum78.pdf and at
http://www.ai.sri.com/pubs/files/737.pdf. [329]
6. Harry G. Barrow and Jay Martin Tenenbaum, “Retrospective on ‘Interpreting Line
Drawings as Three-Dimensional Surfaces,’” Artificial Intelligence, Vol. 59, Nos. 1–2, pp.
71–80, 1993. [331]
7. Harry G. Barrow and Jay Martin Tenenbaum, “Interpreting Line Drawings as
Three-Dimensional Surfaces,” Artificial Intelligence, Vol. 17, pp. 75–116, 1981. Available
online at http://web.mit.edu/cocosci/Papers/Barrow-Tenenbaum81.pdf. [331]
8. Harry G. Barrow and Jay Martin Tenenbaum, “Retrospective on ‘Interpreting Line
Drawings as Three-Dimensional Surfaces,’” Artificial Intelligence, Vol. 59, Nos. 1–2, pp.
71–80, 1993. [332]
9. Thomas D. Garvey, “Perceptual Strategies for Purposive Vision,” Stanford University
Ph.D. thesis, published as SRI International AI Center Technical Note 117, September 1976.
Abstract available online at http://www.ai.sri.com/pub list/759. [333]10. Harry G. Barrow and J. Martin Tenenbaum, “MSYS: A System for Reasoning about
Scenes,” SRI International AI Center Technical Note 121, April 1976. Available online at
http://www.ai.sri.com/pubs/files/757.pdf. [333]11. Martin A. Fischler and Robert A. Elschlager, “The Representation and Matching of
Pictorial Structures,” IEEE Transactions on Computers, Vol. C-22, No. 1, pp. 67–92,
January 1973. [335]
12. David Marr, op. cit., pp. 272–4. [335]
13. David Marr, op. cit., pp. 23–60. [336]
14. Thomas O. Binford, “Visual Perception by Computer,” Proceedings of the IEEE
Conference on Systems and Control, Miami FL, December 1971. [336]
15. Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Artificial
Intelligence, Vol. 17, Nos. 1–3, pp. 285–348, 1981. [336]
16. Gerald J. Agin, “Representation and Description of Curved Objects,” Stanford
University Ph.D. thesis, published as Stanford Artificial Intelligence Project Memo AIM-173,

341
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20

NOTES

October 1972. See also Gerald J. Agin and Thomas O. Binford, “Computer Descriptions ofCurved Objects,” Proceedings of the Third International Joint Conference on Artificial
Intelligence, pp. 629–640, August 1973; later published as Gerald J. Agin and Thomas O.
Binford, “Computer Descriptions of Curved Objects,” lEEE Transactions on Computers,Vol. 25, No. 4, April 1976. [336]
17. Ramakant Nevatia, “Structured Descriptions of Complex Curved Objects for
Recognition and Visual Memory,” Stanford University Department of Electrical Engineering
Ph.D. thesis, published as Stanford Artificial Intelligence Laboratory Memo AIM-250,
October 1974. [336]
18. Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Stanford
University Computer Science Department Ph.D. thesis, 1981, published as Stanford CS
Department Report STAN-CS-81-861. Also published as Rodney A. Brooks, op. cit.. [336]
19. The system was first reported in Rodney A. Brooks, Russell Greiner, and Thomas O.
Binford, “The ACRONYM Model-Based Vision System,” Proceedings of the Sixth
International Joint Conference on Artificial Intelligence, pp. 105–113, Tokyo, 1979. A later
revised version was reported in Brooks’s Artificial Intelligence paper just cited. [336]20. From his Web page at http://www.cfar.umd.edu/∼yiannis/. [337]
21. Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski, “A Critique ofPure Vision,” in Christof Koch and Joel L. Davis (eds.), Large-Scale Neuronal Theories of
the Brain, pp. 23–65, Cambridge, MA: MIT Press, 1994. Available online at http://philosophy.ucsd.edu/faculty/pschurchland/papers/kochdavis94critiqueofpurevision.pdf.
[338]
22. Martin A. Fischler, private communication, August 1, 2007. [338]23. Jay Martin Tenenbaum, private communication, July 31, 2007. [338]24. Private communication, July 31, 2007. [338]
25. Lee S. Bauman (ed.), Proceedings: Image Understanding Workshop, Science
Applications, Inc., Report No. SAI-78-549-WA, April 1977. [338]26. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, publishedby Science Applications, Inc., May 1978. [339]27. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, publishedby Science Applications, Inc., November 1978. [339]28. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, publishedby Science Applications, Inc., November 1979. [339]29. Quoted in the Foreword of the Proceedings: Image Understanding Workshop, publishedby Science Applications International Corporation, December 1985. [340]30. Alex Roland with Philip Shiman, Strategic Computing: DARPA and the Quest for
Machine Intelligence, p. 220, Cambridge, MA: MIT Press, 2002. [340]
31. Richard O. Duda and Peter E. Hart, Pattern Classification and Scene Analysis, New
York: John Wiley and Sons, Inc., 1973. [340]32. Michael Brady, Computer Vision, Amsterdam: North-Holland Publishing Co., 1981, andDana H. Ballard and C. M. Brown, Computer Vision, New York: Prentice Hall, Inc., 1982.
[340]

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21.0

Chapter 21

Boomtimes
Even though the Mansfield Amendment and the Lighthill report caused
difficulties for basic AI research during the 1970s, the promise of important
applications sustained overall funding levels from both government andindustry. Excitement, especially about expert systems, reached a peak during
the mid-1980s.
I think of the decade of roughly 1975–1985 as “boomtimes” for AI. Even
though the boom was followed by a period of retrenchment, its
accomplishments were many and important. It saw the founding in 1980 of theAmerican Association for Artificial Intelligence (AAAI – now called the
Association for the Advancement of Artificial Intelligence), with annual
conferences, workshops, and symposia. (Figure 21.1 shows a scene from one of
the many trade shows during this era.) Several other national and regional AI
organizations were also formed. The Arpanet, which had its beginnings at a
few research sites in the late 1960s, gradually evolved into the Internet, linking
computers worldwide.
Various versions of the LISP programming language coalesced into
INTERLISP, which continued as the predominant language for both AI research
and applications (although PROLOG was a popular competitor in Europe,
Canada, and Japan). Researchers and students at MIT designed
work-station-style computers, called Lisp machines, that ran LISP programs
efficiently. Lisp Machines, Inc., and Symbolics were two companies that built
and sold these machines. They enjoyed initial success but gradually lost out toother providers of workstations.1
Many other AI companies joined the expert systems companies and theLisp machine companies. For example, in 1978 Earl Sacerdoti and Charles
Rosen founded Machine Intelligence Company to market robot vision systems.
In 1984 Cuthbert Hurd (1911–1996), who had earlier helped IBM develop its
first computer, and David Warren founded Quintus, Inc., to market PROLOG

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21

Boomtimes

systems. In 1984 Fritz Kunze, a graduate student at UC Berkeley, foundedFranz, Inc., to market FranzLISP, a version of the LISP programming language.2
Lavish exhibits at trade shows associated with AI conferences charged thewhole field with excitement. Membership in the AAAI rose from around 5,000
shortly after the society’s founding to a peak of 16,421 in 1987. (AAAI
membership has since leveled off, after the boom, back to around 5,000.) Mostof these new members – curious about what AI could do for them – came fromindustry and government agencies. Tutorials about various AI topics at both
AAAI and IJCAI conferences were very well attended by people from industry
wanting to learn about this newly important field.
During the early 1980s, my colleagues in several departments at SRI,
especially those working on Defense Department projects, were eager to gethelp from the SRI AI Center – of which I was the director at the time. Mainly,
I thought, they wanted us to “sprinkle a little AI” on their proposed projects
to make them more enticing to government sponsors.

Figure 21.1: Scene from one of the AAAI trade shows during the 1980s. (Photograph from Bruce B. Buchanan, “Some Recollections about the Early Daysof AAAI,” AI Magazine, Vol. 26, No. 4, p. 14, c 2005 Association for the
Advancement of Artificial Intelligence. Used with permission.)
Reporting on this increasing interest in 1984, the science writer George
Johnson wrote3“We’ve built a better brain,” exclaimed a brochure for [an expert
system called] TIMM, The Intelligent Machine Model: “Expert
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21.0

NOTES
systems reduce waiting time, staffing requirements and bottlenecks
caused by the limited availability of experts. Also, expert systems
don’t get sick, resign, or take early retirement.” Other companies,
such as IBM, Xerox, Texas Instruments, and Digital Equipment
Corporation, were more conservative in their pronouncements. But
the amplified voices of their salesmen, demonstrating various wares[in the 1984 AAAI exhibit hall], sounded at times like carnivalbarkers, or prophets proclaiming a new age.

The boom continued with Japan’s “Fifth Generation Computer Systems”
project. That project in turn helped DARPA justify its “Strategic Computing
Initiative.” It also helped to provoke the formation of similar research efforts in
Europe (such as the ALVEY Project in the United Kingdom and the European
ESPRIT programme) as well as the formation of American industrial consortia
for furthering advances in computer hardware. Assessments of some of AI’sdifficulties and achievements, compared to some of its promises, led to the endof the boom in the late 1980s – causing what some called an “AI winter.” I’ll
be describing all of these topics in subsequent chapters.

Notes
1. By the way, the “iwhois” Web site (http://www.iwhois.com/oldest/) lists Symbolics as
having the oldest registered “.com” domain name (registered on March 15, 1985.) [343]
2. See http://www.franz.com/about/company.history.lhtml. [344]
3. George Johnson, “Thinking about Thinking,” APF Reporter, Vol. 8, No. 1, 1984.
Available online at http://www.aliciapatterson.org/APF0801/Johnson/Johnson.html. [344]345
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21

NOTES

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21.0Part V“New-Generation” Projects

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21

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22.1

Chapter 22

The Japanese Create a Stir
22.1

The Fifth-Generation Computer Systems
Project

In 1982, Japan’s Ministry of International Trade and Industry (MITI)
launched a joint government and industry project to develop what they called“Fifth Generation Computer Systems” (FGCS). Its goal was to produce
computers that could perform AI-style inferences from large data andknowledge bases and communicate with humans using natural language. As
one of the reports about the project put it, “These systems are expected tohave advanced capabilities of judgement based on inference andknowledge-base functions, and capabilities of flexible interaction through an
intelligent interface function.”1
The phrase “Fifth Generation” was meant to emphasize dramatic
progress beyond previous “generations” of computer technology. The first
generation, developed during and after World War II, used vacuum tubes.
Around 1959, transistors replaced vacuum tubes – giving rise to the second
generation – although the transistors, like the vacuum tubes before them, werestill connected to each other and to other circuit components using copper
wires. During the 1960s, transistors and other components were fabricated on
single silicon wafer “chips,” and the several chips comprising a computer wereconnected together by wires. Computers using this so-called small-scale
integration (SSI) technology comprised the third generation. In the late 1970s,
entire microprocessors could be put on a single chip using “very large-scale
integration” (VLSI) technology – the fourth generation. The Japanese fifth
generation, besides its sophisticated software, was to involve many parallel
processors using “ultra large-scale integration” (ULSI).
MITI planned to develop a prototype machine, in the form of what

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22

The Japanese Create a Stircomputer scientists were beginning to call a “workstation,” which was toconsist of several processors running in parallel and accessing multiple data
and knowledge bases. PROLOG, the computer programming language based on
logic, was to be the “machine language” for the system because the Japanesethought it would be well suited for natural language processing, expert
reasoning, and the other AI applications they had in mind. Execution of a
PROLOG statement involved logical inference, so the machine’s performance
was to be measured in logical inferences per second (LIPS). In the early 1980s,
computers were capable of performing around 100,000 LIPS. The Japanesethought they could speed that up by 1,000 times and more. Later in the
project, because of difficulties of adapting PROLOG to run concurrently on
many processors, a new logic-based language, GHC (for Guarded Horn
Clauses), was developed that could run on multiple processing units.
For work on FGCS, MITI set up a special institute called the “Institutefor New Generation Computer Technology” (ICOT). Its Research Center,headed by Mr. Kazuhiro Fuchi (1936–2006; Fig. 22.1), was to carry out thebasic research needed to develop a prototype system. According to its
first-year progress report,2 the “Research Center started with forty top-levelresearchers from the Electrotechnical Laboratories (ETL), Nippon Telephone
and Telegraph Public Corporation (NTT), and eight computer
manufacturers.” The project had a ten-year plan: three years of initialresearch, four years of building intermediate subsystems, and a final three
years to complete the prototype. In 1993, the project was extended for two
years to disseminate FGCS technology.
Koichi Furukawa (1942– ; Fig. 22.1), a Japanese computer scientist, wasinfluential in ICOT’s decision to use PROLOG as the base language for theirfifth-generation machine. Furukawa had spent a year at SRI during the 1970s,
where he learned about PROLOG from Harry Barrow and others. Furukawa
was impressed with the language and brought Alain Colmerauer’s interpreter
for it (written in FORTRAN) back to Japan with him. He later joined ICOT,
eventually becoming a Deputy Director. (He is now an emeritus professor at
Keio University.)
The architecture of the planned fifth-generation system is illustrated inFig. 22.2. Various hardware modules for dealing with the knowledge base,inference, and interface functions were to be implemented using advanced chiptechnology. The hardware would be controlled with corresponding softwaremodules, and interaction with the system would be through speech, natural
language, and pictures.
According to a set of slides by Mr. Shunichi Uchida summarizing the
FGCS project,3 its total ten-year budget was U54.2 billion or approximately(at the 1990 exchange rate) $380 million.
During this time, the project made advances in parallel processing, incomputer architecture, and in developing various AI systems. Several

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22.1

The Fifth-Generation Computer Systems Project

Figure 22.1: Kazuhiro Fuchi (left) and Koichi Furukawa (right). (Fuchi photograph courtesy of Tohru Koyama. Furukawa photograph courtesy of Koichi
Furukawa.)
American and European visitors (especially PROLOG experts) participated in
the project as ICOT visitors. Indeed, the Japanese invited international
participation in the project. International conferences were held in Tokyo in
1984, 1988, and 1992.4
ICOT built a number of “parallel inference machines” (PIMs). The
largest of these, named PIM/p, had 512 processing units.5 (See Fig. 22.3.)
Several AI systems were developed to run on these machines. Among thesewere MGTP (an acronym for Model Generation Theorem Prover), a parallel
theorem prover;6 MENDELS ZONE, a system for automatic program
generation;7 and HELIC-II, a legal reasoning expert system.8
Many observers think that most of the results of the FGCS project are
now of historical interest only. The software developed did not find notableapplications. Improvements in the speed and power of commercial
workstations (and even of personal computers) made these superior to the
PIMs. Taking full advantage of the benefits of parallel processing proved
difficult except for special problems susceptible to that style of computation.
The development of graphical user interfaces (GUIs) during the late 1980s and1990s provided acceptable methods for human–computer interaction –reducing (at least for a time) the need for AI-dependent natural language andspeech understanding systems. One legacy of the project is the journal New
Generation Computing, of which Koichi Furukawa was once editor-in-chief. A“Museum” Web page for the FGCS project is maintained at
http://www.icot.or.jp/ARCHIVE/HomePage-E.html. The page contains links
to several ICOT publications, software, and other information.
351
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The Japanese Create a Stir

Figure 22.2: Fifth-generation system architecture. (Illustration used with permission of Edward Feigenbaum.)
A 1993 article, with contributions from several knowledgeable people,
reflected about the project.9 Evan Tick, one of the contributors who had spenttime at ICOT, had this to say:10. . . I highly respect the contribution made by the FGCS project in
the academic development of the field of symbolic processing,notably implementation and theory in logic programming,
constraint and concurrent languages, and deductive andobject-oriented databases. In my specific area of parallel logic
programming languages, architectures, and implementations, ICOT
made major contributions, but perhaps the mixed schedule ofadvanced technology transfer and basic research was ill advised.
352
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22.2

The Fifth-Generation Computer Systems Project

Figure 22.3: The PIM/p parallel computer system. (Photograph from http://www.icot.or.jp/ARCHIVE/Museum/MACHINE/pim-spec-E.html.)
This basic research also led to a strong set of successful
applications, in fields as diverse as theorem proving and biologicalcomputation. In a wider scope, the project was a success in termsof the research it engendered in similar international projects, suchas ALVEY, ECRC, ESPRIT, INRIA, and MCC. These
organizations learned from one another, and their academic
competitiveness in basic research pushed them to achieve a broader
range of successes. In this sense, the computer science community
is very much indebted to the “fifth-generation” effort.
Separately from what might or might not have been accomplished during
the project, announcements about it in 1980 and 1981 provoked similar
projects in the United States and in Europe. News about the project was
spread by an early document titled “Preliminary Report on a Fifth Generationof Computers,” which was circulated among a few computer science
researchers in the fall of 1980. Also, an international conference to announce
the FGCS project was held in Tokyo in October of 1981.11

353
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22

The Japanese Create a Stir

22.2

Some Impacts of the Japanese Project

22.2.1The Microelectronics and Computer Technology
Corporation

The announcements by MITI of plans for a fifth-generation computer systemand the formation of ICOT caused alarm in the United States and Europe.
The American computer industry, all too aware of growing Japanese
dominance in consumer electronics and in manufacturing, worried that its
current world leadership in computer technology might be eroded.
William Norris, the founder of the Control Data Corporation, organized ameeting of computer industry executives in Orlando, Florida, in February 1982
to discuss the creation of a research and development consortium. Its goal
would be to develop technologies that the member companies could ultimately
use in their products. This meeting led in late 1982 and early 1983 to theformation of the nonprofit Microelectronics and Computer Technology
Corporation (MCC) in Austin, Texas. Admiral Bobby Ray Inman, a former
Director of the National Security Agency and a Deputy Director of the Central
Intelligence Agency, was chosen to be its first President, Chairman, and Chief
Executive Officer. Among the early joiners of the consortium were the Digital
Equipment Corporation, Harris, Control Data, Sperry-Univac, RCA, NCR,
Honeywell, National Semiconductor, Advanced Micro Devices, and Motorola.
These were later joined by several others, including Microsoft, Boeing, GE,
Lockheed, Martin Marietta, Westinghouse, 3M, Rockwell, and Kodak.
The annual budget was planned to be between $50 and $100 million –depending on the number of member companies contributing funds andresearch personnel. At its beginning, MCC focused on four major researchareas, namely, advanced computer architectures, software technology,
microelectronics packaging, and computer-aided design of VLSI circuitry. AI
research was to be carried out (under the eventual direction of Woodrow
Bledsoe) as part of architecture research.
Although the member companies did make use of some MCC-sponsoredinnovations, MCC itself began to decline after the departure of Inman in 1987.
By that time, FGCS was perceived as less of a threat, and many of themember companies were having their own financial difficulties. Also, theInternet and the explosive growth and power of personal computers began toeclipse what was going on at MCC. The number of employees fell from its
peak of about 400 in 1985 to 58 in June 2000, when the board voted todissolve the consortium.12354
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22.2

22.2.2

Some Impacts of the Japanese Project

The Alvey Program

In March 1982, the British government set up a committee “to advise on thescope for a collaborative research programme in information technology (IT)and to make recommendations.” It was chaired by Mr. John Alvey, a seniordirector of British Telecommunications. In its report, issued later that year
and titled “A Programme for Advanced Information Technology,” thecommittee noted that the Japanese FGCS project was seen “as a major
competitive threat” and that anticipated responses to it by the United States“would create an equal if not greater degree of competition for the UK
industry.”13 The report recommended “a five-year programme to mobilise theUK’s technical strengths in IT, through a Government-backed collaborative
effort between industry, the academic sector and other research organisations.
The goal [was to develop] a strong UK capability in the core enabling
technologies, essential to Britain’s future competitiveness in the world IT
market.” The four major technical areas identified for support were “Software
Engineering, Man Machine Interfaces (MMI), Intelligent Knowledge Based
Systems (IKBS) and Very Large Scale Integration (VLSI).” The recommended
budget was £350 million, with the government contributing two-thirds of thecost and industry the rest.
In 1983, the UK Government accepted the committee’s report andinitiated the “Alvey Programme” to carry out the committee’s
recommendations. A new Directorate, headed by Brian Oakley, Secretary of
the Science and Engineering Research Council (SERC), was set up in the
Department of Trade and Industry (DTI) to coordinate the program.
Sponsorship and funds were provided by DTI, the Ministry of Defence (MoD),
SERC, and industry. Among its other accomplishments, the Alvey program
helped revitalize AI research in Britain. According to Oakley, “If the Lighthill
Report of the early 1970s was paradise lost for the AI community, the Alvey
Report of the early 1980s was paradise regained.”14
The program reached a peak level of funding of around £45 million in
1987 and went on until 1991. It is credited with energizing Britain’s computerscience community by expanding research and development efforts in bothacademia and industry. In their excellent summary of the program, publishedin 1990, Brian Oakley and Kenneth Owen describe Alvey’s contributions inAI, parallel architecture, VLSI, integrated circuit CAD, software engineering,and speech technology.15

22.2.3

ESPRITIn 1983, the European Economic Community (the predecessor of the European
Union) launched its ESPRIT program. (ESPRIT is an acronym for European
Strategic Program of Research in Information Technology.) According to Luc
Steels and Brice Lepape, who wrote an article focusing on the AI aspects of355
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22

The Japanese Create a StirESPRIT, its goal was “to foster transnational cooperative research amongindustries, research organizations, and academic institutions across theEuropean Community (EC).”16 It was also a European response to theJapanese FGCS program.
ESPRIT was set up to support research in three major categories, namely,
microelectronics, information processing systems (including software andadvanced information processing), and applications (including
computer-integrated manufacturing and office systems). Information
processing, where most AI research was to be supported, was further divided
into knowledge engineering, advanced architectures (including computerarchitectures for symbolic processing), and advanced system interfaces(speech, image, and multisensor applications).
It was anticipated that the ESPRIT project would go on for ten years andwould be divided into two phases, ESPRIT I and ESPRIT II. (Later, a third
phase was added.) The initial budget for ESPRIT I was 1.5 billion ECUs.
(The euro replaced the ECU in January 1999 at one ECU = one euro.) Funds
would be provided equally between the EC and the project participants. The
budget for ESPRIT II was more than double that of ESPRIT I. According to
Luc Steels and Brice Lepape, by 1993, the program had “more than 6,000
scientists and engineers from about 1,500 organizations working on ESPRIT
projects across EC and European Free Trade Agreement countries.”
Rather than being directed in a top-down manner by program managers,
the projects funded by ESPRIT resulted from proposals submitted byindividual investigators and organizations. The proposals were reviewed by a
distributed team of experts. The program encouraged proposals thatemphasized “transnational cooperative networks,” industrial activities, andshort-term gains. ESPRIT collaborated with Alvey in supporting someresearch in Britain.17
ESPRIT supported several AI-related projects. Among these were ones
that developed various knowledge-based systems, logic programming
environments, natural language parsing systems, and knowledge acquisition
and machine-learning systems. As one example, I might mention the “Machine
Learning Toolbox” (MLT). It was a package of machine learning techniquesfrom which developers could select and assemble algorithms appropriate tospecific kinds of tasks. Partners in its development included teams from
France, the United Kingdom, Germany, Greece, and Portugal. The article by
Steels and Lepape presents a thorough summary of AI efforts supported by
ESPRIT.18 ESPRIT’s accomplishments helped to overcome some of industry’s
reluctance about AI.
While on the topic of national efforts in AI, I’ll mention the German
Research Center for Artificial Intelligence (DFKI, which stands for Deutsches
Forschungszentrum für Künstliche Intelligenz). It was established in 1988 andcontinues to conduct research in all areas of AI. More information about it can

356
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22.2

NOTES

be obtained from its Web page at
http://www.dfki.de/web/welcome?set language=en&cl=en.
In the United States, a DARPA program analogous to Alvey and ESPRIT
got underway in the early 1980s. It was partially a response to the Japanese
FGCS project, but it also owed much to the observation that the time wasripe for a major program that would take advantage of ongoing technicaldevelopments in communications technology and in computer hardware andsoftware. I’ll describe the DARPA program in the next chapter.

Notes
1. “Research Report on Fifth Generation Computer Systems Project,” ICOT Progress
Report, March 1983. [349]2. Ibid. [350]3. Shunichi Uchida, “FGCS Project: Knowledge Information Processing by Highly Parallel
Processing,” Institute for New Generation Computer Technology (ICOT), Tokyo, Japan,
undated. Available online at
http://www.icot.or.jp/ARCHIVE/PICS/OHP/Uchi1-FGohpE.pdf. [350]4. ICOT Staff (eds.) Proceedings of the International Conference on Fifth Generation
Computer Systems, June 1–5, 1992, Tokyo, Japan: IOS Press, 1992; Institute for New
Generation Computer Technology (ICOT, ed.), Proceedings of the International Conference
on Fifth Generation Computer Systems, November 28–December 2, 1988, 3 volumes, Tokyo,
Japan: OHMSHA, Ltd., and Berlin: Springer-Verlag, 1988; Institute for New Generation
Computer Technology (ICOT, ed.), Proceedings of the International Conference on Fifth
Generation Computer Systems, November 6–9, 1984, Tokyo, Japan: OHMSHA, Ltd., andAmsterdam: North-Holland, 1984. [351]5. For a Web page describing the various PIMs, see
http://www.icot.or.jp/ARCHIVE/Museum/MACHINE/pim-spec-E.html. [351]
6. See, for example, Ryuzo Hasegawa, Miyuki Koshimura, and Hiroshi Fujita, “MGTP: A
Parallel Theorem Prover Based on Lazy Model Generation,” Automated Deduction –CADE-11, Lecture Notes in Computer Science, Proceedings of the 11th International
Conference on Automated Deduction, Berlin/Heidelberg: Springer-Verlag, 1992. [351]7. See, for example, Shinichi Honiden, Akihiko Ohsuga, and Naoshi Uchihira, “MENDELSZONE: A Parallel Program Development System Based on Formal Specifications,”
Information and Software Technology, Vol. 38, No. 3, pp. 181–189, March 1996. [351]
8. See, for example, Katsumi Nitta et al., “HELIC-II: Legal Reasoning System on the
Parallel Inference Machine,” New Generation Computing, Vol. 11, Nos. 3–4, pp. 423–448,
July 1993. [351]
9. Kazuhiro Fuchi et al., “Launching the New Era,” Communications of the ACM, Vol. 36,
No. 3, pp. 49–100, March 1993. [352]10. Ibid, p. 99. [352]
11. See, for example, T. Motooka et al., “Challenge for Knowledge Information Processing
Systems (Preliminary Report on FGCS),” Proceedings of the International Conference on
FGCS, JIPDEC, pp. 1–85, 1981. [353]12. For a history of the first ten years or so of MCC, see David V. Gibson and Everett M.
Rogers, R & D Collaboration on Trial: The Microelectronics and Computer Technology

357
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22

NOTES

Corporation, Cambridge, MA: Harvard Business School Press, 1994. [354]
13. The committee’s report, from which these quotations are taken, is available online frompointers at
http://www.chilton-computing.org.uk/inf/literature/reports/alvey report/p001.htm. [355]
14. Brian W. Oakley, “Intelligent Knowledge-Based Systems – AI in the U.K.,” in Ray
Kurzweil, The Age of Intelligent Machines, Cambridge, MA: MIT Press, 1990. Available
online at http://www.kurzweilai.net/articles/art0308.html?printable=1. [355]
15. Brian Oakley and Kenneth Owen, Alvey: Britain’s Strategic Computing Initiative,
Cambridge, MA: MIT Press, 1990. [355]
16. Luc Steels and Brice Lepape, “Knowledge Engineering in ESPRIT,” IEEE Expert, Vol.
8, No. 4, pp. 4–10, August 1993. [356]
17. At this writing, there are still abundant Web pages about ESPRIT. They are available
from http://cordis.europa.eu/esprit/home.html. [356]18. Luc Steels and Brice Lepape, op. cit. [356]358
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23.1

Chapter 23

DARPA’s Strategic
Computing Program
23.1

The Strategic Computing Plan

By the early 1980s expert systems and other AI technologies, such as image
and speech understanding and natural language processing, were showing
great promise. Also, there was dramatic progress in communications
technology, computer networks and architectures, and computer storage andprocessing technologies. Robert Kahn (1938– ; Fig. 23.1), who had become
Director of DARPA’s Information Processing Techniques Office (IPTO) in
1979, began thinking that DARPA should sponsor a major research anddevelopment program that would integrate efforts in all of these areas tocreate much more powerful computer systems. At the same time, there wasconcern that the Japanese FGCS program could threaten U.S. leadership incomputer technology. With these factors as background, Kahn began planning
what would come to be called the “Strategic Computing” (SC) program.
Kahn had been a professor at MIT and an engineer at BBN before hejoined DARPA’s IPTO as a program manager in late 1972. There he initiatedand ran DARPA’s internetting program, linking the Arpanet along with the
Packet Radio and Packet Satellite Nets to form the first version of today’s
Internet. He and Vinton Cerf, then at Stanford, collaborated on thedevelopment of what was to become the basic architecture of the Internet andits “Transmission Control Protocol” (TCP). (TCP was later modularized andbecame TCP/IP, with IP standing for Internet Protocol.) Cerf joined DARPA
in 1976 and led the internetting program until 1982. For their work, Kahn andCerf shared the 2004 Turing Award of the Association for Computer
Machinery.1

359
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23

DARPA’s Strategic Computing Program

Figure 23.1: Robert E. Kahn. (Photograph courtesy of Robert E. Kahn.)
Kahn thought that AI, especially expert systems, could play a major rolein SC. Recall that in the mid-1970s DARPA support for AI research sufferedduring George Heilmeier’s tenure as the DARPA Director. A major casualty
was the speech understanding program. The SC program could revitalize AI
research, but more importantly in Kahn’s view, it would help transfer
promising AI techniques out of university laboratories and into actual
applications. Alex Roland, who wrote a well-researched book about thehistory of the SC program, put it this way:2
Robert Kahn and the architects of SC believed in 1983 [after theexpert systems boom] that AI was ripe for exploitation. It was
finally moving out of the laboratory and into the real world. . . AI
would become an essential component of SC; expert systems would
be the centerpiece. [They] would allow machines to “think.”
Kahn saw the SC program as a pyramid of related technologies to be
developed. At the base were enabling technologies such as facilities for rapid360
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23.1

The Strategic Computing Plan

design and implementation of the needed hardware. Above that sat hardware
and software technologies, with AI being prominent. These would all cometogether in specific military systems, such as robot vehicles and aids for battle
management. One of the (many) versions of this pyramid is shown in Fig.
23.2.

Figure 23.2: The SC program structure and goals. (Illustration used with permission of DARPA.)The SC program would support, coordinate, and manage research anddevelopment for all of the technologies in the pyramid. It was to become abillion-dollar program – the largest computer research and development
program ever undertaken by the U.S. government up to that time.
Kahn’s boss was Robert Cooper, who became the DARPA Director in
July 1981. Cooper was enthusiastic about Kahn’s ideas for the SC program,
although he differed from Kahn about research strategy and how to describe
the program. As Alex Roland wrote3
Kahn said build the technology base and nice things will happen.
Cooper said focus on a pilot’s associate and the technology basewill follow. One paradigm is technology push, the other technology
pull. One is bubble up, the other trickle down. . . . The tension
between them stressed SC through much of its history.
Because of its size, the SC program would have to be “sold” to those
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23

DARPA’s Strategic Computing Program

Congressional committees overseeing DARPA’s budget. Cooper knew thatemphasizing (indeed, promising) specific military applications was how to sell
Congress. He was right as far as convincing Congress was concerned, but inthe end technology pull didn’t work so well.
One factor in helping to convince Congress about the need for the SC
program was the Japanese FGCS program. According to Roland “Congress
was more exercised by Japan’s Fifth Generation program than either the
Reagan administration or the computer community [including Kahn andCooper].”4 The publication of a book5 about the Japanese project by Edward
Feigenbaum and Pamela McCorduck had the effect of strengthening theseconcerns. In the preface to their book, they asked “Will we rise to [this crucial
challenge]? If not, we may consign our nation to the role of the first great
postindustrial agrarian society.” They further warned that6. . . our national self-interest, not to mention our economic security,
does not allow us [to ignore the Japanese project]. Information
processing is an $88-billion-per-year industry in the United States,
and its loss would be disastrous. The default of this Americanindustry, which has led the world for decades, would be a mortal
economic wound. . . . The superior technology usually wins the war– whether that war is martial, entrepreneurial, or cultural.
In June 1983, Feigenbaum testified before the House Committee on
Science, Space, and Technology. According to Roland he told the committee“the era of reasoning machines is inevitable. . . It is the manifest destiny ofcomputing.”7
Kahn was persuaded to yield to Cooper’s vision about how to frame theplan, and it was finally written up in October 1983.8 Funds to support SC
were approved at a level of $50 million for work to begin in fiscal year 1984.
(One Congressional staff person even recommended that DARPA spend “asubstantially higher amount.”) During the decade from 1983 to 1993 DARPA
spent just over $1 billion on SC.9 The plan envisioned supporting two main
thrusts, namely, major projects that would build specific applications andbasic research to develop the “technology base” that would be needed for those
applications. I will describe aspects of each of these in the following sections.

23.2

Major Projects

At the beginning of the program, three major applications were chosen. To get
all three of the military services to “buy in” to the program, the plan called
for a “Pilot’s Associate” (for the Air Force), an aircraft carrier “Battle
Management System” (for the Navy), and an “Autonomous Land Vehicle” (forthe Army). However, to encourage joint service support of all of these, theplan cautioned that10
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23.2

Major Projects[i]t might, for example, prove preferable to pursue an autonomous
underwater vehicle rather than a land vehicle, and a battle
management system for land combat might prove more appropriate
than that for the Naval application.

As Mark Stefik wrote in his excellent review and assessment of the
program, “. . . DARPA is telling the services that the particulars of theseapplications can be shuffled at any time, so they had better buy the wholeplan.”11 Indeed, additional applications, such as an “Autonomous Air Vehicle”
for launching smart bombs behind enemy lines, were undertaken later.

23.2.1The Pilot’s Associate

The Pilot’s Associate (PA) program, begun in February 1986, had as its goal
the development of an interactive computer system that would aid a combat
aircraft commander. Interaction with the system would be through a graphics
user interface, voice recognition (capable of working under noisy and stressful
conditions), and speech synthesis. It would prepare and revise mission plans,
advise the pilot about targets, assess threats, help the pilot to take evasive
action against interceptor missiles (flying the plane automatically in case it
needed to maneuver so rapidly that the pilot might black out), and take overroutine tasks. In addition to pilot inputs, the system would obtain informationfrom navigational aids and several sensors. Advice and decisions would bebased on several collaborating expert systems, automatic planning systems,
and plan-execution systems.
Among the technical capabilities that the 1983 DARPA strategic plan
predicted could be achieved by 1989 were 10,000-rule, real-time expert
systems, animated displays with 108 polygons per second, 200-word,
speaker-independent speech recognition in high-noise environments, and aspeech output system capable of a 1,000-word vocabulary.12 Of course,
compact and aircraft-worthy hardware would be required also.
The program was administered by the Avionics Laboratory at
Wright-Patterson Air Force Base in Dayton, Ohio (later part of the Air Force
Research Laboratory).13 After a preliminary exploratory effort (in which fivecontractors participated), teams led by Lockheed Aeronautical Systems (laterpart of Lockheed Martin Corporation) and McDonnell Douglas (later part of
Boeing) were awarded contracts for work extending from 1986 through the
middle of 1992. As had become standard for DARPA-managed projects,
working demonstrations had to be given. According to a set of Web sites (lastupdated in 2004) describing the project, the Lockheed program was awarded
the American Institute of Aeronautics and Astronautics (AIAA) Digital
Electronics Award “. . . in recognition of outstanding achievement
in. . . advancing the state-of-the-art of artificial intelligence and decision
support systems into the complex, rapidly changing world of air combat.”14363
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23

DARPA’s Strategic Computing Program

The same site also claimed the following:Technically, by 1991, the PA was the most advanced, working,real-time intelligent system of its day and remains unsurpassed in
the world. We [at Lockheed] successfully integrated 6 expert
systems operating in real time in a realistic (some would say too
realistic) combat simulator. The knowledge implemented in eachcomponent of this system was realistic, combat experience that wasdemonstrably applicable to the operation of combat aircraft today.
In spite of the successful demonstrations, the Defense Department did not
follow up directly in installing the technology in fighter aircraft. As one of the
Web sites just cited puts it, “. . . it was left to the PA contractors to take thetechnology to the marketplace.”15 One of the legacies of the Pilot’s Associateprogram was a system that was developed for use in Army helicopters, theRotorcraft Pilot’s Associate. A possible application involving air traffic
management never materialized even though “results [of NASA studies] wereenough to show that PA technology could enable free flight throughout thecontinental and trans-oceanic air space, with enormous savings in operationalcosts for the airlines and the Air Traffic Control infrastructure, while alsooffering an extra level of safety.”16

23.2.2

Battle Management Systems

In 1984 DARPA began funding the Fleet Command Center Battle
Management Program (FCCBMP, pronounced “fik bump”). A company called
Analytics was the engineering contractor for FCCBMP. It provided program
management support, testing, and configuration management. In a 1990
article describing and assessing the program, Rin Saunders, an engineer at
Analytics, wrote that its goal was to produce a system that would “. . . assist
the commander-in-chief of the U.S. Pacific Fleet (CINCPACFLT) in planningand monitoring the operation of nearly 300 ships in the Pacific and Indian
ocean regions.”17 Saunders claimed that it was the most successful of theStrategic Computing programs in bringing expert systems into operational use
and had “the greatest visibility and participation within the user community.”
Expert systems were planned to play a major role in FCCBMP. The
DARPA strategic plan envisioned ones that could process 10,000 rules per
second in highly complex contexts operating at five times real time.18
FCCBMP consisted of two major expert systems communicating over a local
area network. One of these, the “Force Requirements Expert System(FRESH),” was designed to keep track of the current positions and thereadiness status of ships in the fleet and to issue alerts when there weresignificant changes. FRESH, developed by Texas Instruments, was supposed to
be able to make suggestions about what should be done, such as proceeding
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23.2

Major Projects

with the current plan anyway, expediting repairs, or substituting other ships
for unready ones.
Saunders claimed that19
FRESH currently provides daily alert summaries to twelve
CINCPACFLT staff codes. FRESH was shown to replicate expert
judgement in a trial in which FRESH and CINCPACFLT staff were

given the same nomination task to solve. CINCPACFLT staff
estimate that FRESH can accomplish in one minute monitoringtasks which previous[ly] took two hours, yielding a 120-fold timesavings. For the planning task of nominating a replacement for adisabled ship, the time savings is 400 to 1.
Another expert system, the “Capabilities Assessment Expert System”(CASES), used information about U.S. and enemy forces to provide estimatesof how each would fare in hypothetical engagements. It was developed by
BBN. According to Saunders,20
CASES has been used to evaluate carrier battle force operatingareas; assess attack submarine employment strategies; estimate theeffect of pre-D-day surveillance and early assignment of SSNs toenemy submarine attrition; and provide insights on the costs andbenefits of different strike strategies relative to changes in
estimated enemy capability and weather.
Both expert systems made extensive use of natural language
understanding and generation abilities. They were hosted on Symbolics Lisp
machines and written using commercial expert-system “shells.” The battle
simulations in CASES were run on an Encore parallel processor. The final
prototypes for FRESH and CASES were delivered to CINCPACFLT in 1990.
But when the prototype phase was complete and DARPA funding ended, theNavy decided not to continue these systems.
Saunders, now a Technical Director at Computer Sciences Corporation,
has provided me with some recollections about FCCBMP. These contrast a bitwith what he wrote in 1990. In e-mail notes, he wrote me that21[t]he goals of FCCBMP were an overreach for the state of the art
in the 1980s.
...
[The] Navy’s decision to mothball FRESH was because there was nocompelling reason to keep it. It duplicated the expert judgement of
Fleet planners, in a matter of hours rather than days. But theplanners were not looking to retire, and in Naval warfare, days aregood enough.
365
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DARPA’s Strategic Computing Program
Concerning CASES, the Fleet was eager to have the simulation
tools. But the goal of orchestrating the simulations in an intelligent
way to evaluate what-if scenarios never got off the ground.
...
I believe that there was a growing recognition within
DARPA/ISTO that the FRESH and CASES technologies’ research
agendas were not best met within the vicissitudes of an operational
environment. . . . There was also pressure from Congress for
DARPA to divert the funding to anti-submarine warfare, which wasa hot topic at the time. And relations between DARPA and Navy
were always strained near the breaking point. Navy constantly
fought DARPA for control of the program, both for its own sakeand to redefine the program to provide greater near-term payback.
In the end, everyone decided it was time to pack up and go home.

23.2.3Autonomous Vehicles

The third major applications project funded under the umbrella of DARPA’s
SC program was the “Autonomous Land Vehicle” (ALV) project begun inAugust 1984. Martin Marietta (later to merge with Lockheed to become
Lockheed Martin) was selected as the “project integrator” and funded at $10.6
million for a period of forty-two months.22 SRI, Carnegie Mellon University,
the University of Maryland, Hughes Research Laboratories, Advanced Decision
Systems, and the Environmental Research Institute of Michigan (ERIM)
provided components and research help. The U.S. Army Engineering
Topographic Laboratory helped to coordinate the work. The goals of the
project were in line with the Army’s long-range “strategic vision” of using
autonomous vehicles in logistics and supply operations, in search and rescue,and even in combat.
A great deal of information about the ALV project can be obtained froman article by Douglas W. Gage. Rather than paraphrase his summary, I’ll
quote it directly:23
The ALV was built on a Standard Manufacturing eightwheelhydrostatically-driven all-terrain vehicle capable of speeds of up to
45 mph on the highway and up to 18 mph on rough terrain. The
ALV could carry six full racks of electronic equipment in dust-free
air conditioned comfort, providing power from its 12-kW diesel
APU. The initial sensor suite consisted of a color video camera anda laser scanner from the Environmental Research Institute ofMichigan (ERIM) that returned a 64 by 256 pixel range image at
1–2 second intervals. Video and range data processing modules
produced road-edge information that was used to generate a model
366
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23.2

Major Projectsof the scene ahead. Higher level reasoning was performed bygoalseeker and navigator modules, which then passed the desired
path to the pilot module that actually steered the vehicle.

A photograph of the ALV and its system configuration is shown in Fig.
23.3.

Figure 23.3: Martin Marietta’s ALV (top) and its system configuration (bottom). (ALV photograph courtesy of DARPA; diagram from R. Terry Dunlay,“Obstacle Avoidance Perception Processing for the Autonomous Land Vehicle,”
Proceedings of the IEEE Robotics and Automation Conference, pp. 912–917, Los
Alamitos, CA: CS Press, 1988.)
The ALV was to be the forerunner of military vehicles that could move,
unguided, on roads and over rough terrain using computer vision programs to367
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23

DARPA’s Strategic Computing Program

inform them about their environments and planning programs and expert
systems to control their routes. They would have to avoid such hazards asother vehicles, rocks, trees, ditches, water obstacles, and steep or muddy
terrain. They would also have to be able to identify landmarks and othersignificant objects in their immediate surroundings. They were to be entirely
self-contained with all computing to be done on board.
The DARPA Strategic Computing Plan laid out some specific milestones
for the ALV. 24 In 1985 there was to be a “road-following demonstration” inwhich the ALV was to navigate a preset route of 20 km at speeds up to 10
km/hour. By 1986 there was to be an “obstacle avoidance demonstration”
using “fixed, polyhedral objects spaced no less than 100 m” apart and of a size
much smaller than the road width. In 1990 and 1991 there was to be a “mixed
road and open terrain demonstration” with speeds up to 90 km/hour on roads
with other vehicles.
According to a report by the National Research Council assessing progressin unmanned vehicles, “the ALV made a 1 km traverse in 1985 at an average
speed of 3 km/h. . . This increased to 10 km/h over a 4 km traverse in 1986. In
1987, the ALV reached a top speed of 20 km/h. . . and used the laser scanner toavoid obstacles placed on the road.”25 The same report continues with
In August 1987, the ALV performed the first autonomous
cross-country traverse based on sensor data. During this andsubsequent trials extending for about a year, the ALV navigatedaround various kinds of isolated positive obstacles over traverses ofseveral kilometers. The terrain had steep slopes (some over 15
degrees), ravines and gulleys, large scrub oaks, brushes, and rockoutcrops. Some manmade obstacles were emplaced forexperiments. The smallest obstacles that could be reliably detected
were on the order of 2 feet to 3 feet in height. On occasion, thevehicle would approach and detect team members and maneuver toavoid them. The vehicle reached speeds of 3.5 to 5 km/hr andcompleted about 75 percent of the traverses.
Vision for the ALV represented the most difficult challenge. Recognizing
rocks, trees, road, and ditches would stretch the state of the art of bothcomputer vision algorithms and computer processing speeds in the 1980s.
According to Roland, “DARPA estimated that the vision system for the ALV
would require 10–100 billion instructions per second, compared to the rate in1983 of only 30–40 million.”26 It has been estimated that perception for
unmanned vehicles around the time of the ALV accounts for about 85% of thetotal computational load.27
Commenting on the performance of the ALV vision system, Roland
wrote28
The vision system proved highly sensitive to environmental
368
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23.3

AI Technology Baseconditions – the quality of light, the location of the sun, shadows,
and so on. The system worked differently from month to month,
day to day, and even test to test. Sometimes it could accurately
locate the edge of the road, sometimes not. The system reliably
distinguished the pavement of the road from the dirt on theshoulders, but it was fooled by dirt that was tracked onto theroadway by heavy vehicles maneuvering around the ALV. In thefall, the sun, now lower in the sky, reflected brilliantly off the
myriads of polished pebbles in the tarmac itself, producing
glittering reflections that confused the vehicle. Shadows from trees
presented problems, as did asphalt patches from the frequent roadrepairs made necessary by the harsh Colorado weather and theconstant pounding of the eight-ton vehicle.

DARPA cancelled the ALV program in April 1988, but interest inautonomous vehicles continued in research labs, in industry, and in othergovernment agencies. Among the programs supported by the Defense
Department during the 1990s were DEMO-I for tele-operated vehicles and
DEMO-II for autonomous vehicles. Again, Martin Marietta’s Aerospace
Division was chosen as the overall integration contractor. Among theco-contractors providing subsystems were CMU, Hughes ResearchLaboratories, Advanced Decision Systems, SRI, Teleos, JPL, and the
Universities of Massachusetts and Michigan. The vehicle chosen for thedemonstrations was a HMMWV (an acronym for High-Mobility Multipurpose
Wheeled Vehicle, pronounced humvee). A series of demonstrations was givenin the mid-1990s.29 Carnegie Mellon University developed a number ofimportant unmanned vehicles including the “Terragator” and a series of“Navlabs.” (I’ll have more to say about the Navlabs later.) Although the ALV
program might not have achieved all of its goals, it can be said to havelaunched the era of autonomous vehicles. They are becoming more versatileand “autonomous” with each passing year.

23.3

AI Technology BaseEven though the main focus of the SC program was the suite of applications
just mentioned, successful pursuit of these applications required advances inthe technology on which they depended. According to Robert Cooper’s viewof SC, these applications would “pull” the technology of vision, robotics,
expert systems, speech recognition, and natural language processing. So, in
addition to the main contracts for applications, several were let for technology
development. Of course, the technology developers were supposed to be closely
coupled to the applications that were pulling them.

369
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DARPA’s Strategic Computing Program

23.3.1

Computer Vision

Ron Ohlander and (later) Robert Simpson Jr. were program managers at
DARPA IPTO in charge of AI research during the SC days. Continuing thework begun earlier in “image understanding,” DARPA focused on four mainareas of computer vision, namely, visual modeling and recognition, dynamic
scene and motion analysis, vision-based obstacle avoidance and path planning,
and implementation of vision algorithms using parallel computing
architectures.30
Chuck Thorpe, Takeo Kanade, and others at CMU concentrated
especially on vision systems needed by ALVs.31 Other important computer
vision research in support of Strategic Computing was done at the Universityof Maryland under Azriel Rosenfeld, at the University of Massachusetts under
Alan Hanson and Edward Riseman, at SRI under Martin Fischler, and atindustrial laboratories.
Besides applications in robotics, computer vision technology finds
applications in cartography and photo interpretation. One of the earlyphoto-reconnaissance systems was SCORPIUS (an acronym for Strategic
Computing Object-Directed Reconnaissance Parallel-Processing
Image-Understanding System) funded by the CIA and developed at the
Hughes Research Labs. It was supposed to screen aerial and satellite
photographs to detect ships, buildings, airplanes, and other objects of interest.
According to SRI’s Martin Fischler, the project got bogged down ininfrastructure problems associated with having to use a new parallel-processing
computer (the “Butterfly Multiprocessor”) being developed at BBN.32
Later photo-reconnaissance projects, following on after the SC program,
were more successful. One such project was RADIUS (an acronym for
Research And Development for Image Understanding Systems) funded jointly
by DARPA and the CIA. Image processing systems developed at SRI under
the RADIUS project could overlay terrain texture on three-dimensionalmodels of parts of the earth (enabling, for example, a simulated “flythrough”
of Yosemite Valley). They could also be used to help locate buildings andother objects in photographs, thus aiding a human photo interpreter.33

23.3.2

Speech Recognition and Natural Language
Processing

The Pilot’s Associate project and the Battle Management projects depended
on the ability of computer systems to understand verbal requests orcommands. Speech recognition was needed by the Pilot’s Associate, and bothspeech recognition and text understanding were needed by the Battle
Management projects. Accordingly, several basic research projects were funded
by DARPA to advance those technologies. Recall that DARPA discontinued

370
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23.3

AI Technology Base

its speech understanding research program in 1976, but continuing progress inthe field, notably at CMU, MIT, and IBM, justified its resumption.
Texas Instruments integrated work from eight speech recognition research
projects for the Pilot’s Associate project. CMU integrated work from nineprojects for the Battle Management projects. According to Roland, by the endof the SC program, speech recognition systems “could recognize 10,000 wordsof natural language spoken by anyone in an environment of moderate
background noise and low stress on the speaker.”34
Later, at CMU, Kai-Fu Lee and others developed the speaker-independent
SPHINX speech recognition system under DARPA support.35 SPHINX used

HMMs (hidden Markov models) and statistical information about thelikelihoods of word strings to aid recognition.
Speech recognition work continues at several industrial and university
research laboratories. At CMU, to name just one example, the SPHINX project
has assembled “a set of reasonably mature [open source], world-class speechcomponents that provide a basic level of technology to anyone interested increating speech-using applications.” However, the project Web page at
SourceForge36 cautions that “. . . SPHINX is not a final product. Those with acertain level of expertise can achieve great results with the versions of SPHINX
available here, but a naive user will certainly need further help. In otherwords, the software available here is not meant for users with no experience inspeech, but for expert users.” Advances in computer speed and memory have
led to several high-quality, real-time, and commercially available speech
recognition systems of moderate cost.
The SC program also sponsored basic research work on text
understanding as part of what DARPA called “new generation systems.”
Seven contractors received DARPA awards in 1984.37 BBN Laboratories,
USC/Information Sciences Institute, the University of Pennsylvania, and the
University of Massachusetts worked on natural language interfaces that could
respond to typed queries. New York University, the Systems Development
Corporation (later to become part of Unisys), and SRI International worked
on understanding free-form text from military messages. BBN and USC–ISI
coordinated the work on interfaces based initially on BBN’s IRUS system,
which later grew into JANUS.38 New York University and SDC coordinated thework on text understanding and developed the PROTEUS and PUNDIT
systems.39
According to Roland, the results of this work “were most promising, again
exceeding the milestones laid out in the original SC plan. . . . the metrics of theoriginal plan [were] exceeded in error rate and [in] the size of vocabularies.”40
Roland goes on to elaborate:41. . . BBN’s IRUS system, with a vocabulary of 4,500 words and 1,200
domain concepts, received a favorable response in 1986 when it was
installed in a Battle Management Program test bed. By 1987 BBN
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23

DARPA’s Strategic Computing Program
IRUS was becoming JANUS, a system designed to understand vague
and confusing queries. JANUS was successfully demonstrated in the

Air-Land Battle Management Program in the fall of 1987.
Meanwhile in 1986 the first version of the PROTEUS system successfully
processed naval casualty reports (CASREPS) about equipment failure.

23.3.3

Expert Systems

Because expert systems appeared to be so promising in the late 1970s andearly 1980s, they were slated for a prominent role in the Strategic Computing
program. They were to be the reasoning agents that would give “intelligence”
to the SC applications. Ronald Ohlander, the DARPA program manager for
intelligent systems, sought contractors for expert systems research in 1984.
Out of the fifty proposals that were submitted to DARPA, Ohlander
recommended that six of them be funded. A seventh was eventually added tothis list. As Roland reports, Stanford would work on new expert systemarchitectures, BBN would work on the problem of getting the needed
knowledge into expert systems, Ohio State University would work on methods
for expert systems to explain their conclusions, and the University of
Massachusetts and General Electric would work on techniques for reasoning
with uncertain information.42
The other contractors were start-up companies – both founded by Edward
Feigenbaum and other Stanford researchers. One, IntelliCorp (originallyfounded as IntelliGenetics), had already built an EMYCIN-inspired expert
system shell called KEE (an acronym for Knowledge Engineering
Environment).43 IntelliCorp proposed to extend KEE by upgrading several
features, including the ability to maintain the consistency of the knowledge
base as new knowledge is added or deleted, the ability to make reasonable
inferences even though they might not be warranted by specific information inits knowledge base, and the ability to make plans and schedules. It would also
have facilities for knowledge acquisition, user interface construction, andhierarchical descriptions of objects. IntelliCorp was awarded a DARPA
contract in 1984 for $1,286,781.44 KEE eventually evolved into a system called
OPUS.45
The other start-up company, Teknowledge, Inc., proposed to build anexpert-system toolkit called ABE to be used for building expert systems. ABE
was to be brand new, and thus it was somewhat of a gamble for DARPA, but
the company had plenty of AI experts either on its payroll or as consultants.46
Teknowledge was awarded a DARPA contract for $1,813,260 to make good on
its promises.47
Both KEE and ABE were used by the SC applications contractors, butneither provided the intelligence originally expected from expert systems. But
these expectations were probably unrealistic. In their 1994 assessment of372
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23.4

Assessment

knowledge-based systems (KBSs), Frederick Hayes-Roth and Neil Jacobstein,
two pioneers in expert system applications, wrote “. . . although the current
generation of expert and KBS technologies had no hope of producing a robust
and general human-like intelligence, many people were disappointed that it didnot.”48 Even so, Hayes-Roth and Jacobstein concluded the following:49
KBS have been remarkably effective over the past decade ofindustrial experience – often delivering order-of-magnitude
increases in speed, quality, or cost performance. They havepenetrated every major institution from Fortune 500 companies tosmall entrepreneurial firms, military services, government agencies,
health care, and educational institutions. KBS applications may
now be found in virtually every field of human endeavor frommusic to medicine.
DART, an acronym for Dynamic Analysis and Replanning Tool, was a
KBS whose roots can be traced to AI research during the SC program.
Developed at BBN, it helped plan the movement of equipment and personnel
from Europe to Saudi Arabia during the 1990 Persian Gulf War. In fact,
Victor Reis, the Director of DARPA at the time, has been quoted as claiming“The DART scheduling application paid back all of DARPAs 30 years ofinvestment in AI in a matter of a few months.”50

23.4

Assessment

What can be said about the SC program overall? It has been criticized bothby those who thought it might achieve its military goals and also by those who
faulted it for not having done so. Among the former, the Computer
Professionals for Social Responsibility (CPSR) had several concerns, stated in
one of their newsletters:51
1. The SCI [Strategic Computing Initiative] promises specific newweapons systems; autonomous vehicles, such as robot tanks; acombat pilot’s “associate;” and an aircraft carrier group “battle
management system.” Our concern is that proposals for computer
research will be assessed by their relevance to these specificapplications, rather than by their general scientific merit.
2. The SCI promotes the use of machine “intelligence,” to control
the operation of complex military systems under unpredictable
circumstances. Our concern is that, particularly when the stakes
are high, situations of extreme uncertainty are precisely the wrong
environment for the application of artificial intelligence.
3. The SCI promotes the military application of computer
technology as a solution to perceived problems in defense. Our
373
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DARPA’s Strategic Computing Program
concern is that, rather than increasing our security, past attempts
to achieve superiority in new weapons technology have fueled anarms race that has no foreseeable end.
This last concern is the gravest. In the final analysis, we believe
neither that the path to national security lies in military
superiority, nor that superiority can be achieved through the use ofcomputers.

Most commentators doubt that the SC program achieved its goal ofpulling new AI technology into the SC applications. Developing the kinds ofcapabilities envisioned by the SC applications required AI inventions, and theatmosphere needed for invention is not conducive to tightly programmed
milestone demonstrations. Instead, as Roland comments, the “applications
extemporized ad-hoc, off-the-shelf solutions to meet demonstration
deadlines.”52 Furthermore, the showcase systems, namely, the ALVs, thePilot’s Associate, and those for battle management, were not immediately“bought” by their hoped-for military customers. Even so, these systems were
the forerunners of similar ones having much higher levels of performance.
Generous SC support for the AI “technology base” nourished AI researchin general even though the research did not produce results that were
integrated into the SC applications. After a short diminution toward the endof the SC program, AI research has steadily prospered both at universities andin industry and continues to produce important new capabilities.
In addition to the problem of combining invention with application, theSC program suffered from institutional problems and budget reductions.
There was often friction between the DARPA people and the militarycustomers. Furthermore, many of the people who had planned the SC
program, including Kahn and Cooper, had left DARPA by late 1985. In thespring of 1986, DARPA combined its Information Processing Techniques Officewith its Engineering Applications Office and renamed it the Information
Systems Technology Office (ISTO). A succession of ISTO directors both
endured and helped cause budget fluctuations. Jacob Schwartz, who was
skeptical about some AI approaches, became the ISTO director in September
1987. He promptly canceled some AI programs and failed to renew others.
Then, in 1991 ISTO split into SISTO (Software and Intelligent Systems
Technology Office) and CSTO (Computer Systems Technology Office),
effectively ending attempts to couple basic research with applications.
The SC program gradually disappeared from view. According to Roland
it was never mentioned in public documents or reports after 1989. It vanishedfrom the DARPA budget in 1993 and was ultimately replaced by otherprograms including one for High Performance Computing (HPC).53 Even
though the program itself disappeared, its accomplishments, along with thoseof the other “new-generation” projects, were many. Progress made during the
1980s established artificial intelligence as a technology that was capable of374
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23.4

NOTES

taking on a wide variety of real-world applications. As I’ll describe in a
subsequent chapter, much new AI technology was invented during the 1980s
and 1990s, and some of it at least was stimulated by the new-generation
projects. However, before continuing with my roughly chronological history,
I’ll make a temporary diversion to discuss controversies that were simmeringon the sidelines and within the field itself.

Notes
1. To learn more about the evolution of the Internet, see Robert E. Kahn and Vinton G.
Cerf, “What Is the Internet (And What Makes It Work)” available online at
http://www.cnri.reston.va.us/what is internet.html. [359]
2. Alex Roland (with Philip Shiman), Strategic Computing: DARPA and the Quest for
Machine Intelligence, 1983–1993, pp. 191–192, Cambridge, MA: MIT Press, 2002. [360]3. Ibid, p. 71. [361]4. Ibid, p. 91. [362]5. Edward Feigenbaum and Pamela McCorduck, The Fifth Generation: Japan’s Computer
Challenge to the World, Boston, MA: Addison-Wesley Longman Publishing Co., Inc., 1983.
An article with the same title by the same authors appeared in Creative Computing, Vol. 10,
No. 8, p. 103, August 1984, and is available online at
http://www.atarimagazines.com/creative/v10n8/103 The fifth generation Jap.php. [362]6. Edward Feigenbaum and Pamela McCorduck, op. cit., pp. 19–20. [362]7. In Alex Roland, op. cit., pp. 91–92. Roland cites U.S. Congress, House, Committee on
Science, Space, and Technology, Japanese Technological Advances and Possible U.S.
Responses Using Research Joint Ventures. Hearings, 98th Congress, 1st session, June,
29–30, 1983, pp. 116–143, at p. 119. [362]
8. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for
Its Development and Applications to Critical Problems in Defense,” Defense Advanced
Research Projects Agency, Arlington, Virginia, October 28, 1983. [362]9. Alex Roland, op. cit., p. 319. [362]
10. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for its
Development and Applications to Critical Problems in Defense,” p. 20, Defense Advanced
Research Projects Agency, Arlington, Virginia, October 28, 1983.. [362]
11. Mark Stefik, “Strategic Computing at DARPA: An Overview and Assessment,”
Communications of the ACM, Vol. 28, No. 7, pp. 690–704, July 1985. [363]
12. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for
Its Development and Applications to Critical Problems in Defense,” Chart I.2 of appendix I,
Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. [363]13. For a description and review of the program by people from Wright-Patterson Air Force
Base see Sheila B. Banks and Carl S. Lizza, “Pilot’s Associate: A Cooperative,
Knowledge-Based System Application,”IEEE Expert, Vol. 6, No. 3, pp. 18–29, 1991. [363]
14. http://www.dms489.com/PA/PA index.html. [363]
15. http://www.dms489.com/PA/PA index.html. [364]16. http://www.dms489.com/PA/PA index.html. [364]
17. Rin Saunders, “The Fleet Command Center Battle Management Project: Lessons

375
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23

NOTES

Learned,” Proceedings of the IEEE Conference on Managing Expert System Programs andProjects, pp. 51–60, September 1990. [364]
18. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for its
Development and Applications to Critical Problems in Defense,” Chart I.3 of Appendix I,
Defense Advanced Research Projects Agency, Arlington, Virginia, October 28, 1983. [364]
19. Rin Saunders, op. cit., p. 53. [365]
20. Ibid, p. 53. [365]
21. E-mail correspondence of December 12, 2007. [365]
22. Alex Roland, op. cit., p. 222. [366]
23. Douglas W. Gage, “UGV HISTORY 101: A Brief History of Unmanned Ground Vehicle(UGV) Development Efforts,” Unmanned Systems Magazine, Special Issue on Unmanned
Ground Vehicles, Vol. 13, No. 3, Summer 1995. [366]
24. “Strategic Computing: New-Generation Computing Technology: A Strategic Plan for
Its Development and Applications to Critical Problems in Defense,” Defense Advanced
Research Projects Agency, Arlington, Virginia, October 28, 1983. The milestones describedin this paragraph, along with many others, are taken from Chart I.1 in Appendix I. [368]25. National Research Council Staff, Technology Development for Army Unmanned Ground
Vehicles, pp. 152–153, Washington, DC: National Academies Press, 2003. [368]
26. Alex Roland, op. cit.. [368]27. National Research Council Staff, Technology Development for Army Unmanned Ground
Vehicles, p. 148, Washington, DC: National Academies Press, 2003. [368]28. Alex Roland op. cit.. [368]29. For summaries of DEMO-I and DEMO-II technology and demonstrations, see Douglas
W. Gage, “UGV HISTORY 101: A Brief History of Unmanned Ground Vehicle (UGV)
Development Efforts,” Unmanned Systems Magazine, Special Issue on Unmanned Ground
Vehicles, Vol. 13, No. 3, Summer 1995, and National Research Council Staff, Technology
Development for Army Unmanned Ground Vehicles, p. 148, Washington, DC: National
Academies Press, 2003. The latter book recommends Oscar Firschein and Thomas Strat(eds.), Reconnaissance, Surveillance, and Target Acquisition for the Unmanned Ground
Vehicle: Providing Surveillance “Eyes” for an Autonomous Vehicle, San Francisco, CA:
Morgan Kaufmann Publishers, 1997. [369]30. See, for example, Robert L. Simpson Jr., “Computer Vision: An Overview,” Guest
Editor’s Introduction, IEEE Expert, pp. 11–15, August 1991. [370]
31. See, for example, Takeo Kanade, Chuck Thorpe, and William Whittaker, “Autonomous
Land Vehicle Project at CMU,” Proceedings of the 1986 ACM Computer Conference, pp.
71–80, February 1986, and Yoshimasa Goto and Anthony Stentz, “Mobile Robot Navigation:
The CMU System,” IEEE Expert, pp. 44–54. 1987. (The latter paper is available online at
http://www.ri.cmu.edu/pub files/pub3/goto y 1987 1/goto y 1987 1.pdf.) [370]32. Personal communication, November 15, 2007. [370]
33. See Thomas M. Strat and Oscar Firschein, RADIUS: Image Understanding for Imagery
Intelligence, San Francisco: Morgan Kaufmann Publishers, 1997. See also the RADIUS Web
site at http://www.ai.sri.com/∼radius/. [370]
34. Alex Roland, op. cit. For this information, Roland cites Victor Zue, a speech researcher
and professor at MIT. [371]35. Kai-Fu Lee, Hsiao-Wuen Hon, and Raj Reddy, “An Overview of the SPHINX Speech
Recognition System,” IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol.
38, No. 1, January 1990. Available online at
http://www.ri.cmu.edu/pub files/pub2/lee k f 1990 1/lee k f 1990 1.pdf. [371]376
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23.4

NOTES

36. http://cmusphinx.sourceforge.net/html/cmusphinx.php. [371]37. Proceedings of the Workshop on Strategic Computing Natural Language, Foreword byRobert Simpson, Morristown, NJ: Association for Computational Linguistics, 1986. [371]38. For a brief history of the IRUS/JANUS work (as well as related work at BBN), see
Ralph Weischedel, “Natural-Language Understanding at BBN,” IEEE Annals of the Historyof Computing, pp. 46–55, January–March 2006. A BBN report about the project is available
online a http://www.aclweb.org/anthology-new/H/H86/H86-1001.pdf. [371]
39. See the online document by Ralph Grishman and Lynette Hirschman, “PROTEUS and

PUNDIT: Research in Text Understanding,” available at
http://www.aclweb.org/anthology-new/H/H86/H86-1002.pdf. [371]
40. Alex Roland, op. cit., p. 212. [371]41. Ibid, pp. 269–270. [371]42. Ibid, p. 195. [372]43. T. P. Kehler and G. D. Clemenson, “KEE – The Knowledge Engineering Environmentfor Industry,” Systems and Software, Vol. 3, No. 1, pp. 212–224, January 1984. [372]
44. Alex Roland, op. cit. p. 198. [372]45. Richard Fikes et al., “OPUS: A New Generation Knowledge Engineering Environment,”
Phase 1 Final Report, lntelliCorp, Mountain View, CA, 1987. [372]
46. Lee D. Erman, Jay S. Lark, and Frederick Hayes-Roth, “ABE: An Environment forEngineering Intelligent Systems,” IEEE Transactions on Software Engineering, Vol. 14, No.
12, pp. 1758–1770, December 1988. [372]47. Alex Roland, op. cit., p. 201. [372]48. Frederick Hayes-Roth and Neil Jacobstein, “The State of Knowledge-Based Systems,”
Communications of the ACM, Vol. 37, No. 3, p. 36, March 1994. [373]49. Ibid, p. 36. [373]50. Sara Reese Hedberg, “DART: Revolutionizing Logistics Planning,” IEEE Intelligent
Systems, p. 81, May/June 2002. [373]51. The CPSR Newsletter, Vol. 2, No. 2, Spring 1984. Also see S. M. Ornstein, B. C.
Smith, and L. A. Suchman, “Strategic Computing: An Assessment,” Bulletin of the Atomic
Scientists, Vol. 40, No. 10, pp. 11–15, December 1984. [373]
52. Alex Roland, op. cit., p. 243. [374]53. Ibid, p. 285. [374]377
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NOTES

378
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23.4

Part VI

Entr’acte379
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23

380
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24.1

Chapter 24

Speed Bumps
There have been naysayers from the earliest days of artificial intelligence. Alan
Turing anticipated (and dealt with) some of their objections in his 1950 paper.
In this chapter, I’ll recount some of the controversies surrounding AI –including some not foreseen by Turing. I’ll also describe some formidable
technical difficulties confronting the field. By the mid-1980s or so, these
difficulties had caused some to be rather dismissive about progress up to thattime and pessimistic about the possibility of further progress. For example, in
wondering about the need for a special issue of the journal Dædalus devoted to
AI in 1988, the philosopher Hilary Putnam wrote1 “What’s all the fuss about
now? Why a whole issue of Dædalus? Why don’t we wait until AI achieves
something and then have an issue?”
The attacks and expressions of disappointment from outside the field
helped precipitate what some have called an “AI winter.”

24.1

Opinions from Various Onlookers

24.1.1The Mind Is Not a Machine

In the introduction to his edited volume of essays titled Minds and Machines,2
the philosopher Alan Ross Anderson mentions the following two extreme
opinions regarding whether or not the mind is a machine:(1) We might say that human beings are merely very elaborate bitsof clockwork, and that our having “minds” is simply a consequenceof the fact that the clockwork is very elaborate, or(2) we might say that any machine is merely a product of humaningenuity (in principle nothing more than a shovel), and that
381
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Speed Bumps
though we have minds, we cannot impart that peculiar feature ofours to anything except our offspring: no machine can acquire thisuniquely human characteristic.

Most AI researchers probably agree with the first of these two statements.
I certainly do (although I would not have used the word “merely”). Marvin
Minsky put this position most powerfully when he is alleged to have said “Themind is a meat machine.” However, some philosophers hold to the second
view. The most prominent of these is probably the British philosopher, John
R. Lucas (1929– ).
In an essay titled “Minds, Machines, and Gödel,”3 Lucas based hisargument on Kurt Gödel’s proof that there are some true statements thatcannot be proved by any mechanical system that is both consistent and able
(at least) to do arithmetic. Lucas presumes that humans (or at least somehumans) can “see” these statements to be true even though machines cannot
prove them. Several people have pointed to flaws in Lucas’s argument,4 andLucas claims to have responded to at least some of them in his book The
Freedom of the Will.5 In a 1990 paper read to the Turing Conference at
Brighton, Lucas seems to have weakened his argument a bit by saying6
The argument I put forward is a two-level one. I do not offer asimple knock-down proof that minds are inherently better thanmachines, but a schema for constructing a disproof of any plausible
mechanist thesis that might be proposed. . . . Essentially, therefore,
the two parts of my argument are first a hard negative argument,
addressed to a mechanist putting forward a particular claim, andproving to him, by means he must acknowledge to be valid, that his
claim is untenable, and secondly a hand-waving positive argument,
addressed to intelligent men, bystanders as well as mechanists
espousing particular versions of mechanism, to the effect that some
sort of argument on these lines can always be found to deal withany further version of mechanism that may be thought up.
I happen to believe that humans are subject to whatever Gödelian
limitations might apply to machines, but that’s because I believe humans aremachines. Lucas continues to argue his point because (I think) he would liketo believe they are not. In any case, the argument is somewhat sterile because
it does not really limit what AI can potentially do in practice. Even Lucas
admitted in his original paper that we might be capable of “constructing very,
very complicated systems of, say, valves and relays,” that would be “capable ofdoing things which we recognized as intelligent, and not just mistakes or
random shots, but which we had not programmed into it. But then it wouldcease to be a machine.” (Here, he seems to be trying to win his argument byredefining “machine.”)382
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Opinions from Various Onlookers

The engineer Mortimer Taube (1910–1965) also believed that humans
were not machines. In his 1961 book Computers and Common Sense: The
Myth of Thinking Machines,7 he railed against efforts to get computers toreason, to translate human languages, and to learn. Many of the things hesaid that computers would not be able to do have, by now, been done.

24.1.2The Mind Is Not a Computer

A. New Physics Is Needed
Several people have put forward the argument that, although humans may
well be machines, intelligence cannot be exhibited by computers – at least notby present-day computers made of transistors and other ordinary
electromagnetic components and working the way they do.
The British physicist Sir Roger Penrose (1931– ; Fig. 24.1) is persuaded
by Lucas’s Gödelian arguments about the limitations of computers. (Penrose
is famous for work in quantum physics, relativity theory, the structure of theuniverse, and “Penrose tilings.”) He, like Lucas, believes that computers could
never be conscious, nor could they have the full range of human intelligence.
But Penrose imagines that these limitations apply only to machines based on
the presently known laws of physics. To escape from Gödel’s limitations (asPenrose believes brains do), he claims a new kind of physics must be invoked –one that involves something he calls “correct quantum gravity.” Unfortunately,
correct quantum gravity, whatever it is, remains to be discovered (or, I would
rather say, invented.)
Penrose puts forward these ideas (along with some very engaging material
about physics) in two books: The Emperor’s New Mind8 and Shadows of the
Mind: A Search for the Missing Science of Consciousness.9 I, along withmany others, am skeptical that a new physics is needed to realize all of AI’s
ambitions. (But, of course, we have not realized them yet.) Penrose attempts
to answer some of the criticisms of his views in his article “Beyond the
Doubting of a Shadow: A Reply to Commentaries on Shadows of the Mind.”10
B. Intentionality Is Needed
The American philosopher John Searle (1932– ; Fig. 24.2) argues thatcomputational processes, as we know them, do not have something humans dohave – something he and some other philosophers call “intentionality.”
Intentionality has to do with attaching “meaning” to objects and to propertiesof objects. Searle’s definition is as follows: “Intentionality is . . . that feature ofcertain mental states by which they are directed at or about objects and statesof affairs in the world.” For example, according to Searle, “beliefs, desires, andintentions are intentional states.”11 Thus, he would claim, although it ispossible to represent the phrase “John is tall” in a computer, say as a logical383
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24

Speed Bumps

Figure 24.1: Sir Roger Penrose.
expression such as “G33(K077),” there is no way for the computer to knowthat G33 refers to the “in-the-world” property of “tallness” nor that K077
refers to the actual John “in the world.” In short, computational processes
lack “aboutness”; they don’t know what their symbols are about. In contrast,
when humans use words, they know what those words are about.
Searle is famous among AI researchers and philosophers for a thought
experiment he proposed about “understanding.” It has come to be called “theChinese Room” experiment.12 Searle sets up the thought experiment by
writing
Suppose that I’m locked in a room and given a large batch of
Chinese writing. Suppose furthermore (as is indeed the case) that I
know no Chinese, either written or spoken, and that I’m not evenconfident that I could recognize Chinese writing as Chinese writing
distinct from, say, Japanese writing or meaningless squiggles.
To make his experiment relevant to AI work about “story understanding,”
384
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Opinions from Various Onlookers

Figure 24.2: John Searle. (Photograph courtesy of John Searle.)
Searle imagines that his room contains two batches of Chinese symbols, which,unknown to Searle, constitute a story and general background information
about such stories. The room also contains rules, written in English, about
how to manipulate sets of Chinese characters and how to generate Chinesecharacters as a result of such manipulations.
Into such a room, then, comes a third batch of Chinese symbols. As
Searle puts it, he has rules in the room (written in English) that
instruct me how to give back certain Chinese symbols with certain
sorts of shapes in response to certain sorts of shapes given me in
the third batch. Unknown to me, the people who are giving me allof these symbols call [the first two batches a story and its
background information] and they call the third batch “questions.”
Furthermore, they call the symbols I give them back in response to
the third batch “answers to the questions,” and the set of rules inEnglish that they gave me, they call the “program.” . . . Suppose
also that after a while I get so good at following the instructions
for manipulating the Chinese symbols and the programmers get sogood at writing the programs that from the external point of view
385
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24

Speed Bumps– that is, from the point of view of somebody outside the room inwhich I am locked – my answers to the questions are absolutely
indistinguishable from those of native Chinese speakers. Nobody
just looking at my answers can tell that I don’t speak a word ofChinese.

Searle’s question, essentially, is “Can it be said that the room (containing
Searle, the rules, and the batches of Chinese symbols) ‘understands’ Chinese?”
Searle claims the answer is “no” because all that is going on is “formal symbol
manipulation” without understanding what the symbols mean. In Searle’s
words:
Because the formal symbol manipulations by themselves don’t have
any intentionality; they are quite meaningless; they aren’t evensymbol manipulations, since the symbols don’t symbolize anything.
In the linguistic jargon, they have only a syntax but no semantics.
Such intentionality as computers appear to have is solely in theminds of those who program them and those who use them, those
who send in the input and those who interpret the output.
While acknowledging that the Chinese room “simulates” understanding,
he distinguishes between simulations and “the real thing.” He wrote
No one supposes that computer simulations of a five-alarm fire willburn the neighborhood down or that a computer simulation of arainstorm will leave us all drenched. Why on earth would anyone
suppose that a computer simulation of understanding actually
understood anything? . . . For simulation, all you need is the right
input and output and a program in the middle that transforms theformer into the latter. That is all the computer has for anything it
does. To confuse simulation with duplication is the same mistake,whether it is pain, love, cognition, fires, or rainstorms.
Searle’s Chinese Room reminds me of Herb Simon’s experiment insimulating the execution of the Logic Theorist (LT) program. Recall from
Section 3.2 that LT began by hand simulation, using Simon’s children as thecomputing elements, while writing on and holding up note cards as theregisters that contained the state variables of the program. Presumably, thechildren knew nothing about propositional logic, yet the whole assemblage,
Simon, the children, and the note cards, proved a theorem. Apparently,“simulating” the proof of a theorem is pretty much the same as actuallyproving a theorem – just as simulating addition is the same as addition. Could
it be that simulating understanding is really the same as real understanding?
There are several possible responses to Searle’s arguments, and there is no
shortage of responders! In his paper, Searle anticipates many potential replies,
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Opinions from Various Onlookers

and twenty-eight actual replies were published along with Searle’s original
paper. Here is what I think:Let’s imagine that we can look inside Searle’s brain when he is in theprocess of understanding a question put to him in English. There are,presumably, trillions of synapses engaged in firing and inhibiting billions ofneurons in a coordinated effort to make sense of the question and to compose
and deliver an answer. We would not claim that any individual synapse nor
the neurons it connects is understanding English. The process of“understanding” is not a concept that is meaningful at the level of detailappropriate for analyzing the workings of neurons. Analogously, the process ofproving a theorem by a computer (or by Simon’s children) is not a conceptthat is meaningful at the level of individual transistors (or children holdingnote cards). In explaining phenomena, either of the brain or of computers (orof anything else), we use concepts matched to the level of explanation. The
concept of “understanding” is a concept we find useful to apply to mental
activities viewed at the “whole-person level,” not at the nerve-cell level.
Similarly, we would, I think, find it useful to say that the assemblage of room,
Searle, rules, and Chinese characters understood Chinese.
But what about meaning and intentionality? If we write G33(K077) incomputer memory, does it “mean” anything? Well, it depends on what else isin computer memory – especially what else in memory is linked to the symbols
G33 and K077. The symbols and the links between them constitute a network,
and it is the whole network that contains the meanings. Recall the question
M. Ross Quillian was attempting to answer in his 1966 dissertation, namely,“What sort of representational format can permit the ‘meanings’ of words tobe stored, so that humanlike use of these meanings is possible?” Perhaps it’sworth repeating here something I wrote in Section 6.3:
According to Quillian, the meaning of a term is represented by its
place in the network and how it is connected to other terms. Thissame idea is used in dictionaries where the meaning of a word isgiven by mentioning the relationship of this word to other words.
The meanings of those other words are, in turn, given by theirrelationships to yet other words. So we can think of a dictionary as
being like a large semantic network of words linked to other words.
In some cases, it is also necessary to link a network’s symbols to actual
objects in the world through a computer’s sensory and motor facilities. Newell
and Simon anticipated this need in their paper about the physical symbol
system hypothesis (PSSH). That hypothesis claims that a physical symbol
system (such as a computer) has the necessary and sufficient conditions forintelligent behavior. Newell and Simon wrote13
A physical symbol system is a machine that produces through time
an evolving collection of symbol structures. Such a system exists in
387
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Speed Bumps
a world of objects wider than just these symbolic expressions
themselves.

Regarding this “world of objects,” a physical symbol system includes (inaddition to its means for formal symbol manipulation) the ability to“designate.” Here is Newell and Simon’s definition: “An expression [composedof symbols] designates an object if, given the expression, the system can either
affect the object itself or behave in ways dependent on the object ” (my italics).
So, I believe Searle is simply confused about some basic ideas in computerscience. Although it has not yet been empirically established that a computer(manipulating symbols and attached as needed to its environment) can bemade to exhibit all of the aspects of intelligent behavior of which humans arecapable, I don’t believe that Searle’s thought experiment casts doubt on thepossibility.
Searle himself believes that physical systems of some sort can be
intelligent and understand things. He believes that humans are one kind ofsuch a system. He wrote as follows:“Could a machine think?”
The answer is, obviously, yes. We are precisely such machines.
“Yes, but could an artifact, a man-made machine think?”
Assuming it is possible to produce artificially a machine with anervous system, neurons with axons and dendrites, and all the restof it, sufficiently like ours, again the answer to the question seems
to be obviously, yes.
Yet, Searle gives us no clue as to what it is about brains, composed ofneurons, that is different from computers, composed of transistors, thatendows the former, but not the latter, with intentionality. He claims that for amachine to think it would have to have “internal causal powers” equivalent tothose of brains. He does not say just what these internal causal powers mightbe.
C. Strong and Weak AI
Searle’s paper introduced definitions for “strong AI” and “weak AI” that are
useful for distinguishing between two types of AI endeavors. Strong AI isassociated with the claim that an appropriately programmed computer could
be a mind and could think at least as well as humans do. Achieving strong AI
is the ultimate goal for many artificial intelligence researchers. Searle’s article
attempts to show that strong AI (using computers) is impossible. However,practitioners of weak (or “cautious”) AI use programs as a tool to study themind by formulating and testing hypotheses about it. Weak AI has also come
388
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24.1

Opinions from Various Onlookers

to be associated with attempts to build programs that aid, rather thanduplicate, human mental activities. Weak AI has already been (and continues
to be) quite successful, whereas the quest for strong AI will no doubt go on fora rather long time.
D. “Global Processes” Are Needed
Hubert L. Dreyfus (Fig. 24.3), now a philosophy professor at UC Berkeley,began his career teaching philosophy at MIT.14 He first encountered the AI
enterprise there, and in the early 1960s he and his brother, Stuart, attended atalk by Herb Simon. Several things about AI and about what they heard in
the talk rankled the brothers Dreyfus. At about that time, the RAND
Corporation in Santa Monica, California, thought that having a philosopher
on board along with their computer people would be a good idea. Stuart, aspecialist in operations research who was working at RAND, recommended
Hubert. So Hubert spent the summer of 1961 at RAND as a consultant
studying AI research. Shortly after the summer, Hubert wrote a RAND paper
titled “Alchemy and Artificial Intelligence,” in which, among other things, heconcluded that the ultimate goals of AI research were as unachievable as were
those of alchemy.15

Figure 24.3: Hubert Dreyfus. (Copyright photo: Sijmen Hendriks. Used withpermission from Sijmen Hendriks.)
In his paper, Dreyfus evaluated AI progress in four areas, namely, gameplaying, problem solving (including theorem proving), language translation,and pattern recognition. He wrote
389
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Speed Bumps
An overall pattern is taking shape: an early, dramatic successbased on the easy performance of simple tasks, or low-quality workon complex tasks, and then diminishing returns, disenchantment,
and, in some cases, pessimism.

A typical case, he claimed, was Gelernter’s geometry-theorem proving
machine: “No more striking example exists of an ‘astonishing’ early successand the equally astonishing failure to follow it up.” And, answering AI’s claimthat progress is being made, he wrote “According to this definition [ofprogress], the first man to climb a tree could claim tangible progress toward
flight to the moon.”
One of the reasons for this stagnation, according to Dreyfus, was that AI
research is based on the assumption that thinking can be analyzed as a finite
set of simple determinate operations (such as the application of rules to a finite
set of data). Rather, he claimed, “thinking involves global processes, whichcannot be understood in terms of a sequence or even a parallel set of discrete
steps.” These global processes are manifest in three ways. The first is “fringe
consciousness.” It is what the brain uses to access the infinite “open-ended
information characteristic of everyday experience.” Fringe consciousness allows
humans to consider details and the big picture simultaneously. Another global
process is at work in human thinking when we distinguish the essential fromthe unessential. The third is “global context,” which allows us to reduce
ambiguity. A combination of these abilities permits what he calls “perspicuousgrouping” – what the brain does when it recognizes complex patterns, such as
human faces, for example. Dreyfus claimed that computer programs are unable
to employ these global processes, which are essential for intelligent behavior.
Dreyfus stated that the brain processes information in an entirely
different way than a computer does. He wrote that information in the brain is“processed globally the way a resistor analogue [a kind of analog computer]solves the problem of the minimal path through a network.” Furthermore, hesaid that the “body plays a crucial role in making possible intelligent
behavior.” Several other people have emphasized the importance of“embodiment” for progress in AI, and I’ll have more to say about that topic
shortly.
About the future (as judged from the early 1960s), Dreyfus wrote
Only experimentation can determine the extent to which newer
and faster machines, better programming languages, and clever
heuristics can continue to push back the frontier. Nonetheless, thedramatic slowdown in the fields we have considered and the general
failure to fulfill earlier predictions suggest the boundary may benear.
Dreyfus’s comments on AI should not be taken to imply that he thoughtthat human-level artificial intelligence by machines is impossible – he just390
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24.1

Opinions from Various Onlookers

thought (and still thinks) that it is impossible using the methods of what thephilosopher John Haugeland called “good old-fashioned artificial intelligence”(GOFAI),16 namely, the kind that uses heuristic search and discrete collectionsof symbolically represented facts and rules. He acknowledged that, inprinciple, “we could simulate intelligent behavior if we could build or simulate
a device which functioned exactly like the human brain.” But, he thought,
such a simulation could not be realized in practice. “We do not know theequations describing the physical processes in the brain, and even if we did,the solution of the equations describing the simplest reaction would take aprohibitive amount of time.” The summary of his paper concluded with
“Significant developments in artificial intelligence. . . must await an entirely
different sort of computer. The only existing prototype for it is thelittle-understood human brain.”
The main ideas of his RAND paper have been presented and expanded inseveral of Dreyfus’s books and articles.17 Pamela McCorduck’s book,
Machines Who Think,18 has an excellent chapter about Dreyfus, detailing hisarguments and the rather contentious interactions between him and AI
scientists. Because she covers that ground so well, I’ll concentrate on his ideas
about the need for “embodiment” as described in a couple of his recent papers.
E. “Being There” Is Needed
Dreyfus’s main point, I think, is that intelligence in humans derives from their“being in the world” and not because they are guided by rules. The use of rulesin AI programs (as in humans) might allow competent behavior but not expert
behavior. Here are some excerpts from an address Dreyfus gave in 2005:19. . . in our formal instruction we start with rules. The rules,
however, seem to give way to more flexible responses as we become
skilled. . . . The actual phenomenon suggests that to become
experts we must switch from detached rule-following to a more
involved and situation-specific way of coping.
...
In general, instead of relying on rules and standards to decide on
or to justify her actions, the expert immediately responds to thecurrent concrete situation.
...
“Expert Systems” based on the rules so-called knowledge engineers
elicited from experts were at best competent. It seems that,instead of using rules they no longer remembered, as the AI
researchers supposed, the experts were forced to remember rules
they no longer used. Indeed, as far as anyone could tell, theexperts weren’t following any rules at all.
391
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Speed Bumps

According to Dreyfus, the transition from merely competent behavior toexpert behavior requires “being in the world” through having a bodyembedded in the world. Embodied agents, such as humans, “can dwell in theworld in such a way as to avoid the infinite task of formalizing everything” (asAI programs futilely attempt to do).20 Dreyfus’s view of this need for
embodiment is based on a branch of a philosophical school called“phenomenology.” Dreyfus wrote me that the existential phenomenology ofMartin Heidegger (1989–1976), which stresses “our practical involvement withpeople and things as our basic way of being,” is the basis for his critique of
GOFAI.21 He argues that for AI to succeed it would need22. . . a model of our particular way of being embedded and embodied
such that what we experience is significant for us in the particular
way that it is. That is, we would have to include in our program amodel of a body very much like ours with our needs, desires,
pleasures, pains, ways of moving, cultural background, etc.
Others arguing for embodiment point out that some of the“computations” needed by an intelligent agent could be accomplished by the
dynamic interactions between parts of its body and its environment. For
example, Rolf Pfeifer, Max Lungarella, and Fumiya Iida have written that “Anembodied perspective, because it distributes control and processing to all
aspects of the agent (its central nervous system, the material properties of its
musculoskeletal system, the sensor morphology, and the interaction with theenvironment), provides an alternative avenue for tackling the challenges facedby robotics. The tasks performed by the controller in the classical approach
are now partially taken over by morphology and materials in a process of
self-organization. . . ”23
But even if a body were needed, its form would seem to depend on what
the associated AI system is used for. The body of the fictional HAL 9000 was
the entire spacecraft that it controlled. Shakey the robot had a body that wasapparently appropriate for its needs. If ever a “conversational Google” were to
be developed that could engage in dialogs with users about the content of all
Web pages, its “body” would be the entire Internet and the routines needed toaccess it.

24.1.3Differences between Brains and Computers

In addition to Dreyfus, several critics of AI have pointed out that “the brain is
not a computer,” and, therefore, people who are attempting to do withcomputers what brains can do must necessarily fail. These critics often stressdistinctions such as the following:• Computers have perhaps hundreds of processing units whereas brains
have trillions.
392
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24.1

Opinions from Various Onlookers• Computers perform billions of operations per second whereas brains need
perform only thousands.
• Computers are subject to crashes whereas brains are fault tolerant.
• Computers use binary signals whereas brains work with analog ones.
• Computers do only what their programmers tell them to do whereas
brains are creative.
• Computers perform serial operations whereas brains are massively
parallel.
• Computers are constrained to be “logical” whereas brains can be“intuitive.”• Computers are programmed whereas brains learn.
Aside from the fact that many of these distinctions are no longer valid,24
comparisons depend on what is meant by “the brain” and what is meant by “acomputer.” If our understanding of the brain is in terms of its component
neurons, with their gazillions of axons, dendrites, and synaptic connections,and if our understanding of a computer is in terms of serial, “vonNeumann–style” operation – reading, processing, and writing of bits – allaccomplished by transistor circuitry, well then of course, the brain is not thatkind of a computer.
However, we don’t understand “computation” by reference only to a
low-level, von Neumann–style description. We can understand it at any one ofa number of description levels. For example, computation might beunderstood as a very large number of concurrently active “knowledge sources”
asynchronously reading from, transforming, and writing complex symbolic
expressions on a “Blackboard” or as a collection of symbol-processing andneural network demons arranged in a Pandemonium-style network. Perhaps
our gradually increasing understanding of how the brain operates will evenlead to other useful computational models. Ideas about what “computation”
can be are ever expanding, so those who would claim that the brain is not a
computer will need to be more precise about just what kind of computer thebrain is not. (After all, if some people, like Lucas, can restrict what a machine
can be, it seems only fair that others can expand the definition of what acomputer can be.)24.1.4

But Should We?

Besides the criticisms of AI based on what people claim it cannot do, there arealso criticisms based on what people claim it should not do. Some of the“should-not” people mention the inappropriateness of machines attempting to393
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24

Speed Bumps

perform tasks that are inherently human-centric, such as teaching, counseling,
and rendering judicial opinions. Others, such as the Computer Professionals
for Social Responsibility mentioned previously, don’t want to see AI
technology (or any other technology for that matter) used in warfare or for
surveillance or for tasks that require experience-based human judgment. In
addition, there are those who, like the Luddites of 19th century Britain, areconcerned about machines replacing humans and thereby causing
unemployment and economic dislocation. Finally, there are those who worry
that AI and other computer technology would dehumanize people, reduce theneed for person-to-person contact, and change what it means to be human.
Joseph Weizenbaum (1923–2008; Fig. 24.4), the man who wrote the ELIZA
program I mentioned in Section 2.3.3, has written and lectured about thedangers of giving computers responsibilities that he thought ought best be left
to humans. Some say that the motivating reason for his concern was that hewas surprised and shocked by the fact that some people mistook conversations
with ELIZA for conversations with a real person. In his book Computer Powerand Human Reason: From Judgment to Calculation,25 Weizenbaum argued
that “there is a difference between man and machine, and. . . there are certain
tasks which computers ought not be made to do, independent of whether
computers can be made to do them.
In his book, Weizenbaum stressed the importance of the cultural milieu in
which a person grows up, lives, and works. No machine experiences (or could
experience) a human-type background, and therefore no machine should beallowed to make the kinds of decisions or give the kinds of advice that require,among other things, the compassion and wisdom engendered by such abackground. He emphasizes this point by saying that inexperience with these“domains of thought and action” would also apply “to the way humans relate
to one another as well as to machines and their relations to man.”26 Thus, I
suppose he would think that just as it would be inappropriate for a machine to
make judicial decisions, so also would it be inappropriate for a person raised in
America to make judicial decisions in Japan. Moreover, he ridicules the ideathat a machine could obtain the necessary background by giving it ahuman-like body and sensory apparatus. He wrote that “the deepest and mostgrandiose fantasy that motivates work on artificial intelligence. . . is nothing
less than to build a machine on the model of man, a robot that is to have its
childhood, to learn language as a child does, to gain its knowledge of the world
by sensing the world through its own organs, and ultimately to contemplate
the whole domain of human thought.”27
Weizenbaum escaped from Nazi Germany with his family in 1936. Thatexperience cannot but have sharpened his keen sense of social responsibility.
He wrote, for example, that28
The very asking of the question, “What does a judge (or apsychiatrist) know that we cannot tell a computer?” is a monstrous
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24.1

Opinions from Various Onlookers

Figure 24.4: Joseph Weizenbaum. (Photograph courtesy MIT Museum.)
obscenity. That it has to be put in print at all, even for the purposeof exposing its morbidity, is a sign of the madness of our times.
...
[The relevant issues] cannot be settled by asking questions
beginning with “can.” The limits of the applicability of computers
are ultimately statable only in terms of oughts. What emerges as
the most elementary insight is that, since we do not now have any
ways of making computers wise, we ought not now to give
computers tasks that demand wisdom.
Even though Weizenbaum hedges a bit on the “can” question, I believe hereally believed that machines “could not” as well as “should not.” For if
machines really could make judgments with all of the “compassion andwisdom” with which humans can, why shouldn’t they? In addition to theconcern about using any technology for antisocial purposes (such as war), thereal danger, I think, lies in the premature use of machines: thinking that they
are able to perform a task before they are really competent to do so.
395
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24

Speed Bumps

Another person who recoiled from the prospect of machines “taking over”
is the physician, biologist, and essayist Lewis Thomas (1913–1993). In one of
his celebrated columns, “Notes of a Biology-Watcher,” in The New England
Journal of Medicine, he wrote29
The most profoundly depressing of all ideas about the future of thehuman species is the concept of artificial intelligence. The ambition
that human beings will ultimately cap their success as evolutionary
overachievers by manufacturing computers of such complexity andingenuity as to be smarter than they are, and that these devices
will take over and run the place for human betterment or perhaps,
later on, for machine betterment, strikes me as wrong in a deep
sense, maybe even evil.
...
This is what the artificial intelligence people are talking about: amechanical brain with the capacity to look back over the past andmake accurate predictions about the future, then to lay out flawlessplans for changing that future any way it feels like, and, mostappalling of all, capable of feeling like doing one thing or another.
...
It is, in my view, an absolutely hideous prospect, and if I thought
it were really something waiting ineluctably ahead of us I would
spend all my days in protest.
Although there have been several other authors who have warned about
the dangers of the inappropriate use of computers in general and of intelligent
machines in particular, I’ll mention just one more, a self-confessed“neo-Luddite.” Theodore Roszak (1933– ) is a prominent author and social
thinker – one well worth reading in my opinion. In his book The Cult ofInformation,30 he claimed that a growing cult, infatuated with “information”
and “information processing,” is having debilitating cultural effects –“especially when it comes to teaching the young.” Roszak wrote that he is “anally of all those serious students and users of information technology who holda reasonably balanced view of what computers can and cannot, should andshould not, do,” but claims that “the creation of a mystique of information[has made] basic intellectual discriminations between data, knowledge,
judgment, imagination, insight and wisdom impossible.”31
Claiming that there is a “vital distinction” between information
processing and thinking, he wrote32
Because the ability [of the computer] to store data somewhat
corresponds to what we call memory in human beings, and because
the ability to follow logical procedures somewhat corresponds towhat we call reasoning in human beings, many members of the cult396
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24.1

Opinions from Various Onlookers[of information] have concluded that what computers do somewhat
corresponds to what we call thinking.

However, Roszak concludes, computers cannot really “think.” The danger
is that those who are persuaded (or duped into believing) that they can might
inappropriately employ computers in tasks that require thinking and not just“data processing.” I believe Roszak has a legitimate concern here – AI is not
yet up to all of the tasks to which we might try to put it.
But Roszak also wrote33
There is no possibility that computers will ever equal or replace themind except in those limited functional applications that do
involve data processing and procedural thinking. The possibility isruled out in principle, because the metaphysical assumptions that
underlie the effort are false.
Here, I disagree. I know of no “metaphysical assumptions” of AI otherthan that the brain is a kind of machine and therefore we ought to be able tounderstand it and build something that works very much like it. Furthermore,
I know of no credible evidence that that metaphysical assumption is false.
Although he does not think that computers can become minds, he worries
about the additional danger that “it is possible to redefine the mind and its
uses in ways that can be imitated by machine. Then we have a mechanicalcaricature which levels the activity down to a lower standard.”34
Roszak does have at least two good things to say about AI – one anegative result and one a positive contribution. As for the negative result, hesays35
There is an ironic but highly valuable quality to AI in all its forms.
The effort to simulate or surpass human intelligence is uncovering
subtleties and paradoxes about the human mind we might never
have imagined. By way of heroic failures, AI is teaching us how
truly strange real intelligence is.
On the positive side he comments that36
One field of AI, however, has made remarkable progress. . . Often,
by quizzing specialists closely about their work, computer
programmers can tease out procedures, assumptions, values that
can then be formally specified. The result is an Expert System, oneof the few practical applications of AI. Edward Feigenbaum seessuch systems as the gateway to the next era of machine
intelligence; he calls it “knowledge processing,” as opposed to mere
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24

Speed Bumps
data processing. Whatever he may mean by “knowledge,” it surely
represents a more complex approach to thinking than once
prevailed in the field.

24.1.5

Other Opinions

In January 1981, to sample some opinions about AI for a talk I was planning,
I wrote to some leaders in computer science and related disciplines asking
them what they thought about AI’s achievements, weaknesses, and prospects.
I received several replies and will excerpt some comments.37
The computer scientist (and my colleague at Stanford) Donald Knuth
wrote
I’m intrigued that AI has by now succeeded in doing essentiallyeverything that requires “thinking” but has failed to do most ofwhat people and animals do “without thinking” – that, somehow,
is much harder! I believe the knowledge gained while building AI
programs is more important than the use of the programs. . .
John R. Pierce, whom I have already mentioned in connection with both
the ALPAC report on machine translation (in Section 7.2) and his negative
comments about speech understanding (p. 282), wrote me a very short letterin which he stated
Concerning artificial intelligence, I believe I invented the slogan,“Artificial intelligence is real stupidity.”...
I resent artificial intelligence because I feel that it is unfair tocomputers. But then, artificial intelligence people did devise LISP,
which is pretty good.
The letter did not elaborate either on the slogan38 or why AI is “unfair tocomputers.”
The Dutch computer scientist Edsger W. Dijkstra (1930–2002) was
famous for many innovations in computer science, including an algorithm forfinding the shortest (or least-costly) paths in graphs. He also championed
what is called “structured programming,” a methodology that greatly
improved the efficiency of writing (and understanding) programs. In response
to my letter, he wrote (most cordially and in beautiful penmanship)
To the artificial intelligentsia that argue “But we are only symbol
manipulating machines, aren’t we?” one can only answer “There is
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24.2

Problems of Scalenone so blind as them that won’t see!” The analogy is so shallowthat I can characterize an appeal to it only as typically medieval
thinking.

In addition to concerns about AI’s “overstated claims,” many computerscientists thought AI to be a kind of “fringe activity” that did not adhere to
rigorous scientific standards and a field that housed charlatans. I recall thatwhen I first interviewed for a position at SRI in 1961, I was warned by one
researcher there against joining research on neural networks. Such research, heclaimed, was “premature,” and my involvement in it could damage myreputation.
Concern for “respectability” has had, I think, a stultifying effect on someAI researchers. I hear them saying things like, “AI used to be criticized for its
flossiness. Now that we have made solid progress, let us not risk losing ourrespectability.” One result of this conservatism has been increased
concentration on “weak AI” – the variety devoted to providing aids to humanthought – and away from “strong AI” – the variety that attempts tomechanize human-level intelligence. This is too bad, because, although I think
the goals of weak AI are important and worthy, building an artifact thatmimics the abilities of the human brain would be a tremendous scientificachievement – well worth the risk and not at all an “obscenity,” “evil,”“hideous,” nor “impossible in principle.”

24.2

Problems of Scale

24.2.1The Combinatorial Explosion

Because search plays such a prominent role in artificial intelligence, it isimportant to say something about how extremely difficult search problems canbe. A typical search problem is usually cast as growing a “tree” of nodes, suchas Arthur Samuel’s checkers game tree shown in Section 5.4 or the sliding-tile(eight-puzzle) search tree shown in Section 5.1. For example, if each node in asearch tree has three possible “child” nodes (that is, a “branching factor” of3), the top part of the tree would look like the one in Fig. 24.5.
The “first level” of the tree has three nodes, the second has nine nodes,
and so on. In the general case, for a tree with branching factor b, the dth level
would have bd nodes (that is, b multiplied by itself d times). The total numberof nodes that a search process would generate if it generated a whole tree with
branching factor b down to and including all of the nodes at depth level d canb(bd − 1). Readers who recall their high school algebrabe calculated to be (b−1)
will recognize these expressions as “exponential” functions of d. Because thenumber of nodes in a search tree is an exponential function of its depth, searchis called an exponential process.
399
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Speed Bumps

Figure 24.5: A search tree.
If a program had to search a tree with branching factor of 3 to a depth
level of 10 to find a goal, it would have to generate 88,572 nodes. Numbers likethat were well within the range of the capabilities of computers of the 1960s
and 1970s, and so they were quite capable of solving some of the simpler AI“toy problems.” But more realistic problems would involve search trees ofmuch higher branching factors, having goals at much greater depth levels. For
example, to search a tree with branching factor of 10 to depth 20 (a treecorresponding to only a modestly difficult search problem) would require thegeneration of more than 1020 nodes, a quite impossible feat. (1020 is one
followed by 20 zeros, that is, 100 billion billion.)
The difficulty of such searches has two aspects: computing time andstorage space. Considering just computing time for a moment, even if we
could generate a billion nodes each second (which is perhaps just barely
thinkable), it would still take 100 billion seconds (over 3,000 years) to generate
the tree with the branching factor and depth we have just been considering.
Regarding storage space, even personal computers these days come withlots of it – 100 gigabytes (100 billion bytes) is typical. Assuming that a single
node requires about one byte, one would need the storage equivalent of arounda billion such computers for even our modestly sized search tree.
The exponential nature of search means that as a problem’s size increases(as measured either by the branching factor or by the depth of search trees),
the computational difficulty needed to solve the problem increases drastically –creating what is called a “combinatorial explosion.”

400
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24.2

Problems of Scale

Of course, even early AI researchers knew about combinatorial explosions.
That’s the reason for their interest in heuristics. Unfortunately, heuristics do
not change the exponential character of search – at best they reduce thebranching factor. The “explosion” still happens – just not quite so rapidly.
For example, reducing the branching factor from 10 to 4 and searching again
to depth 20 would still require the generation of over 420 or, roughly, one
trillion nodes.
AI critics have focused on this problem in their pessimistic assessments ofAI’s achievements and prospects. For example, Sir James Lighthill (in his
report that I mentioned in Chapter 16) wrote that “one rather general cause
for the [AI] disappointments that have been experienced [is a] failure torecognize the implications of the ‘combinatorial explosion.’” Lighthill’s report
did cause, as I have already mentioned, funding difficulties for AI research in
Britain.

24.2.2Complexity Theory

AI researchers are not the only people who are concerned about thecomputational difficulty of problem solving. A branch of computer sciencecalled “complexity theory” deals with how long and how much storage spacedifferent kinds of programs might take (in the worst case) to solve different
kinds of problems. They ask, among other things, “How does the size of aproblem affect the time and space required to solve it?”
Let’s look at some examples. The time that it would take for a computer
program to find the largest number in a list of numbers is proportional to thesize of this problem, namely, the number of items in the list. (The worst casewould happen if the largest item happened to be the last item in the list; aprogram would then have to examine each item in the list.) Such a program issaid to take “linear time.” Similarly, a program for finding out whether a given
item is a member of a list of items would take linear time. (Again, the worst
case would happen if the item happened to be the last item in the list.) In
both cases, if we were to double the size, we would double the time required.
Sorting a list of names, putting them in alphabetical order for example, isa harder problem. Programs for sorting lists differ in how long they take andhow complicated they are to program. Reasonably simple sorting programs
take time that is proportional to the square of the number of items in the list.
That is, for these programs, sorting a list that has 100 items in it would takefour times as long as a list with only 50 items in it. (Multiplying the numberof elements by two increases the sorting time by two squared, or four. Sorting
can actually be done faster. There are programs that can sort a list in time
proportional to the logarithm of the size times the size.) Programs that can (inthe worst case) take time proportional to a problem’s size, or the square, or the
cube, or other “powers” of the size, run in what is called “polynomial time.”

401
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Speed Bumps

Problems themselves can be graded according to the complexity of theleast complex programs that are able to solve them. For example, if a program
exists that can solve a problem in linear time, but none could solve it faster,than that problem is said to be of “linear complexity.” Finding out whether or
not an item is a member of a list of items is a problem of linear complexity.
We denote by P the class of problems that can be solved in polynomial time.
Members of this class include calculating the greatest common divisor anddetermining whether or not a number is a prime number.
Unfortunately, as I have already mentioned, the search procedures used inmany AI programs require running times that are exponential in the size ofthe problem. For example, searching a tree having branching factor of b to
depth d would take time proportional to bd . Using my previous example, we
see that searching a tree with branching factor of 3 to a depth level of 10
requires the generation of 88,572 nodes. But doubling the depth to 20 would
require searching not four times 88,572 nodes (which would be the case if
search time was proportional to the square of the depth) but 88,572 × 88,572
or almost eight-billion nodes. Exponential complexity is much, much worse
than polynomial complexity!
The American computer scientist Stephen Cook (1939– ) [as well as theRussian, and now American, computer scientist Leonid Levin (1948– )] mademajor contributions to our understanding of the complexity of problems andthe programs used to solve them. In particular, Cook (and Levin
independently) defined the class called the nondeterministic polynomial, orNP, class of problems. This is the class of problems for which a candidate
solution can be checked (but not necessarily solved) in polynomial time. For
example, a proposed sequence of moves to solve a sliding tile puzzle can bechecked to see whether it actually solves the puzzle in polynomial time, but (asfar as is known) it would require exponential time to find a solution. So sliding
tile problems, along with many other AI problems, belong to the class NP.39
It is not known whether or not there might be polynomial programs forsolving the problems in the class NP. If there were, NP would equal P. So far,programs for solving problems in the class NP require exponential time (in theworst case). Whether or not NP equals P is one of the most famous unsolved
problems in computer science. Many people think NP does not equal P
because otherwise we would have found out by now. (The Clay Mathematics
Institute has offered a prize of $1,000,000 for a solution of the P versus NP
question – that is, showing either that they are equal or not equal.)40

24.2.3

A Sober Assessment

These results in complexity theory caused some people to have grave doubts
about the prospects for artificial intelligence. One of the most penetrating,and to my mind intelligent, assessments was written by the mathematician
and computer scientist Jacob T. Schwartz (1930–2009; Fig. 24.6), whom I
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24.2

Problems of Scale

have already mentioned in connection with his tenure at DARPA. In his article
titled “The Limits of Artificial Intelligence,”41 Schwartz wrote that theextraordinary powers of the human brain arose from the way it used its
prodigious computational and storage abilities “to organize information
presented in relatively disordered form into internally organized structures on
which sophisticated, coherent courses of symbolic and real-world action can bebased.” To rival the brain, what AI needs, and what AI has vainly been trying
to achieve, Schwartz claimed, are “coherent structures capable of directly
guiding some form of computer action. . . generated automatically from
relatively disorganized, fragmented input.” The ability to generate such“organized structures” would constitute a tremendous breakthrough because
computers are “already enormously superhuman” in solving problems forwhich they can “accept, retain, and utilize fully structured material.” “If thebasic obstacle posed by the need to program [computers] in detail could beovercome,” he wrote, “computers could ingest the information contained in all
the world’s libraries and use this information with superhuman effectiveness.”(Nowadays, of course, besides libraries there is all the information, andmisinformation, on the World Wide Web.)

Figure 24.6: Jacob (Jack) Schwartz. (Photograph used with permission of Diana
Schwartz.)
The methods that AI researchers had used for automatically creating
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24

Speed Bumps

structure from fragmented input were all based on heuristic search – either
searching for chains of logical deductions or for paths in trees (and moregenerally graphs). Logical deductions had been used (among other things) toanswer questions, to provide expert advice, and to generate programs andplans. Searching for paths in graphs had been used to prove theorems ingeometry, to parse sentences, and to produce plans for robots. All of these are
instances of creating structure (to use Schwartz’s term) from unstructured
input. These “successes” however were all achieved on either trivially small
problems or on ones whose subject matter was strictly circumscribed.
Unfortunately, as Schwartz correctly claimed, all attempts to date to generate“broadly useful symbolic structures from more disorganized and fragmentary
input” have invariably been defeated by the combinatorial explosion. He
summarized the situation by echoing Dreyfus’s charge that “. . . the history of
AI research to date [consisted] always of very limited success in particularareas, followed immediately by failure to reach the broader goals at whichthese initial successes seem at first to hint. . . ”
Schwartz’s opinions about AI did have consequential effects because, as I
mentioned in Section 23.4, he was the Director of the DARPA ISTO from 1987
to 1989 and presided over some cutbacks in AI research (including thecancellation of one of my own research projects in 1987). Even though he was
generally dismissive of AI work, Schwartz did write that those “areas of AI towhich classical scientific and algorithmic techniques apply can be expected toprogress more rapidly than areas that deal with deeper problems for which
only less focused approaches are available.” As one example, he cited the
problem of determining “whether one or more objects of known shape moving
in an environment containing obstacles of other known shapes can pass fromone specified position to another without colliding either with the obstacle orwith each other.”
Although these results from complexity theory did constitute one of the“speed bumps” in AI’s rapid forward progress, AI researchers quickly recovered
and found various ways around the combinatorial explosion problem. They
pointed out, for example, that complexity results were based on worst-caseperformance, and solutions might often be found faster than in the worst case.
For example, I’ll mention the work of Richard Korf (1956– ; Fig. 24.7), an
AI researcher at the University of California at Los Angeles. Korf is wellknown for his work on tackling extremely difficult search problems. He often
uses the sliding tile and other puzzles as laboratory “drosophila” for exploringnew ideas in search.
You will recall that I used the example of a sliding tile puzzle to illustrateheuristic search processes. The one I used consisted of eight tiles in a 3 × 3
array; the classic version consists of fifteen tiles in a 4 × 4 array. One canimagine larger versions, such as twenty-four tiles in a 5 × 5 array. The 4 × 4
puzzle already presents a quite challenging problem for heuristic search. In
fact, in his comments about the scaling difficulties of search processes, Jack
404
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24.2

Problems of Scale

Figure 24.7: Richard Korf. (Photograph courtesy of Richard Korf.)
Schwartz wrote “. . . the graph of states [of the 3 × 3 puzzle] consists of 9!, or
362,880 [nodes], so even for so simple a problem brute-force graph search
begins to become taxing. For the corresponding 4 × 4 puzzle, whose state
space involves 16!, or over 1013 , nodes, it is completely infeasible.” [Actually,
Schwartz was off by (an inconsequential) factor of 2 in both cases. It has been
known since 1879 that if you start from any particular initial configuration,
you can only reach 1/2 of all the possible configurations.42 Thus, for theeight-tile version, the entire state-space graph consists of two separate
disconnected graphs of size 9!/2 = 181, 440.]
Yet, not only has Korf written heuristic search programs to solve
instances of the 4 × 4 puzzle, but in an abstract of a 1996 paper he and his
co-author wrote 43
We have found the first optimal solutions to a complete set of405
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24

Speed Bumps
random instances of the Twenty-Four Puzzle, the 5 × 5 version of
the well-known sliding-tile puzzles. Our new contribution to thisproblem is a more powerful admissible heuristic function.
...
[W]e observe that as heuristic search problems are scaled up, more
powerful heuristic functions become both necessary andcost-effective.

The search space for the 5 × 5 puzzle is about a trillion times larger than thatof the 4 × 4 puzzle. It has about 7.756 ×1024 nodes.
Search is also made easier if one could be satisfied with “good” orapproximate solutions to problems rather than insisting on the “best”
solutions. In a subsequent chapter, I’ll describe some rather remarkableprogress on dealing with large search problems; this progress has flattened the
speed bumps a bit.
Even with these ways around the complexity results, however, AI people
did begin to acknowledge other shortcomings – a subject I’ll turn to next.

24.3

Acknowledged Shortcomings

As AI researchers began to confront problems of practical importance, they
themselves had to acknowledge several difficulties. These came up in several
application areas. I’ll mention just a few.
In attempting to prove nontrivial mathematical theorems, for example,
theorem-proving programs quickly exhausted the space necessary to store
intermediate results. But humans (well, some humans at least) are able toprove theorems. What methods are they using that computer programs arenot? Mathematicians would probably say that intuition, judgment, experience,
mathematical sophistication, and such are critical to their successes. So far, it
has proven difficult to provide computers with these capabilities.
In game playing, although MAC HACK VI and CHESS 4.6, to name two
examples, played pretty good chess, they were far from being able to beatworld champions. In fact, in August 1978 at the Canadian National Exhibitionin Toronto, David Levy defeated the reigning Computer Chess Champion,
CHESS 4.7, from Northwestern University, thus winning the bet he made ten
years before against John McCarthy and Donald Michie. As Levy put it, “Imanaged to beat the program fairly convincingly, by three wins to one with
one game drawn (the sixth game did not need to be played) and with thismatch I won my bet.”44
What accounts for championship ability? It’s unlikely that chesschampions look at more chess positions than computers do. However, they406
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24.3

Acknowledged Shortcomings

probably look farther ahead in the game tree along the most importantbranches. Their experience seems to enable them to evaluate the potential of
those candidate positions that are worthy of exploration and to ignore further
exploration of worthless positions. Perhaps they also think in a more strategicmanner than do chess programs that consider individual chess moves.
Another game, perhaps more challenging for humans even than chess, isthe game of Go – a board game that originated in China more than 4,000 years
ago and is very popular in Asian countries today. In Go, two players alternate
in placing black and white stones at the intersections of a 19 × 19 grid of two
sets of lines ruled on a board. I won’t describe the rules of the game here,
except to say that (at least in the early stages of the game) a player is facedwith the problem of deciding at which of almost 361 (19 × 19) positions toplace a stone. Even for the most powerful search processes, searching a tree,
each of whose nodes has nearly 361 immediate descendants, is out of thequestion. Human players must be using other strategies, and, whatever thesestrategies are, they are still unknown to AI researchers. Many AI researchers
think that performance in Go is a better measure of AI’s abilities than is
performance in chess. I’ll describe some recent progress later in the book.
Although expert systems reason usefully (and even with economic
advantage) about specific problems in medicine, geology, chemistry and otherdelimited areas, they are acknowledged to be “brittle” – that is, they break
down when confronted with problems outside their area of expertise or even on
problems within their area of expertise if knowledge were needed that had notbeen provided in their rules. They don’t know what they don’t know andtherefore might provide wrong answers in cases where a human expert would
do better. It is said that John McCarthy, in an interaction with the medical
expert system MYCIN, typed in some information about a hypothetical patient,saying that he was male and also saying that he underwent amniocentesis.
MYCIN accepted all of that without complaint! That male patients don’t get
pregnant was not considered part of the “expert knowledge” that MYCIN
needed to be given.
One of the reasons why expert systems are brittle is that they lack“common sense.”45 In addition to the expert knowledge that humans might
acquire through education and professional experience, they also have a lot ofgeneral knowledge. They know, for example, that only females can become
pregnant, that umbrellas protect against sun and rain, that certain birds
migrate, that food can be purchased in markets, and millions upon millions ofother facts. Benjamin Kuipers, an AI researcher and professor at the
University of Michigan (formerly at the University of Texas at Austin), definedcommon sense this way: “Commonsense knowledge is knowledge about thestructure of the external world that is acquired and applied without
concentrated effort by any normal human that allows him or her to meet the
everyday demands of the physical, spatial, temporal and social environment
with a reasonable degree of success.”46

407
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24

Speed Bumps

This general knowledge is acquired gradually as children grow into adults
and as adults mature. For example, a child probably does not know that small
tablets in little plastic bottles might be dangerous if swallowed (which is why
these bottles have child-proof caps), teenagers know a lot of things thateight-year olds typically do not, and the knowledge that enables a reader of
The New Yorker, say, to understand its reviews of books and films goesbeyond what a teenager typically knows. Also, of course, people in different
countries and cultures will have different common-sense knowledge.
It seems to me that the knowledge of any particular human should bethought of as an ever-growing tree whose base and lower branches comprise“common sense” and whose upper ramifications comprise the “expertise” ofspecialized disciplines that the person might have learned. The tree metaphor
is also useful in emphasizing the point that the knowledge in the upper
branches uses concepts that occur in the trunk and lower branches.
We saw in an earlier chapter that a full understanding of sentences innatural language seemed to require the common-sense information thathumans have but computers still do not. The daunting prospect of endowing
computers with common sense has led to two quite opposite reactions. Somesee this difficulty as ruling out the possibility of AI (or at least of strong AI)
for the foreseeable future. Others, though, say “Let’s get on with it.” (I’ll betalking about the work of one of the let’s-get-on-with-it people in a subsequent
chapter.) Of course, it is no more to be expected that any particular AI
system will understand all natural language sentences than it can be expectedthat any particular human will understand natural language sentences about
all subjects. Humans have their limitations, and AI programs will have themtoo. That prospect should no more limit our attempts to produce intelligent
programs than it does to educate intelligent humans.

24.4

The “AI Winter”

During the early 1980s, many AI sponsors, in government and in industry, hadgreatly inflated expectations of what AI could do. Undoubtedly, some of theblame for their unjustified optimism could be placed on AI researchers
themselves who were motivated to make exaggerated promises. The failure todeliver systems matching these unrealistic hopes, together with theaccumulating critical commentary that I have already mentioned, combined in
the mid- to late 1980s to bring on what came to be called an “AI winter.”Indeed, at the 1984 AAAI National Convention several leading AI
researchers warned about this possibility during a panel session titled “The‘Dark Ages’ of AI – Can We Avoid Them or Survive Them?” The panel’schair, Drew McDermott of Yale University, started the session off by saying47
In spite of all the commercial hustle and bustle around AI these408
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24.4

NOTES
days, there’s a mood that I’m sure many of you are familiar with ofdeep unease among AI researchers who have been around morethan the last four years or so. This unease is due to the worry thatperhaps expectations about AI are too high, and that this will
eventually result in disaster.
. . . I think it is important that we take steps to make sure the “AI
Winter” doesn’t happen – by disciplining ourselves and educating
the public.

But if “disciplining” and “educating” did take place, they were insufficient
to prevent the worried-about downturn. During the late 1980s, membership in
the AAAI gradually fell. By 1996, it had leveled off to between 4,000 and
5,000 members. Advertising in the AI Magazine dropped also – as did
participation by government and industry in AI conference exhibits. Several
AI companies closed their doors, and AI research at some of the larger
computer hardware and software companies was terminated. According toAlex Roland, between 1987 and 1989, DARPA’s budget for basic AI and
Strategic Computing research fell from $47 million to $31 million. (Even so,
according to Alex Roland, CMU’s budget was increased for its speech
understanding program and its autonomous vehicle program during this time.)
But the winter endured only for a season – a season not of hibernation butof renewed efforts to carry on. Several new ideas were explored, and older ones
were strengthened with added powers, as I’ll explain in subsequent chapters.

Notes
1. Hilary Putnam, “Much Ado about Not Very Much,” Dædalus (Special Issue on Artificial
Intelligence), pp. 269–281, Winter 1988. [381]2. Alan Ross Anderson (ed.), Minds and Machines, Englewood Cliffs, NJ: Prentice-Hall,
Inc., 1964. [381]
3. John R. Lucas, “Minds, Machines, and Gödel,” Philosophy, Vol. XXXVI, pp. 112–127,
1961; reprinted in Alan Ross Anderson (ed.), Minds and Machines, pp. 43–59, Englewood
Cliffs, NJ: Prentice-Hall, Inc., 1964; available online at
http://users.ox.ac.uk/∼jrlucas/Godel/mmg.html. [382]4. Lucas himself lists some references to these in a Web site,
http://users.ox.ac.uk/∼jrlucas/Godel/referenc.html. [382]
5. John R. Lucas, The Freedom of the Will, Oxford: Oxford University Press, 1970. Lucasclaims on his Web site that the book is out of print but is now available from Oxford
University Press “on a one-off basis.” [382]6. Available online at http://users.ox.ac.uk/∼jrlucas/Godel/brighton.html. [382]7. Mortimer Taube, Computers and Commonsense: The Myth of Thinking Machines, New
York: Columbia University Press, 1961. [383]8. Roger Penrose, The Emperor’s New Mind: Concerning Computers, Minds and the Laws
of Physics, New York: Random House, Inc., 1989. [383]409
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24

NOTES

9. Roger Penrose, Shadows of the Mind: A Search for the Missing Science ofConsciousness, Oxford: Oxford University Press, 1994. [383]
10. Roger Penrose, “Beyond the Doubting of a Shadow: A Reply to Commentaries on
Shadows of the Mind,” PSYCHE, Vol. 2, p. 23, January 1996. Available online (withpointers to articles by critics) at
http://psyche.cs.monash.edu.au/v2/psyche-2-23-penrose.html. [383]
11. See John R. Searle, “Minds, Brains, and Programs,” Behavioral and Brain Sciences,Vol. 3, No. 3, pp. 417–457, 1980. Available online at
http://www.bbsonline.org/documents/a/00/00/04/84/bbs00000484-00/bbs.searle2.html.
[383]
12. Ibid. [384]
13. Allen Newell and Herbert A. Simon, “Computer Science as Empirical Inquiry: Symbols
and Search,” Communications of the ACM. Vol. 19, No. 3, pp. 113–126, March 1976.
Available online at
http://www.rci.rutgers.edu/∼cfs/472 html/AI SEARCH/PSS/PSSH1.html. [387]14. For an online interview with Dreyfus about his career, see
http://globetrotter.berkeley.edu/people5/Dreyfus/dreyfus-con0.html. [389]
15. Hubert L. Dreyfus, “Alchemy and Artificial Intelligence,” RAND paper P-3244, The
RAND Corporation, Santa Monica, CA, December 1965. Available online at
http://www.rand.org/pubs/papers/2006/P3244.pdf. [389]16. John Haugeland, Artificial Intelligence: The Very Idea, Cambridge, MA: MIT Press,
1985. [391]17. See, for example, Hubert L. Dreyfus, What Computers Can’t Do: A Critique of
Artificial Reason, New York: Harper & Row, 1972 (second edition, 1979). A revised edition
with the title What Computers Still Can’t Do: A Critique of Artificial Reason was
published by MIT Press, 1992. Hubert L. Dreyfus and Stuart E. Dreyfus, Mind Over
Machine: The Power of Human Intuition and Expertise in the Era of the Computer, New
York: Free Press, 1986. [391]18. Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History andProspects of Artificial Intelligence, San Francisco: W. H. Freeman and Co., 1979. [391]
19. Hubert L. Dreyfus, “Overcoming the Myth of the Mental: How Philosophers Can Profit
from the Phenomenology of Everyday Expertise,” Presidential Address, Proceedings andAddresses of the American Philosophical Association, Vol. 79, No. 2, November 2005.
Available online at http://socrates.berkeley.edu/∼hdreyfus/pdf/Dreyfus%20APA%
20Address%20%2010.22.05%20.pdf. [391]
20. Hubert L. Dreyfus, What Computers Still Can’t Do: A Critique of Artificial Reason, p.
255, Cambridge, MA: MIT Press, 1992. [392]
21. E-mail correspondence of August 9, 2007. For Dreyfus’s comments about Heidegger, see
Hubert L. Dreyfus, Being-in-the-World: A Commentary on Heidegger’s Being and Time,
Division I, Cambridge, MA: MIT Press, 1991. [392]22. Hubert L. Dreyfus, “Why Heideggerian AI Failed and How Fixing It Would Require
Making It More Heideggerian,” (a paper written in connection with being awarded the
APA’s Barwise Prize, 2006), Philosophical Psychology, Vol. 20, No. 2, pp. 247–248, 2007;
reprinted in Michael Wheeler (ed.), The Mechanization of Mind, Cambridge MA: MIT
Press, in press. [392]
23. Rolf Pfeifer, Max Lungarella, and Fumiya Iida, “Self-Organization, Embodiment, andBiologically Inspired Robotics,” Science, Vol. 318, No. 5853, pp. 1088–1093, November 16,
2007. [392]24. For example, a paper written in 2003 claimed that “Google’s architecture features

410
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24.4

NOTES

clusters of more than 15,000 commodity-class PCs with fault-tolerant software.”
Undoubtedly, Google uses many more networked computers today. See Luiz André Barroso,
Jeffrey Dean, and Urs Hölzle, “Web Search for a Planet: The Google Cluster Architecture,”
IEEE Micro, March–April 2003. Available online at
http://labs.google.com/papers/googlecluster-ieee.pdf. [393]25. Joseph Weizenbaum, Computer Power and Human Reason: From Judgment to
Calculation, San Francisco: W. H. Freeman and Co., 1976. [394]26. Ibid, pp. 223–224. [394]27. Ibid, pp. 202–203. [394]28. Ibid, p. 227. [394]29. Lewis Thomas, “Notes of a Biology Watcher: On Artificial Intelligence,” The New
England Journal of Medicine, Vol. 302, No. 9, pp. 506ff, February 28, 1980. [396]30. Theodore Roszak, The Cult of Information: A Neo-Luddite Treatise on High-Tech,
Artificial Intelligence, and the True Art of Thinking, second edition, Berkeley, CA:
University of California Press, 1994. [396]31. Ibid, pp. xviii–xix. [396]32. Ibid, p. xiv. [396]33. Ibid, p. 232. [397]34. Ibid, p. 232. [397]35. Ibid, p. xxiv. [397]36. Ibid, p. 232. [397]
37. All responses are in my files. [398]
38. The online encyclopedia Wikipedia mentions the slogan in its entry for Pierce at
http://en.wikipedia.org/wiki/John R. Pierce. [398]39. For a proof about sliding tile puzzles, see Daniel Ratner and Manfred Warmuth,“Finding a Shortest Solution for the N*N-extension of the 15-puzzle Is Intractable,” Journalof Symbolic Computing, Vol. 10, pp. 111–137, 1990. [402]
40. See http://www.claymath.org/prizeproblems/index.htm. [402]41. Jacob Schwartz, “Limits of Artificial Intelligence,” in Stuart C. Shapiro and David
Eckroth (eds.), Encyclopedia of Artificial Intelligence, Vol. 1, pp. 488–503, New York: John
Wiley and Sons, Inc. 1987. [403]42. W. W. Johnson and W. E. Story, “Notes on the 15 Puzzle,” American Journal of
Mathematics, Vol. 2, pp. 397–404, 1879. I thank Richard Korf for this citation. [405]43. Richard E. Korf and L. A. Taylor, “Finding Optimal Solutions to the Twenty-Four
Puzzle,” Proceedings of the Thirteenth National Conference on Artificial Intelligence andthe Eighth Innovative Applications of Artificial Intelligence Conference, pp. 1202–1207,
Menlo Park, CA: AAAI Press and Cambridge, MA: MIT Press, August 1996. [405]44. David Levy, Robots Unlimited: Life in a Virtual Age, p. 84, Wellesley, MA: A. K.
Peters, Ltd., 2006. [406]45. See the paper by John McCarthy, “Some Expert Systems Need Common Sense,” Heinz
Pagels (ed.), Computer Culture: The Scientific, Intellectual and Social Impact of theComputer, Annals of the New York Academy of Sciences, Vol. 426, November 1995.
Available online at http://www-formal.stanford.edu/jmc/someneed/someneed.html. [407]46. Benjamin Kuipers, “On Representing Commonsense Knowledge, in Nicholas V. Findler(ed.), Associative Networks: The Representation and Use of Knowledge by Computers, pp.
393–408, New York: Academic Press, 1979. Available online at

411
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24

NOTES

ftp://ftp.cs.utexas.edu/pub/qsim/papers/Kuipers-csk-79.ps.gz. [407]47. See Drew McDermott, M. Mitchell Waldrop, B. Chandrasekaran, John McDermott, andRoger Schank, “The Dark Ages of AI: A Panel Discussion at AAAI-84,” AI Magazine, Vol.
6, No. 3, pp. 122–134, Fall 1985. [408]412
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25.1

Chapter 25

Controversies andAlternative Paradigms
Difficulties such as those I have just written about rekindled a number ofcontroversies among AI researchers themselves. Frustrated with AI’s
slowdown, people with different approaches to AI eagerly stepped forward toclaim that what AI needed was more of this or that alternative to AI’s
reigning paradigm – the paradigm John Haugeland called “good-old-fashionedAI” or GOFAI. GOFAI, of course, had as its primary rationale Newell andSimon’s belief that a “physical symbol system has the necessary and sufficientmeans for intelligent action.” But GOFAI seemed to be running out of steam
during the 1980s, making it vulnerable to challenges by AI researchers
themselves – challenges that had to be taken more seriously than those of
Searle, Dreyfus, Penrose, and others outside of the field. In this chapter I’ll
describe some of these internal controversies and mention a few of the newparadigms that emerged.

25.1

About Logic

Among the pursuers of the GOFAI approach were those who used logicalrepresentations and logical reasoning methods – ideas pioneered by John
McCarthy. These people were sometimes called “logicists.” (I was among
them, having co-authored a 1987 book titled The Logical Foundations ofArtificial Intelligence.)1 Drew McDermott, a professor at Yale University (whoreceived his Ph.D. from MIT), was one of those who began to have doubts
about the role of logic in AI. This fact was significant because McDermotthimself had been a prominent logicist, but in an influential 1987 paper heconcluded that the premise that “. . . a lot of reasoning can be analyzed as
deductive or approximately deductive, is erroneous.”2413
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25

Controversies and Alternative Paradigms
He went on to say
Unfortunately, the more you attempt to push the logicist project,
the less deduction you find. What you find instead is that many
inferences which seem so straightforward that they must be
deductions turn out to have nondeductive components.
...
Think of the last time you made a plan, and ask yourself if you
could have proven the plan would work. Chances are you could
easily cite ten plausible circumstances under which the plan would
not work, but you went ahead and adopted it anyway

Several people, logicists (including me) and near-logicists, were invited tosubmit “commentaries,” and these were published along with McDermott’s
article. Have a look if you would like to sample one of the important
controversies in AI. The discussions about the role of logic in artificial
intelligence helped reshape AI’s use of logic, and, in extended form, it stillserves as the primary means for representing declarative knowledge.

25.2

Uncertainty

Another objection to the use of logical representations was based on the factthat logical sentences must be either true or false whereas so much of humanknowledge is uncertain. Both MYCIN and PROSPECTOR (along with someother expert systems) were able to accommodate uncertainty – MYCIN with its“certainty factors” and PROSPECTOR with its use of probability values.
Several other ideas for dealing with uncertainty have been proposed. I’ll
mention two alternatives to the use of probabilities. One is the so-called
Dempster–Shafer (D-S) theory for assigning degrees of belief to statements
and for combining degrees of belief based on independent items of evidence.3
D-S theory has been used extensively in problems where data from several
sources need to be combined (or “fused,” the term used by D-S people) toreach decisions.4
The other alternative to using probabilities is “fuzzy logic,” invented by
the computer scientist Lotfi Zadeh (1921– ; Fig 25.1).5 Fuzzy logic allows
truth values of statements to take on any value between 1 (certainly true) and0 (certainly false). It is based on fuzzy set theory in which set membership cantake on intermediate values between “in the set” and “not in the set.” That is,something can be “partially in the set.” Zadeh uses, as one example, the set oftall people. Depending on one’s definition of tall, John, say, who is 5 feet 10
inches (177.8 cm), might be described as being in the set “tall” to degree 0.7
Then the statement “John is tall” would have a truth value of 0.7. A truth
value of 0.95, for example, might correspond to the statement “John is quite
414
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25.2

Uncertainty

tall.” Modifiers such as “slightly,” “moderately,” and “very” are easily
converted to fuzzy truth values for the statements using them.
Here is how the truth values of combinations of statements are computedin fuzzy logic: If A and B are two statements, then the truth value of the“conjunctive” combination, (A And B), is the smaller of the truth values of A
and of B. The truth value of the “disjunctive” combination, (A Or B), is thelarger of the truth values of A and of B.

Figure 25.1: Lotfi Zadeh. (Photograph courtesy of Lotfi Zadeh.)
Zadeh points out that his truth values and set membership values cannot
be construed as probabilities. His reasons need not concern us here; in any
case, the matter is controversial. (Most statisticians claim that probability
theory is the only mathematically rigorous way to deal with uncertainty.)
Suffice it to say that there is an extensive literature on fuzzy logic and its
several applications, especially in control systems.6
One oft-cited example of the use of fuzzy control is Maytag Company’s“IntelliSense” dishwasher. According to a press account,7415
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25

Controversies and Alternative Paradigms
Maytag Co., Newton, Iowa, has developed what it claims is theworld’s first “intelligent” dishwasher. At the touch of a button, thecomputerized machine figures out the optimum wash cycle for any
load. The dishwasher’s apparent ability to reason stems fromfuzzy-logic control and an advanced sensor module that sits in thepump, measuring food particles, water temperature, detergent, andwash-arm rotation.

In Zadeh’s view, fuzzy logic is one component of a larger effort in what hecalls “soft computing” – a discipline that “. . . differs from conventional (hard)
computing in that, unlike hard computing, it is tolerant of imprecision,
uncertainty and partial truth. In effect, the role model for soft computing is
the human mind.”8Contrasted with these alternatives to probability theory, the invention ofBayesian networks, to be described in a subsequent chapter, has revitalized
methods based on probabilities for representing and reasoning with uncertain
information.

25.3“Kludginess”

Another controversy concerned the very nature of the mechanism (ormechanisms) underlying intelligent behavior. Opposing those who sought someunitary general principle based on search or learning or logic or massive
amounts of common-sense knowledge, Marvin Minsky claimed that intelligence(at least as exhibited by the human brain) was a “kludge.” (Among various
dictionary definitions of “kludge” are the following: 1. A system, especially acomputer system, that is constituted of poorly matched elements or ofelements originally intended for other applications. 2. A clumsy or inelegant
solution to a problem.) Minsky’s view was that intelligence resulted fromperhaps hundreds or thousands of ad hoc, special-purpose mechanisms, looselyinteracting, sometimes cooperating and sometimes competing, to solve the
myriad problems faced by evolving humans. In Minsky’s words,9The brain’s functions simply aren’t based on any small set ofprinciples. Instead, they’re based on hundreds or perhaps eventhousands of them. In other words, I’m saying that each part ofthe brain is what engineers call a kludge – that is, a jury-rigged
solution to a problem, accomplished by adding bits of machinery
wherever needed, without any general, overall plan: the result isthat the human mind – which is what the brain does – should be
regarded as a collection of kludges. The evidence for this isperfectly clear: If you look at the index of any large textbook of416
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25.4

About Behavior
neuroscience, you’ll see that a human brain has many hundreds ofparts – that is, subcomputers – that do different things. Why do
our brains need so many parts? Surely, if our minds were based on
only a few basic principles, we wouldn’t need so much complexity.

Of course, just because the brain is a kludge does not mean thatcomputer intelligences have to be. Nevertheless, some AI researchers favoredsystems consisting of collections of experimentally derived, ad hoc routinesdesigned to solve specific problems. These people called themselves “scruffies”
to distinguish themselves from the “neats” who favored programs based ontheoretically based principles. (These terms were apparently first used byRoger Schank in the 1970s to contrast his approach to building natural
language processing systems with the more theoretically based work of
McCarthy and others.) In his keynote address at the 1981 annual meeting of
the Cognitive Science Society, Robert Abelson compared the two camps by
saying “The primary concern of the neat is that things should be orderly andpredictable while the scruffy seeks the rough-and-tumble of life as itcomes. . . ”10
I believe that both neats and scruffies are needed in a field as immature as
AI is. Scruffies are better at exploring frontiers outside the boundaries ofwell-established theory. Neats help codify newly gained knowledge so that it
can be taught, written about, and thus remembered.

25.4

About Behavior

25.4.1

Behavior-Based Robots

Using an approach that harkens back to Grey Walter’s “tortoises,” the MIT
computer scientist Rodney Brooks eschewed complex representations andreasoning processes and focused instead on what he called a “behavior-based
approach to building robots that operate in the real world.”
Brooks wrote that his approach drew inspiration from attempting to[r]ecapitulate evolution, or an approximation thereof, as a design
methodology, in that improvements in performance come about by
incrementally adding more situation specific circuitry [or softwareorganized like circuitry] while leaving the old circuitry in place, able
to operate when the new circuitry fails to operate (most probably
because the perceptual conditions do not match its preconditions
for operating). Each additional collection of circuitry is referred toas a new layer. Each new layer produces some observable new
behavior in the system interacting with its environment.11

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Controversies and Alternative Paradigms

Figure 25.2: Rodney Brooks (top) and his Crawling Robot, Genghis (bottom).
(Photographs courtesy of Rodney Brooks.)
Genghis, shown in Fig. 25.2, was an early example of one of Brooks’s
robots using layered circuitry. It was a six-legged robot about 35 cm long witha leg span of 25 cm and weighing about a kilogram. It was able to crawl overrough terrain and follow a person using its infrared sensors. (For a short movieof Genghis walking visit
http://groups.csail.mit.edu/lbr/genghis/genghis-short2.mov.) Its sensors
included two front “whiskers,” two inclinometers (to measure pitch and roll),and six forward-looking passive infrared sensors.
The on-board circuitry controlling Genghis was built by adding modules,
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25.4

About Behavior

one on top of another, incrementally. Each layer handled increasingly complex
modes of walking and “subsumed” (overrided when appropriate) the layer
below when the layer below was not able to handle the current situation.
Brooks called this type of layered organization a “subsumption architecture.”
The circuitry consisted of simple computational devices called “augmented
finite-state machines” (implemented by 8-bit microprocessors).12
Unlike earlier symbolic approaches, Brooks’s approach to robotics did notuse central models of the environment and programs to “plan” courses ofaction. He argued that “the symbol system hypothesis upon which classical AI
is based is fundamentally flawed. . . ”13 Instead, Brooks wrote. . . the specific goals of the robot are never explicitly represented[in the behavior-based approach], nor are there any plans – thegoals are implicit in the coupling of actions to perceptual
conditions, and apparent execution of plans unroll in real-time as
one behavior alters the robot’s configuration in the world in such a
way that new perceptual conditions trigger the next step in asequence of actions.14
In his paper “Elephants Don’t Play Chess,”15 Brooks gives examples ofseveral other quite interesting robot systems developed in his MIT lab. The
title of Brooks’s paper is meant to indicate that quite complex behavior (suchas the behavior of elephants for example) can be achieved with systems that(presumably) do not have the representational and reasoning powers requiredfor intelligent activities such as playing chess. Yet, although AI scientists
would certainly be pleased to be able to build machines with the intelligence ofelephants, achieving AI’s ultimate goals would seem to require complex
representational and reasoning methods beyond what the behavior-based
approaches are able to offer.
Although I think that following along the path (or paths) of the evolutionof ever-more capable and intelligent animals has a lot to recommend it, I don’t
think we are very far along in going “from earwigs to humans” (to use the titleof one of Brooks’s articles) – let alone in getting up to elephants.16

25.4.2

Teleo-Reactive Programs

Even though it’s doubtless not the whole story, coordinating behavior withongoing perceptual input is an important part of an intelligent system. It’s apart that I have been interested in ever since the days of working on
intermediate-level actions for Shakey the robot. I was able to return tothinking about behavioral control during a sabbatical year in 1990 and 1991. I
spent part of that year in Brooks’s laboratory at MIT. There, aided by some
important suggestions made by a Stanford (and soon-to-be MIT) student,
Mark Torrance, I developed what I called the “teleo-reactive” (T-R)
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Controversies and Alternative Paradigms

programming language. (“Teleo” comes from the Greek word telos, meaning“end” or “purpose.”) A T-R program is an intermediate-level agent control
program that robustly directs a robot toward a goal in a manner that
continuously takes into account the robot’s perceptions of its dynamicallychanging environment. Perhaps you will tolerate a slight digression into how
T-R programs operate. I use it to illustrate some of the issues that arise incontrolling a purpose-driven robot.
Here’s an example of a T-R program, one that controls a robot kicking asoccer ball. (The program is really very simple; you can try “running it” byhand after I explain how these kinds of programs work.) This program mimics
how a beginning soccer player (say a six-year old) might go about kicking asoccer ball. Not heeding what else might be going on, he or she runs to getclose to the ball, faces it, and then boots it away.
kick(x):
1. Close(x) AND Facing(x) -> foot-swing
2. Close(x) -> face(x)3. Facing(x) -> move-forward
4. True -> moveto(x)face(x):
1. Facing(x) -> do-nothing
2. Left(x) -> rotate-ccw
3. True -> rotate-cw
moveto(x):1. Close(x) -> do-nothing
2. Facing(x) -> move-forward
3. True -> face(x)There are three parts to this program, a main part, namely, kick(x), and two“subprograms,” namely, face(x) and moveto(x). To understand how it
works, I’ll first describe two important sets of components, the “perceptual
routines” and the “primitive action routines.” The perceptual routines
determine whether or not some feature of the robot’s situation is true or false.
The primitive action routines control the basic motor actions of the robot andare presumed to be “built into the robot” (much like “reflexes” are built into
animals).
• Perceptual Routines– Close(x) determines whether the robot is within “kicking distance”
of x, where x can be anything at all. In programs like this, x iscalled a “parameter” or “variable” of the program. When the
program is actually run, x will have a definite value, such as Ball,
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25.4

About Behavior
the soccer ball. But using a parameter in the program instead of adefinite value permits us to use the same program for different“instances” of x.
– Facing(x) determines whether the robot is facing x.
– Left(x) determines whether x is somewhere (anywhere) off to theleft of the direction the robot is facing. When it is true, the robot
should rotate counterclockwise to be facing x.

• Primitive Action Routines– foot-swing is the basic action that moves the robot’s “foot”
forward rapidly. If a ball happens to be in the way, the ball goessailing.
– move-forward makes the robot move in the direction it is facing.
– rotate-ccw makes the robot rotate (in place) in a counterclockwise
direction.
– rotate-cw makes the robot rotate (in place) in a clockwise
direction.
The other action routines, namely, kick(x), face(x), and moveto(x) are
not primitive but are composed of other programs. Note that the numberedlines of the T-R programs shown here consist of a part to the left of an arrow(->) and a part to the right of an arrow. The part to the left is called the“condition part” because it consists of a check to determine whether somecondition is true. The part to the right is called the “action part.”
My first step in explaining how T-R programs work in general is to show
how kick(Ball) works in a specific situation. Let’s assume that the robot isfacing the ball but is not close enough to it to kick it. The robot wants to kickthe ball so it activates the program kick(x) with the parameter x set to Ball.
Here then is the program that the robot activates:kick(Ball):
1. Close(Ball) AND Facing(Ball) -> foot-swing
2. Close(Ball) -> face(Ball)
3. Facing(Ball) -> move-forward
4. True -> moveto(Ball)
Note how every appearance of x in the program is now replaced by Ballbecause it is the ball that is to be kicked. T-R programs are interpreted bylooking at the lines of the program in numeric order and identifying the firstline in the program whose condition part is true. The action part of that lineis then activated. In this specific case, the condition part of line 1 is not true
because the robot is not close to the ball. For the same reason, the condition
part of line 2 is not true either. However, the condition part of line 3 is true,
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25

Controversies and Alternative Paradigms

so the robot activates the associated primitive action move-forward. In themeantime and while the move-forward action is proceeding (this isimportant!), the part of the robot system that is checking to see which is thefirst true line in the program is still checking (in the background, as it were).
Sooner or later (if we assume the robot does not change its “heading” while it
is moving forward), the condition part of line 1 will become true. Precisely atthat time, line 3 is no longer the first line of the program whose condition partis true; line 1 is. So, line 3’s action part is suspended, line 1’s action part isactivated, and the ball is kicked away.
Now, to illustrate the robustness of T-R programs and to explain how
subprograms are activated, let’s assume everything is the same as before(namely, the robot is facing the ball and is far away from the ball) but that,during the time that the robot is moving forward (because move-forward isbeing activated), the robot inadvertently drifts off course so that it is nolonger facing the ball. At the time the robot perceives this change, line 3 ofthe program is no longer the first line whose condition part is true – line 4 is(because its condition, namely, True is assumed always to be true.) So at thattime activation of move-forward ceases, and instead moveto(Ball) is
activated.
To activate moveto(Ball), the program moveto(x) is retrieved from the“program library,” and its parameter, x, is replaced by Ball, and the following
program is activated:
moveto(Ball):
1. Close(Ball) -> do-nothing
2. Facing(Ball) -> move-forward
3. True -> face(Ball)
The first line of this program whose condition part is true is line 3 – resulting
in activating face(Ball), another subprogram. If we assume that the robot’s
drift off its heading resulted in the ball being to its left, activation offace(Ball) will cause the robot to rotate in a counterclockwise direction.
Sooner or later, the robot will be facing the ball again. Now, an interesting
thing happens. The subprogram moveto(Ball), with all of itscondition-checking apparatus, is still running in the background. Its line 2 is
now the first line in the program whose condition part is true (instead of its
line 3 as before). So, the face(Ball) program ceases operation and themove-forward primitive program is activated. If nothing further untoward
happens, line 1 of kick(x) will be the first line in that program whose
condition part is true [moveto(Ball) will be suspended], and foot-swing will
be activated. (Whew! It’s easier for the circuitry that controls all of this tofunction automatically than it is for us to think about it.) If you aren’t
exhausted, you might want to consider some of the other ways that these
programs might be activated.

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25.5

Brain-Style Computation

25.5

Brain-Style Computation

25.5.1

Neural Networks

Because apparently the brain does what it does by massive parallel
computations implemented by networks of interconnected neurons, somepeople began anew to explore the possibilities of neural networks. During thelate 1970s a group at the University of California at San Diego (UCSD)
headed by cognitive psychologists David E. Rumelhart (1942– ) and James L.
McClelland (1948– ) (Fig. 25.3) began a study of networks that they called“parallel distributed processing” (PDP) systems. The group came to be
known as the PDP group.

Figure 25.3: David Rumelhart (left) and James McClelland (right). (Rumelhart
photograph courtesy of Donald Rumelhart. McClelland photograph courtesy ofJames McClelland.)The PDP group held that mental processes in the brain were the result ofinteractions among elementary neural units connected in networks. These units
excite and inhibit each other in parallel. This view of computation is quite atodds with the serial computations performed by most symbol processing
approaches and with Newell and Simon’s physical symbol system hypothesis.
Thus, rather than storing information as lists in localized data structures,
PDP systems distributed information throughout the connections among the423
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25

Controversies and Alternative Paradigms

units. Furthermore, PDP neural networks were not limited to feed-forward,
layered arrangements. Harking back to Rosenblatt’s original view of generalperceptrons, some of the PDP systems allowed what were called “recurrent”
connections – ones that were parts of loops through the various units. As
Rumelhart later pointed out, “The common theme to all these efforts has been
an interest in looking at the brain as a model of a parallel computational
device very different from that of a traditional serial computer.”17
PDP work gained prominence with the publication of two volumes by
McClelland, Rumelhart, and the PDP Research Group.18 An important
chapter in Volume One, titled “Learning Internal Representations by Error
Propagation” by Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams,
introduced a new technique, called “back propagation,” for adjusting network
weights. It led to many new applications, which I’ll describe in a subsequent
chapter.
The physicist John J. Hopfield (1933– ) invented another type of neuralnetwork.19 Each neural element in a Hopfield network is connected to all of theothers. The weights on these connections are symmetrical; that is, the weight
connecting unit i to unit j has the same value as the weight connecting unit j
to unit i. The operation of the network is a dynamical process; that is, thevalues of the units at each time step depend on the values at the just-precedingtime step. The collection of unit values are related to what physicists call an“energy function,” and (regardless of the initial state of the network) thesevalues tend to converge to values that correspond to a locally minimal energy
state. These are called the “stable states” of the network and can be thoughtof as the set of memories stored by the net. Hopfield nets have been used asassociative memories and for some simple computations. (For a demonstrationof a Hopfield net solving a ten-city “traveling salesman” problem, visit
http://to-campos.planetaclix.pt/neural/hope.html.) A “Boltzmann machine”
is an elaboration of the Hopfield net in which unit values at each time step
depend randomly on the unit values at the just-preceding time step.
Much of the neural network research during this period came to be called“connectionist” or “brain-style” computation, to contrast it with GOFAI.
Another person active in this movement was Jerome A. Feldman, who in 1974
moved from Stanford to the University of Rochester to set up the Departmentof Computer Science there as well as to pursue connectionist-oriented
research.20

25.5.2

Dynamical Processes

Some researchers believe that dynamical processes, similar to those exhibitedby Hopfield and Boltzmann networks (and including those described by sets ofdifferential or difference equations), underlie much of the computation
performed by the brain. For example, in an article in The MIT Encyclopediaof Cognitive Science, Tim van Gelder wrote21
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25.6

Simulating Evolution
A dynamical system for current purposes is a set of quantitative
variables changing continually, concurrently, and interdependently
over quantitative time in accordance with dynamical laws
described by some set of equations. Hand in hand with this firstcommitment goes the belief that dynamics provides the right tools
for understanding cognitive processes.
...
A central insight of dynamical systems theory is that behavior canbe understood geometrically, that is, as a matter of position andchange of position in a space of possible overall states of thesystem. The behavior can then be described in terms of attractors,
transients, stability, coupling, bifurcations, chaos, and so forth –features largely invisible from a classical perspective.

However, in the same article van Gelder wrote “Currently, many aspects ofcognition – e.g., story comprehension – are well beyond the reach of dynamicaltreatment.”
The University of Indiana computer scientist Randall Beer (1961– ) is
more optimistic. In an article titled “Dynamical Approaches to Cognitive
Science,” Beer wrote that “dynamical approaches are beginning to engage
substantive empirical questions in cognitive science.”22 He gives three
examples, one of which is a simulated agent whose horizontal motion iscontrolled by a dynamical system implemented by a fourteen-neuron,continuous-time recurrent neural network. The agent’s task is to discriminate
between two differently shaped falling objects – avoiding one shape (bymoving out of its path) and engaging the other (by moving into its path). He
terms this behavior “minimally cognitive,” which he defines as “the simplest
behavior that begins to raise questions of cognitive interest.” To myknowledge, dynamical systems have not yet been used in tasks requiring morethan these minimally cognitive behaviors.
A feature stressed by Beer and others is the importance of the interaction
between the network and its environment. Indeed, the environment itself
provides an important component of most dynamical systems. Exploiting
properties of the environment to make the overall system simpler has been
carried to an extreme by Mark W. Tilden, who did some of his work at theLos Alamos National Laboratory. For example, Tilden’s walking robots don’t
use computers at all but are able to walk by exploiting the resistive input fromtheir motors as they amble over rough terrain.23

25.6

Simulating Evolution

In Chapter 2, I discussed attempts to create intelligent artifacts by using theevolutionary processes of random generation and selective survival. Of these,
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25

Controversies and Alternative Paradigms

John Holland’s genetic algorithms (GAs) seemed to offer the most promise.
GAs attempt to evolve strings of symbols that encode a solution to someparticular problem. Much of the early work in GA used binary-valued symbols(0’s and 1’s), although other symbols can be used also.
The traveling salesman problem is often used to illustrate the use of GAs.
In that problem, we have a list of cities that must be visited, and we must finda tour that starts at one city, visits all of the others just once, and returns tothe starting city. The problem is to find an ordering of the cities thatminimizes the total distance traveled. To encode the solution, the names of
the cities can be used as the symbols. If, for example, there are fourteen cities
named by the letters A, B,. . . , N, and if we must start and end at city C, thenthe symbol string (C, F, N, K, B, L, M, H, D, A, E, G, I, J, C) would
represent a tour that starts with C, visits F next, and so on. In keeping withevolutionary terminology, the total distance traversed by this tour is related to
the fitness of this string. We want shorter tours to have greater fitness, so let
us set the fitness of a tour to minus its distance traveled. The GA processattempts to evolve a a string having maximal fitness.
The evolutionary process starts by assembling a large population of
random strings. In our traveling-salesman-problem example, they would all
start and end with C but have all of the other names just once in each string.
Populations of these strings are subjected to two different processes –analogous to some of what happens in biological evolution. First, some of thestrings undergo random mutations in which the values of some of theircomponents are changed. An example of a mutation of a traveling-salesman
string might be to interchange two symbols selected randomly within thestring.
Second, pairs of strings within a population that have relatively highfitness are selected to participate in an operation called “cross-over,” which
generates an “offspring” string. Different kinds of cross-over operations areused in GAs. For the traveling-salesman example, the operation must preserve
the “legality” of the offspring string; that is, it must correspond to a tour that
visits the other cities just once. One way to do this is to repeat in theoffspring string the first k symbols of one of the parents and then scan thesymbols in the other parent to fill out the offspring string with symbols notalready appearing there. The value of k is selected randomly.
The illustration in Fig. 25.4 shows how this style of cross-over operates.
At each stage of the evolutionary process, the current generation ofstrings gives rise to a new generation. The new generation contains some ofthe strings from the old one (preferring strings with high fitness), the mutatedstrings, and the new strings resulting from cross-over operations. Interestingly,
succeeding generations eventually contain strings that are better and better atsolving the problem at hand (for some kinds of problems).
GAs have been applied to various combinatorial optimization problems in
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25.6

Simulating Evolution

Figure 25.4: A cross-over operation.
computer science, engineering, economics, chemistry, and other fields. The
online encyclopedia Wikipedia has some excellent material on GAs, including
pointers to tutorials, at http://en.wikipedia.org/wiki/Genetic algorithm.24
We can think of a genetic algorithm as a search process attempting tolocate high points in a “fitness landscape.” Each possible GA string can bethought of as a “place” in a contour map, with the fitness of that string being
the elevation at that place. Initially, “paratroopers” are dropped randomly
over the landscape, and these report their elevations. Some of them moveslight distances from their current positions (corresponding to the mutations),
and some pairs rocket to a position somewhere in between their current
positions (corresponding to cross-over). Then the process repeats. The fitness
landscape may have several peaks, with some higher than others, and it may
have several plateaus. After several generations, a GA process may succeed
only in finding the location of minor peaks, or it may have difficulty getting off
a large plateau. But occasionally it may find the highest peak in thelandscape. AI has used what mathematicians call hill-climbing (or gradientascent) procedures, but before GAs these techniques usually involved only one“climber.” GAs, along with other evolutionary algorithms, allow several
climbers to search simultaneously, resulting in what is called “parallel search.”
One of John Holland’s students, John Koza (1943– ; Fig. 25.5), developed
a somewhat different evolutionary procedure called Genetic Programming(GP). GP evolves LISP programs rather than strings. The process starts witha random collection of programs containing some basic LISP functions andconstants thought to be important for solving the task at hand. Again,
random mutations and cross-over are used to produce new generations ofprograms. (Later versions of GP have added biologically inspired operations
analogous to inversion, gene duplication, and gene deletion.) Various
techniques can be used for the mutation part, including replacing parts of a427
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25

Controversies and Alternative Paradigms

program with randomly selected new program components. In cross-over, two“parent programs” with relatively high fitness are selected. Randomly selectedparts of each program are then interchanged to produce two new programs forthe next generation of programs.

Figure 25.5: John Koza. (Photograph courtesy of John Koza.)
Koza has employed GP to produce programs that have created new kindsof electrical filters, optical lenses, antennas, and control circuitry, among otherthings. Many of these programs are, as he says, “competitive with humanperformance.” He claims that because the main goal of AI is to produce
programs that are capable of intelligent behavior, one should use a program
synthesis technique able to produce such programs directly and that GP is (sofar) the best such synthesis technique. As he puts it, “Virtually all problems
in artificial intelligence, machine learning, adaptive systems, and automatedlearning can be recast as a search for a computer program. Genetic
programming provides a way to successfully conduct the search for a computer
program in the space of computer programs.”25
Since 1999, Koza has been using a 1,000-Pentium “Beowulf-Style Cluster
Computer” for his GP work.26 He and co-authors have written several books
and papers on GP.27
There is a Special Interest Group for Genetic and Evolutionary
Computation (SIGEVO) of the Association for Computing Machinery (ACM).
It sponsors conferences dealing with various aspects of evolutionary
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25.7

Scaling Back AI’s Goals

computation including GAs and GPs.28 A compelling narrated video
demonstration of the power of simulated evolution, presented at the 1991
SIGGRAPH conference, can be viewed at
http://www.archive.org/details/sims evolved virtual creatures 1994. As
described by the Web site, “A population of several hundred creatures iscreated within a supercomputer, and each creature is tested for [its] ability toperform a given task, such [as] the ability to swim in a simulated water
environment.”29

25.7

Scaling Back AI’s Goals

During the AI winter, many AI researchers began to focus on more modest
and achievable goals than on those of previous years. One heard fewer bravepredictions about what AI could ultimately achieve. Increasingly, effort wasdevoted to what AI could (at the time) actually achieve. The result was morework on limited or “weak” AI and less on “strong AI.” The emphasis was on
using AI to help humans rather than to replace them.
Companies and government agencies with funds to support researchlooked to computer technologies generally (rather than to AI specifically) tohelp solve their problems. Research funds were directed at improving databasesystems, user interfaces, graphics, computer networks, data mining, computer
games, information retrieval, computer vision, and word-processing andspreadsheet programs, to name just a few areas. The AI technologies of searchand inference, expert systems, speech recognition, and natural language
processing were used, when appropriate, as components of large, integrated
systems. AI researchers began to be satisfied with adding bits of intelligence
here and there to these systems to make them more useful and appealing.

Notes
1. Michael Genesereth and Nils Nilsson, Logical Foundations of Artificial Intelligence, San
Francisco: Morgan Kaufmann Publishers, 1987. [413]
2. Drew McDermott, “A Critique of Pure Reason,” Computational Intelligence, Vol. 3, No.
3, pp. 151–160, August 1987. [413]3. For a brief online summary with citations to longer articles, see Glenn Shafer,“Dempster–Shafer Theory,” 2002; available at
http://www.glennshafer.com/assets/downloads/articles/article48.pdf. [414]4. See, for example, David L. Hall and Sonya A. H. McMullen, Mathematical Techniques inMultisensor Data Fusion, Norwood, MA: Artech House, Inc., 2004. [414]5. Zadeh’s original article is Lotfi Zadeh, “Fuzzy Sets,” Information and Control, Vol. 8,
pp. 338–353, 1965; available online at http://www-bisc.cs.berkeley.edu/Zadeh-1965.pdf. Fora summary, see E. Cox, “Fuzzy Fundamentals,” IEEE Spectrum, Vol. 29, No. 10, pp. 58–61,
1992. The Association for the Advancement of Artificial Intelligence (AAAI) maintains a

429
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25

NOTES

fuzzy logic Web page (with lots of pointers to tutorials, papers, and applications) at
http://www.aaai.org/AITopics/html/fuzzy.html. [414]6. See, for example, Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Menlo Park,
CA: Addison Wesley Longman, 1998. (The book is no longer in print but can be
downloaded from http://www.ece.osu.edu/∼passino/FCbook.pdf.) [415]7. See Machine Design, March 1995. [415]8. See The Berkeley Initiative in Soft Computing Web site at
http://www-bisc.cs.berkeley.edu/. [416]9. Marvin Minsky, “Smart Machines,” Chapter 8 of John Brockman, The Third Culture:
Beyond The Scientific Revolution, New York: Simon & Schuster, 1995. Available online at
http://edge.org/documents/ThirdCulture/p-Ch.8.html. The whole book is available at
http://www.edge.org/documents/ThirdCulture/d-Contents.html. [416]
10. The quotation is taken from Wendy G. Lehnert, “Cognition, Computers, and Car
Bombs: How Yale Prepared Me for the 1990s,” in Roger Schank and Ellen Langer (eds.),
Beliefs, Reasoning, and Decision Making: Psycho-Logic in Honor of Bob Abelson, pp.
143–173, Hillsdale, NJ: Lawrence Erlbaum Associates, 1994. [417]
11. Rodney A. Brooks, “From Earwigs to Humans,” Proceedings IIAS The Third Brain and
Mind International Symposium on Concept Formation, Thinking and Their Development,
pp. 59–66, Kyoto, Japan, May 1996. Available online at
http://people.csail.mit.edu/brooks/papers/ascona.pdf. [417]12. For a description of how Genghis works, see Rodney A. Brooks, “A Robot That Walks:
Emergent Behavior from a Carefully Evolved Network,” Neural Computation, Vol. 1, No. 2,
pp. 253–262, Summer 1989. Also in Proceedings of the IEEE International Conference on
Robotics and Automation, pp. 292–296, Scottsdale, AZ, May 1989. Available online as an
MIT AI Lab Memo (No. 1091) at http://people.csail.mit.edu/brooks/papers/AIM-1091.pdf.
[419]
13. Rodney A. Brooks, “Elephants Don’t Play Chess,” Robotics and Autonomous Systems,
Vol. 6, pp. 3–15, 1990. Also in Pattie Maes (ed), Designing Autonomous Agents: Theory
and Practice from Biology to Engineering and Back, pp. 3–15, Cambridge, MA: MIT Press,
1990. Available online at http://people.csail.mit.edu/brooks/papers/elephants.pdf. [419]
14. Rodney A. Brooks, op. cit. [419]
15. Rodney A. Brooks, op. cit. [419]
16. See Brooks’s Web pages for pointers to others of his publications:
http://people.csail.mit.edu/brooks/. [419]
17. David E. Rumelhart, “Brain Style Computation: Learning and Generalization,” inSteven E Zornetzer, Joel L. Davis, and Clifford Lau (eds.), An Introduction to Neural andElectronic Networks, San Diego: Academic Press, 1990. [424]18. James L. McClelland, David E. Rumelhart, and the PDP Research Group, Parallel
Distributed Processing, Explorations in the Microstructure of Cognition, Volume 1:
Foundations, Cambridge, MA: MIT Press, 1986, and James L. McClelland, David E.
Rumelhart, and the PDP Research Group, Parallel Distributed Processing, Explorations in
the Microstructure of Cognition, Vol. 2: Psychological and Biological Models, Cambridge,
MA: MIT Press, 1986. [424]19. John J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective
Computational Abilities,” Proceedings of the National Academy of Science, Vol. 79, No. 8,
pp. 2554–2558, 1982. Available online fromhttp://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=346238. [424]
20. See, for example, Jerome A. Feldman et al., “Computing with Structured Connectionist
Networks,” Communications of the ACM, Vol. 31, No. 2, pp. 170–187, February 1988. [424]430
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25.7

NOTES

21. T. J. van Gelder, “Dynamic Approaches to Cognition” in R. Wilson and F. Keil (eds.),
The MIT Encyclopedia of Cognitive Sciences, pp. 244–246, Cambridge MA: MIT Press,
1999. Available online at http://sites.google.com/site/timvangelder/publications-1/
dynamic-approaches-to-cognition/MITDyn.pdf?attredirects=0. [424]
22. Randall D. Beer,“Dynamical Approaches to Cognitive Science,” Trends in Cognitive
Sciences, Vol. 4, No. 3, March 2000; available online at
http://mypage.iu.edu/∼rdbeer/Papers/TICS.pdf. For a longer paper, see Randall D. Beer,“A Dynamical Systems Perspective on Agent-Environment Interaction,” Artificial
Intelligence, Special Volume on Computational Research on Interaction and Agency, Part 1,
Vol. 72, Nos. 1–2, pp. 173–215, 1995; available online at
http://mypage.iu.edu/∼rdbeer/Papers/AIJ95.pdf. Beer’s Web pages(http://mypage.iu.edu/∼rdbeer/) provide many additional citations. [425]23. For more about these kinds of simple robots, visit the Wikipedia site
http://en.wikipedia.org/wiki/BEAM robotics. [425]24. See also Melanie Mitchell, An Introduction to Genetic Algorithms, Cambridge, MA:
MIT Press, 1996. [427]
25. The quotation is from Koza’s homepage at
http://www.genetic-programming.com/johnkoza.html. [428]26. See Forest H. Bennett et al., “Building a Parallel Computer System for $18,000 That
Performs a Half Peta-flop per Day,” in Wolfgang Banzhaf et al. (eds.), GECCO-99:
Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1484–1490, San
Francisco, CA: Morgan Kaufmann Publishers, 1999. [428]27. See, for example, John R. Koza, Genetic Programming: On the Programming ofComputers by Means of Natural Selection, Cambridge, MA: MIT Press, 1992; John R. Koza,
Genetic Programming II: Automatic Discovery of Reusable Programs, Cambridge, MA: MIT
Press, 1994; John R. Koza, Forrest H Bennett III, David Andre, and Martin A. Keane,
Genetic Programming III: Darwinian Invention and Problem Solving, San Francisco:
Morgan Kaufmann Publishers, 1999; and John R. Koza, Martin A. Keane, Matthew J.
Streeter, William Mydlowec, Jessen Yu, and Guido Lanza, Genetic Programming IV:
Routine Human-Competitive Machine Intelligence, Norwell, MA: Kluwer Academic
Publishers, 2003. [428]28. Visit the SIGEVO Web site at http://www.sigevo.org/index.html. [429]29. I thank Mykel Kochenderfer for telling me about this video. [429]431
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25

NOTES

432
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25.7

Part VII

The Growing
Armamentarium: From the
1980s Onward

Throughout the 1980s, while AI was enjoying increased popularity andcommercial successes and then suffering funding cuts and a wintry season, its
basic research workers produced a significant number of powerful newtechnical tools and sharpened others. New results unfolded in all of its
subfields, including reasoning and representation, machine learning, natural
language processing, and computer vision. This work, technically andmathematically deeper than before and strengthened by new connections withstatistics and control engineering, helped vitiate some of the criticisms hurled
at earlier AI systems and greatly enhanced AI’s abilities.

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25

434
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26.1

Chapter 26

Reasoning andRepresentation
26.1

Nonmonotonic or Defeasible Reasoning

Those AI researchers called logicists, who favor the use of logical languages forrepresenting knowledge and the use of logical methods for reasoning,
acknowledge one problem with ordinary logic; namely, it is monotonic. By
that they mean that the set of logical conclusions that can be drawn from a
set of logical statements does not decrease as more statements are added tothe set. If one could prove a statement from a given knowledge base, one could
still prove that same statement (with the very same proof!) when more
knowledge is added.
Yet, much human reasoning does not seem to work that way – a fact well
noticed (and celebrated) by AI’s critics. Often, we jump to a conclusion using
the facts we happen to have, together with reasonable assumptions, and thenhave to retract that conclusion when we learn some new fact that contradicts
the assumptions. That style of reasoning is called nonmonotonic or defeasible(meaning “capable of being made or declared null and void”) because newfacts might require taking back something concluded before.
One can even find examples of nonmonotonic reasoning in children’s
stories. In That’s Good! That’s Bad!, by Margery Cuyler,1 a little boy floats
high into the sky holding on to a balloon his parents bought him at the zoo.
“Wow! Oh, that’s good,” the story goes. The balloon breaks on a branch of atall, prickly tree. “Pop! Oh, that’s bad,” the story continues. The boy falls
into a muddy river and climbs up onto a hippopotamus and rides to shore.
“Oh, that’s good.” The story goes on like that – changing back and forthabout whether the balloon ride is turning out “good” or “bad.”435
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26

Reasoning and Representation

There were already some methods used in AI (and elsewhere in computerscience) for defeasible reasoning. For example, in the problem-solving language
PLANNER proposed by Carl Hewitt, if a goal, say G, could not be achieved by aprogram then Not G could be asserted (under the assumption that G was astatement that the program was trying to establish). Such reasoning was
defeasible because if additional statements were later added to the program orto its knowledge base, then establishing G might become possible. Similarly, in
the PROLOG programming language, if a statement could not be proved by aprogram, then it was inferred to be false. Inferring that something is false if itcannot be proved true is called “negation as failure.”
The SRI planning system, STRIPS, was also a type of nonmonotonic
reasoning system. Assumptions about things “staying the same” after actions
were performed were certainly just that – assumptions. Conclusions drawn
after making such assumptions might be defeated after adding new
information whose implications might negate those assumptions.
Another method for defeasible reasoning was being used in the databaseworld. Databases are used for encoding a wide variety of information. For
example, a company might have a database about its employees. One canquery such a database to find out an employee’s salary, the department he or
she works in, and so on. Suppose we attempt to find out from one of these
employee databases information about a person, say Jack Smith, whose nameis not found in the database. We might reasonably conclude then that Mr.
Smith is not an employee of that company, and that’s what some databasesystems would do. That conclusion would be based on the assumption thatthe database names all of that company’s employees – an instance of theso-called closed-world assumption (CWA). Of course, Jack Smith may later
join the company, and then his name would be added to the database. At thattime we would have to take back the conclusion that Jack Smith is not one of
the company’s employees; this is another example of defeasible reasoning.
You may recall that, way back in 1964, Bertram Raphael’s
question-answering system, SIR, included a style of defeasible reasoning hecalled the “exception principle.” In SIR, general information about all theelements of a set applied to particular elements – but only in the absence ofmore specific information about those particular elements. Several AI
knowledge representation schemes represent some of their knowledge in“taxonomic hierarchies,” somewhat like the one Raphael used, and use theexception principle, which is now often called “cancellation of inheritance,” fordefeasible reasoning.
In Fig. 26.1, I show a taxonomic hierarchy of some office machines. A
program using this hierarchy would conclude that the energy source of a laser
printer, for example, is a wall outlet because that property is inherited fromthe general class “office machines.” However, more specific information about
the energy source for robots would force the conclusion that the energy source
for R2D2, for example, is a battery, overriding the inheritance of properties of436
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26.1

Nonmonotonic or Defeasible Reasoning

Figure 26.1: A hierarchy of office machines. (From Nils J. Nilsson, Artificial
Intelligence: A New Synthesis, p. 311, San Francisco: Morgan Kaufmann Publishers, 1998.)
the general class of office machines.
During the 1980s some of the most creative AI researchers became
fascinated with the problem of defeasible reasoning and made several new
proposals for how to do it. Their proposals were accompanied by a good dealof theoretical analyses comparing and contrasting the different approaches andhow some of them could be considered either as specializations or as
generalizations of the others.
The Canadian AI researcher Raymond Reiter (1939–2002) proposed oneof the new methods.2 In its simplest form, it uses special inference rules that
permit drawing a conclusion from a knowledge base if some specified conditionis satisfied and if that conclusion is not contradicted by what could ordinarily
be deduced from that knowledge base. Reiter’s special inference rules arecalled default rules, and his system that uses them is called default logic. As
an example of its use, suppose we have a knowledge base used by a robot thatspecifies which rooms in an office building may be entered by the robot. We
might have a rule that says that for rooms on the second floor, if it is
impossible to prove that the robot may not enter a room there, then one canconclude that the robot may enter that room. Again, the reasoning is
437
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26

Reasoning and Representation

defeasible, because someone may later add to the knowledge base a fact that
permits the deduction that some room on the second floor may not be entered.
John McCarthy, the originator of the proposal that knowledge should beencoded as logical statements, was also worried about the problem of
nonmonotonicity. To deal with the problem, he proposed a method calledcircumscription.3 Circumscription is rather difficult to explain without using a
lot of logical jargon. In principle, a version of it called “predicate
circumscription” (just one of several versions of circumscription4 ) involves
limiting (thus “circumscribing”) the set of entities that make predicates true
to just those that can be proved to be true. For example, if we have aknowledge base that contains statements such as Tall(John) andTall(Frank), plus a lot of other facts, we can circumscribe (if we wish) the
predicate Tall. Doing so allows us to conclude ¬Tall(Susy) if Tall(Susy) is
not logically implied by the knowledge base.
One of the motivations for McCarthy’s interest in nonmonotonic
reasoning was the possibility that it would be a key to solving the frame
problem (see p. 222). Recall that the frame problem concerns the difficulty of
how to represent which things change and which things stay the same when an
action is taken (say by a robot). One approach is to make the assumption that
if a predicate describing some state of the world is not mentioned by adescription of an action (including the action’s preconditions and effects) thenthat predicate is not changed by the action. This assumption is nonmonotonic
because later (or more detailed) information may imply that a nonmentioned
predicate is indeed changed. Some early attempts to solve the frame problem
using nonmonotonic reasoning ran into various technical difficulties (which are
too technical to bother about here), but work continued. A recent paper
claimed that “the Frame Problem as it was originally formulated has beensolved with Shanahan’s and Thielscher’s approaches and that at least thelogical chapter of the Frame Problem has been closed.” (The two people
mentioned are Murray Shanahan of Imperial College, London, and MichaelThielscher of the Dresden University of Technology.)5
It might not have escaped your attention that the many proposals fornonmonotonic reasoning are rather similar, but there are many subtle
technical differences. There are even other proposals that I have notmentioned, including auto-epistemic logics, nonmonotonic logics, abductive
reasoning, truth-maintenance systems, and methods based on probability
theory.6 This profusion of defeasible reasoning methods can probably be
attributed to the creativity and mathematical sophistication of many of the AI
researchers involved and their keen abilities to spot and to attempt to escape
the limitations of each others’ proposals.

438
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26.2

26.2

Qualitative Reasoning

Qualitative Reasoning

Many of you have probably taken courses in physics, either in high school or incollege or in both. The job of physics is to build theories of the physical world,
and these theories are usually formulated using mathematics. For example, theformula F = M a, relates the force, F , acting on an object to the object’s
mass, M , and its acceleration, a. Many mathematical formulas describing
physical processes are more complex. For example, the following “waveequation” can be used to calculate the velocity of a water wave:
s
v=



h
gλ
tanh 2π .
2πλEngineers could use it, for example, to predict when the crest of a wave would
pass by a certain point.
We humans are also able to predict, with useful accuracies, the future
course of many of the physical processes we commonly experience. For
example, when people play in ocean waves at the beach, they are usually able
to predict when a wave crest will arrive so that they can jump up in time. Do
our brains use anything like the equation just shown to make that prediction?
Probably not. Instead, prediction routines for guiding skilled actions arelearned by repeated experiences and are part of what psychologists call“procedural knowledge.”
In addition to acting automatically and effectively using proceduralknowledge embedded in our various motor skills, we can also make declarative
statements predicting what will happen in certain situations. For example, asurfer looking out at incoming waves can tell a friend “Take the next wave; it’sgoing to be a big one.”7 Apparently we have some facility for representing andusing “qualitative knowledge” about physical processes – knowledge that isneither part of our procedural “muscle memory” nor represented in our brains
by complex mathematical formulas. I could give several examples. How do weknow that when we knock over a glass of water on a table the water will
eventually come to the edge of the table and spill off? How do we know that if
we stack heavy boxes on top of light, fragile boxes, the fragile boxes mightcollapse? How do we know that if we drive to our destination a little bit
faster, we’ll get there a little bit sooner?
Several AI researchers have worked on systems having the ability torepresent and to reason with qualitative knowledge. Scott Fahlman (1948– )wrote such a program for his master’s thesis while a student at MIT. Called
BUILD, the program was able to plan how to stack toy blocks by taking into
account various forces acting on the blocks, such as gravity and friction.8 It
did this in a more-or-less qualitative way rather than by using exact
mathematical models. Thus, BUILD can be said to be one of the first AI
attempts to do qualitative reasoning about physics.
439
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Reasoning and Representation

Soon after, another MIT student, Johan de Kleer (1951– ), wrote a
program called NEWTON for his master’s thesis that included a component
able to do qualitative reasoning. NEWTON, de Kleer claimed, “understands
and solves problems in a mechanics mini-world of objects moving on
surfaces.”9 NEWTON used its qualitative knowledge about physics to produce
approximate problem solutions, which it then used to plan and carry out
subsequent quantitative calculations. Most physics teachers will tell you that
qualitative reasoning about “the physics” of a problem is essential before
plunging into the mathematics.
In doing qualitative reasoning, NEWTON used a process called“envisioning” for “generating a progression of scenes encoded in a symbolic
description which describe what could happen.” It used descriptions of six
basic actions appropriate to the kinds of problems NEWTON could solve. Oneof these descriptions, for example, was for FLY. It encoded the knowledge that“[i]f the object is moving on top of a surface which is concave away from the
motion, the object might fly off.” de Kleer’s main contribution was to show
how qualitative calculations and quantitative reasoning can be combined in acomputer program.
In 1979 Pat Hayes published “The Naive Physics Manifesto.”10 A revised
version appeared in 1985.11 He proposed that the artificial intelligence
research community begin “the construction of a formalization of a sizable
portion of common-sense knowledge about the everyday physical world: about
objects, shape, space, movement, substances (solids and liquids), time, etc.”
These topics had long presented particularly difficult representational andreasoning challenges for AI. Encoding our everyday knowledge about thesesubjects so that computers can reason about them is at the heart ofqualitative physics, which Hayes called “naive physics.”
His manifestos presented some general ideas about how to represent“clusters” of common-sense knowledge about the physical world. As one
example, he proposed the notion of “histories” for representing events, insteadof states and functions of states as he and John McCarthy had earlier
advocated. He defined a history as “a piece of spacetime with natural
boundaries, both temporal and spatial.” For example, “the event of putting
four blocks together in a square is the beginning of the history of a platform,
and the end of that history is when and where they are separated from one
another.”
Hayes said, in effect, that we should not be “too hasty” about writing
naive physics programs – preferring instead to delay implementations until
more foundational work had been done on the representations themselves. He
had already sketched out some of this work on liquids.12
These initial explorations in qualitative reasoning soon led to a rapidly
growing subfield of AI with many applications, especially in diagnosing faults
based on qualitative models of equipment. (I’ll mention one example,

440
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26.3

Semantic Networks

diagnosing faults in spacecraft equipment, in Part 8.) Prominent groups wereformed by Professor Kenneth D. Forbus (1955– ) at Northwestern University13
and by Benjamin Kuipers (1949– ) at the University of Texas at Austin.
(Kuipers has now relocated to the University of Michigan).14 Special issues ofjournals and edited volumes and books devoted to the subject haveappeared.15
I’ll conclude this chapter on Reasoning and Representation by turning
next to new developments in the use of semantic networks for knowledge
representation.

26.3

Semantic Networks

In my earlier discussion of defeasible reasoning, I showed a semantic network
representing a taxonomic hierarchy of office machines. Taxonomic networks
are widely used in AI and in computer science to represent what are called“ontologies.” In AI, an ontology consists of a set of concepts and relationships
among those concepts. (In philosophy, it means the study of being or
existence.) AI systems for reasoning with these networks would commonly
have mechanisms for property inheritance using exception principles.
Although we understand taxonomic networks best by thinking about
them in the form of trees, a collection of special data structures is used when
encoding them for computers. These structures are often called “frames,”
following Minsky’s original use of the word. For example, one of the frames forthe office machines network might be represented as in Fig. 26.2.
Typically there would be a frame for each class of individuals or entities
in a taxonomy as well as for each of the entities themselves. Frames for classes
would name the superclass to which it belonged and the subclasses belonging
to it. It would also specify properties of the entities belonging to the class. It
is also common for a frame to have “meta-information,” such as the date theframe was created.

26.3.1

Description Logics

Earlier in AI’s history there was controversy about whether knowledge should
be represented by data structures such as semantic networks (encoded, say, as
frames) or by sets of logical statements. Gradually the controversy moderated
because researchers came to accept the idea that semantic networks could bethought of as a special way of representing certain kinds of logical statements,
thereby permitting some deductions to be made directly from the network.
Two of the researchers who helped to establish this view were Ronald J.
Brachman (1949– ) and Hector J. Levesque (1951– ) (Fig. 26.3). (Each ofthem has also done related foundational work in knowledge representation and441
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26

Reasoning and Representation

Figure 26.2: A frame. (Adapted from Nils J. Nilsson, Artificial Intelligence: A
New Synthesis, p. 313, San Francisco: Morgan Kaufmann Publishers, 1998.)
reasoning generally.16 )Figure 26.3: Ronald Brachman (left) and Hector Levesque (right). (Courtesyof Ronald Brachman and of Hector Levesque.)Brachman did his Ph.D. work17 at Harvard under Bill Woods. (Besides
his work in natural language processing, Woods had also written about the442
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26.3

Semantic Networks

relationship between semantic networks and logic.18 ) Expanding on the ideas
in his thesis, Brachman, along with other colleagues at BBN (including
Woods) and at USC-ISI, developed a frame-based knowledge representation
system called KL-ONE,19 which became the basis for what came to be called
description logics.
Levesque did all of his college work (B.S., M.S., and Ph.D.) at the
University of Toronto. After receiving his Ph.D. degree in 1981, he joined
Brachman at the Fairchild Laboratory for Artificial Intelligence Research in
Palo Alto, a group founded by Peter Hart after Hart left SRI. There,
Brachman and Levesque, together with Richard Fikes (then at Xerox PARC),
developed the KRYPTON representation and reasoning system.20 KRYPTON
was a hybrid system – meaning it represented knowledge both by logicalformulas and by a semantic network.
Although semantic networks make it easy to reason about individuals andtheir properties in a hierarchy, it is difficult for them to represent statements
containing negations and disjunctions. As the KRYPTON paper states, “. . . astatement such as ‘either Elsie or Bessie is the cow standing in Farmer Jones’s
field’ cannot be made in a typical assertional frame system.” KRYPTON’s
solution is to use a combination of both types of representations:. . . we have split the [reasoning] operations into two separate kinds,
yielding two main components for our representation system: aterminological [that is, network-based] one, or T Box, and an
assertional [that is, logic-based] one, or A Box. The T Box allows
us to establish taxonomies of structured terms and answer
questions about analytical relationships among these terms; the A
Box allows us to build descriptive theories of domains of interestand to answer questions about those domains.21
The diagram from the KRYPTON paper shown in Fig. 26.4 illustrates the
structure of the system. As the authors wrote, it consists of “a T Box ofstructured terms organized taxonomically, an A Box of (roughly) first-ordersentences whose predicates come from the T Box, and a symbol table
maintaining the names of the T Box terms so that a user can refer to them.”
The T Box in the diagram represents (among other things) that a child is aperson. The A Box states that there exists a doctor who has a child.
KRYPTON was the forerunner of several description-logic systems,
including CLASSIC, developed by Brachman and colleagues after he moved to
AT&T Bell Laboratories.22 In addition to their use in AI reasoning systems,
description logics are used in ontology languages for the semantic Web, forexample DAML-ONT23 and OWL.24

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Reasoning and Representation

Figure 26.4: Parts of a KRYPTON T Box and A Box. (Adapted from Ronald J.
Brachman, Richard E. Fikes, and Hector J. Levesque, “KRYPTON: A Functional
Approach to Knowledge Representation, IEEE Computer, Vol. 16, No. 10, p.
71, October 1983.)

26.3.2

WordNetWordNet is a large “conceptual” dictionary of English words, organizedsomewhat like a semantic network and inspired by psycholinguistic andcomputational theories of human lexical memory.25 Its development was
begun at Princeton University in the 1980s under the direction of Professor
George A. Miller (the same George Miller whom I mentioned earlier and who
wrote “The Magical Number Seven, Plus or Minus Two”). In a 1990 paper,
Miller and his colleagues had this to say about the beginnings of the project:26
In 1985 a group of psychologists and linguists at Princeton
University undertook to develop a lexical database along lines
suggested by [earlier psycholinguistic] investigations. The initialidea was to provide an aid to use in searching dictionaries
conceptually, rather than merely alphabetically – it was to be usedin close conjunction with an on-line dictionary of the conventional444
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26.3

Semantic Networks
type. As the work proceeded, however, it demanded a more
ambitious formulation of its own principles and goals. WordNet isthe result. Inasmuch as it instantiates hypotheses based on resultsof psycholinguistic research, WordNet can be said to be adictionary based on psycholinguistic principles.

WordNet groups its words into collections called “synsets.” Each synset
contains a group of synonymous words, that is, words with more-or-less thesame meaning. I’ll use WordNet’s online search facility (athttp://wordnetweb.princeton.edu/perl/webwn) to provide some examples ofsynsets and relations among them. The word “computer,” for example, is in
two different synsets. One of these synsets contains, besides “computer,” thesynonyms “computing machine,” “computing device,” “data processor,”“electronic computer,” and “information processing system.” The other synset
contains the synonyms for the older use of the word “computer” (when it
referred to humans doing the computing), namely, “calculator,” “reckoner,”“figurer,” and “estimator.” A synset may also be accompanied by a shortdefinition, called a “gloss,” which provides a meaning for the words in thesynset. For the first synset, the gloss is “a machine for performing calculations
automatically.” For the second, the gloss is “an expert at calculation (or at
operating calculating machines).” Sometimes, the gloss also contains an
example sentence to illustrate typical usage.
Synsets are connected to other synsets using relations similar to thoseused in semantic networks. One such relation is called a “hypernym,”
corresponding (roughly) to “is a kind of.” For example, the hypernym of oursynset containing “computer” and “computing device,” etc. is the synset
containing the word “machine” (and possibly other words too) having thegloss “any mechanical or electrical device that transmits or modifies energy toperform or assist in the performance of human tasks.” A “hyponym,”
corresponding (roughly) to “is a general case of,” is the opposite of a
hypernym. The “computer” synset just mentioned has several hyponyms,
among them are ones containing the words “digital computer” and any of its
synonyms (a computer that represents information by numerical digits),“number cruncher” and any of its synonyms (a computer capable ofperforming a large number of mathematical operations per second), “Turingmachine” and any of its synonyms (a hypothetical computer with an infinitely
long memory tape), and others.
There are other relations also. For synsets containing nouns there is arelation called a “meronym,” corresponding to “has as parts.” The synset
containing “computer” and “computing device,” etc. has several meronyms,
among them are ones containing the words “chip” and its synonyms (all withthe gloss “electronic equipment consisting of a small crystal of a silicon
semiconductor fabricated to carry out a number of electronic functions in an
integrated circuit”), “monitor” and its synonyms (all with the gloss “display
produced by a device that takes signals and displays them on a television
445
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26

Reasoning and Representation

screen or a computer monitor”), and several others. A “holonym” is the
opposite of a meronym.
Each synset also mentions the part of speech of the words it contains:noun, verb, adjective, or adverb. The relations among synsets differ somewhat
depending on the part of speech. For example, verb synsets have a relation
called “entailment.” For example, one of the synsets for the verb “walk” (useone’s feet to advance; advance by steps) entails the synset containing the verb
“step” (shift or move by taking a step).27
According to its Web site (as of this writing), WordNet contains 155,287
words and 117,659 synsets. It is being maintained and expanded at Princeton
and is freely and publicly available for download. Besides its use as an online
dictionary and thesaurus, it is being used to support automatic text analysis,
in natural language processing applications, as a knowledge base for question
answering, and in semantic Web applications. Similar “wordnets” have beencreated in dozens of other languages.
WordNet’s use as an ontology in a taxonomic knowledge base depends on
the hypernym/hyponym relationships among the noun synsets and on
WordNet’s use of an inheritance mechanism to infer properties of objects
represented by synsets from the properties of their ancestors. For example, one
chain (from specific to general) in such a hierarchy is the following:
workstation → digitalcomputer → computer → machine→ device → instrumentality → artifact → . . .
Of course, there are side branches along this chain (which you can explore
using WordNet’s online search facility).
Some modifications may be needed when using WordNet as an ontology,
however, because, according to Wikipedia, “. . . it contains hundreds of basic
semantic inconsistencies such as (i) the existence of common specializations for
exclusive categories and (ii) redundancies in the specialization hierarchy,”
among other things.28
In a related effort, Karin Kipper Schuler has created “VerbNet.”29
According to a Web page about it,30 VerbNet “is the largest on-line verb
lexicon currently available for English. It is a hierarchicaldomain-independent, broad-coverage verb lexicon with mappings to otherlexical resources such as WordNet, Xtag, and FrameNet.”

26.3.3Cyc

In 1984, realizing that a large amount of common-sense knowledge would beneeded for many AI applications, especially for natural language
understanding, Stanford professor Douglas Lenat (1950– ), who had previously
done work on automating the discovery of mathematical concepts and446
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26.3

Semantic Networks

heuristics,31 decided to undertake the immense task of providing computerswith common-sense knowledge. (See Fig. 26.5.) The first step, he thought,
would be to “prime the pump with the millions of everyday terms, concepts,
facts, and rules of thumb” that comprise common sense. As he later described
it, the project began this way:32
In the fall of 1984, Admiral Bobby Ray Inman convinced me that if
I was serious about taking that first step, I needed to leaveacademia and come to his newly formed MCC (Microelectronics
and Computer Consortium) in Austin, Texas, and assemble a teamto do it. The idea was that over the next decade dozens ofindividuals would create a program, Cyc, with common sense. We
would “prime the knowledge pump” by handcrafting andspoon-feeding Cyc with a couple of million important facts andrules of thumb.
The name “Cyc” (pronounced like “psych”) comes from three letters in
the middle of the word “encyclopedia.” The idea was that if Cyc had enoughknowledge to understand articles in an encyclopedia, it would be able to read
all kinds of material and acquire additional knowledge on its own – a “secondstep” toward smart computers. (The “third step” would be to conceive of andperform experiments to gain more knowledge, that is, knowledge beyond what
humans already know.)
To understand encyclopedia articles, humans must already know quite a
bit about the world. As Lenat put it:33
If we take any sentence from an encyclopedia article and think
about what the writer assumes the reader already knows about theworld, we will have something worth telling Cyc. Alternatively, we
can take a paragraph and look at the “leaps” from one sentence tothe next and think about what the writer assumes the reader willinfer “between” the sentences. [Consider, for example, thesentences] “Napoleon died on St. Helena. Wellington was greatly
saddened.” The author expects the reader to infer that Wellington
heard about Napoleon’s death, that Wellington outlived Napoleon,and so on.
Just how much knowledge would Cyc need to have to understand articles
in an encyclopedia? Lenat recently told me that he originally thought Cyc
would need “a couple million general assertions, such as ‘mammals have hair’(plus a vastly larger number of specific facts, such as what the capital of
California is).” Now he believes that “the number is more like 200 million.”34
Lenat and his team of programmers and “knowledge enterers” worked
away on Cyc, entering knowledge by hand, for about ten years at MCC.
447
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Reasoning and Representation

Figure 26.5: Douglas Lenat (top) and the structure of the Cyc knowledge base(bottom). (Photograph courtesy of Douglas Lenat.)
Several reports, a number of papers, and a book were written describing the
project and its goals.35 In 1994, partly because of difficulties at MCC, Lenat
founded Cycorp in Austin, Texas , “to research, develop, and commercialize
Artificial Intelligence.” Work on Cyc continues there under Lenat and his staff.
The language Cyc uses for representing knowledge is called CycL, an
extension of the first-order predicate calculus. The object classes that Cyc
knows about are arranged in a taxonomic hierarchy (such as the one shown in
Fig. 26.5), which permits object classes to inherit the properties of object
classes higher in the hierarchy. In Cyc’s hierarchy, for example, an “eventclass” such as “turning on a light switch” is a subclass of a “temporal-thing,”
which is a subclass of an “individual,” which is a subclass of the most general
448
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26.3

Semantic Networks

class in Cyc, namely, a “thing.” Cyc uses “rules” (stated in its logical language)
to describe relationships among objects. For example, an English version of
one of its rules is “For all events A and B, A causes B implies A precedes B.”
Cyc’s knowledge base (KB) is divided into thousands of “micro-theories” –collections of concepts and facts about some circumscribed area. For example,
one micro-theory contains knowledge about European geography. Others are
devoted to expert knowledge about “chemistry, biology, military organizations,diseases, and weapon systems.” Each micro-theory is consistent, although theentire Cyc knowledge base, taken as a whole, might have contradictions. Cyc’s
KB contains over five-million general assertions.36 Most of these capturecommon-sense knowledge about “the objects and events of everyday human
life, such as buying and selling, kinship relations, household appliances, eating,office buildings, vehicles, time, and space.” In addition, the KB contains
grammatical and lexical knowledge needed for natural language processing.
Cyc uses an “inference engine” to conclude new facts from other existing
facts and rules in its KB. Two main inference methods are used. One is theinference rule called resolution, which I mentioned in Section 11.1. To reason
efficiently with resolution, Cyc has developed some proprietary heuristics andrestricts the scope of its search processes by its use of micro-theories. The
other inference method is property inheritance, as is commonly used insemantic network representations. “Cyc also over 1,000 special-purpose
inferencing modules for handling specific classes of inference. One such module
handles reasoning concerning collection membership/disjointness. Others
handle equality reasoning, temporal reasoning, and mathematical reasoning. . .
CycL uses a form of circumscription. . . and can make use of the closed world
assumption where appropriate.”37

Cycorp is working on several applications, including intelligent search andinformation retrieval from the World Wide Web and natural language
understanding. Its Web site claims that it “is now a working technology withapplications to many real-world business problems.”
Yet, there are several criticisms of Cyc. It gets stumped on some reasoning
problems that humans find easy. Its vast knowledge base makes some of its
reasoning impractically slow (and it will undoubtedly get even slower as more
knowledge is added). It does not have satisfactory solutions for certain
representation problems that AI researchers are still struggling to solve – suchas how to represent substances. Also, because most of the work on Cyc is donein a private setting, it is not generally available for peer evaluation.
Although Cyc has pretensions of having enough knowledge to understandnatural language, it cannot yet automatically (that is, without interaction by
the user) adequately translate typical English questions into CycL. To query
Cyc, one must either use the cumbersome CycL language or work in aninteractive fashion. Lenat described to me a working example of suchinteraction employed at the Cleveland Clinic where medical researchers use

449
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26

NOTES

Cyc to get information from patient databases.38 The researcher types in his or
her query in English. “Cyc parses what it can, recognizing some portions of thequery, and presents the user with a set of partial query ‘fragments’ that arelike fill-in-the-blank templates.” Cyc then uses its specialized knowledge about
medicine plus its common-sense knowledge to figure out how to paraphrase thequery for the user to check. On getting user agreement about what the user
asked, it uses its knowledge about how the database is organized to generate
database queries to retrieve the desired information.
There are two versions of Cyc available for download. One is called
ResearchCyc and is available to the research community (for research-only
purposes) under a ResearchCyc license.39 Besides the Cyc Inference Engine, it
contains “nearly 3,000,000 assertions (facts and rules), using 26,000+
relations, that interrelate, constrain, and, in effect, (partially) define theconcepts.” Another, called OpenCyc, is a publicly available open source versionof the Cyc technology.40 It contains “hundreds of thousands of terms, alongwith millions of assertions relating the terms to each other. . . .” One can also
examine the concept hierarchy in OpenCyc using an online browser.41
No one knows exactly how humans organize and use their common-sense(and expert) knowledge. Whether the facts and relations already amassed(and yet to be gathered) by the Cyc project will be adequate in amount andorganization to permit human-level reasoning has yet to be demonstrated.
Yet, I applaud the effort and wish the project well. Certainly, I thinksomething at least as ambitious as Cyc will be required. (Another attempt togather common-sense knowledge is that of the “Commonsense Computing
Initiative” at the MIT Media Lab. The work there is described at
http://xnet.media.mit.edu/.)It’s possible that Cyc might get to the point where (with some humanhelp) it will be able to gather more of the required knowledge directly from theInternet. Lenat mentions42 a game called “FACTory,” designed to help gather
knowledge from humans who play the game. You can play it at
http://game.cyc.com/game.html. In the game, Cyc generates natural language
statements it has gathered from English sentences it has found on the Web. It
presents these statements to ten randomly chosen players of the game. If
enough of them answer that the statement is “true,” Cyc adds that fact to its
KB (and the players get points in the game). I tried the game, and Cyc asked
me whether or not “All spaghetti marinara includes some garlic.” I answered“true,” and Cyc said that I agreed with 66% of the other players and that it
now (therefore) believes the sentence is “true.”Notes
1. Margery Cuyler, That’s Good! That’s Bad!, New York: Henry Holt and Co., 1991. [435]450
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26.3

NOTES

2. Raymond Reiter, “A Logic for Default Reasoning,” Artificial Intelligence, Vol. 13, pp.
81–132, 1980. [437]
3. John McCarthy, “Circumscription – A Form of Non-monotonic Reasoning,” Artificial
Intelligence, Vol. 13, pp. 27–39, 1980. There are several papers about circumscription by
Vladimir Lifschitz, for example, “On the Satisfiability of Circumscription,” Artificial
Intelligence, Volume 28, No. 1, pp. 17–27, 1986. [438]4. See, for example, John McCarthy, “Applications of Circumscription to Formalizing
Common Sense Knowledge,” Artificial Intelligence, Vol. 28, No. 1, pp. 89–116, 1986. [438]5. For people who are interested in the history of the problem, the major players, and thetechnical details of its alleged solution, see M. Kamermans and Tijn Schmits, “The Historyof the Frame Problem,” available online fromhttp://student.science.uva.nl/∼tschmits/Bachelorproject/index.html, 2004. (The paper has
a nice chart summarizing this history, which is available separately at
http://student.science.uva.nl/∼tschmits/Bachelorproject/poster HotFP.PNG. See also
Murray Shanahan, Solving the Frame Problem: A Mathematical Investigation of theCommon Sense Law of Inertia, Cambridge, MA: MIT Press, 1997. [438]6. See, for example, papers in collections by Matt Ginsberg (ed.), Readings inNonmonotonic Reasoning, Los Altos, CA: Morgan Kauffman Publishers, 1987, and D.
Gabbay, C. Hogger, and J. Robinson (eds.), Handbook of Logic in Artificial Intelligence andLogic Programming, Vol. 3, Oxford and New York: Oxford University Press, 1994. [438]7. I found the wave equation on a Web site belonging to a manufacturer of surfing
products: http://www.waveequation.com/wave equation data.html. [439]8. Scott E. Fahlman, “A Planning System for Robot Construction Tasks,” Artificial
Intelligence, Vol. 5, No. 1, pp. 1–49, 1974. The thesis is available online as an MIT AI
Laboratory Technical Report No. 283 with the same title and dated May 1973 at
http://dspace.mit.edu/bitstream/handle/1721.1/6918/AITR-283.pdf?sequence=2. [439]
9. Johan de Kleer, “Qualitative and Quantitative Knowledge in Classical Mechanics,”
Artificial Intelligence Laboratory, Technical Report 352, December 1975. Available online at
http://dspace.mit.edu/bitstream/handle/1721.1/6912/AITR-352.pdf?sequence=2. (Some of
his ideas were described in his master’s thesis proposal. See
http://www2.parc.com/spl/members/dekleer/Publications/
QualitativeandQuantitativeKnowledgeinClassicalMechanics.pdf.) [440]
10. Patrick J. Hayes,“The Naive Physics Manifesto,” in D. Michie (ed.), Expert Systems in
the Micro-Electronic Age, pp. 242–270, Edinburgh: Edinburgh University Press, 1979. [440]
11. Patrick J. Hayes, “The Second Naive Physics Manifesto,” in Jerry R. Hobbs and Robert
C. Moore (eds.), Formal Theories of the Commonsense World, pp. 1–36, Norwood, NJ:
Ablex Publishing Corporation, 1985. [440]12. Patrick J. Hayes, “Naive Physics 1: Ontology for Liquids,” in Jerry R. Hobbs andRobert C. Moore (eds.), Formal Theories of the Commonsense World, pp. 71–107,
Norwood, NJ: Ablex Publishing Corporation, 1985, An early version appeared as Memo 35of the Institut pour les Études Semantiques et Cognitives, Université de Genève, 1978. [440]
13. The group’s Web page is at http://www.qrg.northwestern.edu/. [441]
14. The Web page for the Texas group is at http://www.cs.utexas.edu/∼qr/. [441]15. See, for example, Artificial Intelligence, Vol. 51, Nos. 1–3, October 1991; IEEE Expert:
Intelligent Systems and Their Applications, Vol. 12, No. 3, May/June 1997; theintroductory article by Yumi Iwasaki, “Real World Applications of Qualitative Reasoning:
Introduction to the Special Issue,” AI Magazine, Vol. 24, No. 4, pp. 16–21, Winter 2003 (apreprint of which is available online at
http://ksl-web.stanford.edu/people/iwasaki/my-intro.ps); Benjamin J. Kuipers, Qualitative
Reasoning: Modeling and Simulation with Incomplete Knowledge, Cambridge, MA: MIT
Press, 1994; and Daniel S. Weld and Johan de Kleer, Readings in Qualitative Reasoning

451
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26

NOTES

about Physical Systems, San Francisco: Morgan Kaufmann Publishers, 1990. [441]16. See Ronald J. Brachman and Hector J. Levesque, Knowledge Representation andReasoning, San Francisco: Morgan Kaufmann Publishers, 2004. [442]17. Ronald J. Brachman, “A Structural Paradigm for Representing Knowledge,” Ph.D.
dissertation, Division of Engineering and Applied Physics, Harvard University, Cambridge,
MA, 1977. [442]18. William A. Woods, “What’s in a Link: Foundations for Semantic Networks,” in Daniel
Bobrow and Allan Collins (eds.), Representation and Understanding: Studies in Cognitive
Science, pp. 35–82, New York: Academic Press, 1975. [443]
19. Ronald J. Brachman and James G. Schmolze, “An Overview of the KL-ONE Knowledge
Representation System,” Cognitive Science: A Multidisciplinary Journal, Vol. 9, No. 2, pp.
171–216, 1985. [443]
20. Ronald J. Brachman, Richard E. Fikes, and Hector J. Levesque, “KRYPTON: A
Functional Approach to Knowledge Representation, IEEE Computer, Vol. 16, No. 10, pp.
67–73, October 1983. Reprinted in Ronald J. Brachman and Hector J. Levesque (eds.),
Readings in Knowledge Representation, pp. 411–429, San Francisco: Morgan Kaufmann
Publishers, 1985. [443]21. Ibid, pp. 68–69. [443]22. For a Web site with much information and resources about description logics, see
http://dl.kr.org/. [443]
23. Deborah L. McGuinness, Richard Fikes, Lynn Andrea Stein, and James Hendler,“DAML-ONT: An Ontology Language for the Semantic Web,” in Dieter Fensel, Jim
Hendler, Henry Lieberman, and Wolfgang Wahlster (eds.), The Semantic Web: Why, What,and How, Cambridge, MA: MIT Press, 2002; available online at
http://www.ksl.stanford.edu/people/dlm/papers/daml-ont-semantic-web.htm. [443]24. Deborah L. McGuinness and Frank van Harmelen, “OWL Web Ontology Language
Overview,” W3C Recommendation, February 10, 2004; available online at
http://www.w3.org/TR/owl-features/. [443]
25. Christine Fellbaum (ed.), WordNet: An Electronic Lexical Database, Cambridge, MA:
MIT Press, 1998. See also the WordNet Web site at http://wordnet.princeton.edu/ and the
Wikipedia article at http://en.wikipedia.org/wiki/WordNet. [444]26. George A. Miller et al., “Introduction to WordNet: An On-line Lexical Database,”
International Journal of Lexicography, Vol. 3, No. 4, pp. 235–244, 1990. [444]27. The reader interested in details about WordNet might refer to the WordNet Web site
and to the set of five papers appearing in International Journal of Lexicography, Vol. 3, No.
4, 1990. [446]28. http://en.wikipedia.org/wiki/WordNet. [446]29. Karin Kipper Schuler, “VerbNet: A Broad-Coverage, Comprehensive Verb Lexicon,”
University of Pennsylvania Ph.D. dissertation, 2005. Online version available fromhttp://repository.upenn.edu/dissertations/AAI3179808/. [446]30. http://verbs.colorado.edu/∼mpalmer/projects/verbnet.html. [446]31. Douglas B. Lenat, “AM: Discovery in Mathematics as Heuristic Search,” in Randall
Davis and Douglas B. Lenat (eds.), Knowledge-Based Systems in Artificial Intelligence, pp.
1–225, New York: McGraw-Hill, 1982; Douglas B. Lenat, “Eurisko: A Program Which
Learns New Heuristics and Domain Concepts,” Artificial Intelligence, Vol. 21, Nos. 1–2,
61–98, 1983. [447]32. From an article by Lenat in David G. Stork (ed.), Hal’s Legacy: 2001’s Computer as
Dream and Reality, Cambridge, MA: MIT Press, 1998. The article is available online at

452
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26.3

NOTES

http://www.cyc.com/cyc/technology/halslegacy.html. [447]33. Ibid. [447]34. E-mail communication, January 24, 2008. [447]35. The main paper is Douglas B. Lenat et al., “Cyc: Toward Programs with Common
Sense,” Communications of the ACM, Vol. 33, No. 8, pp. 30–49, August 1990. The first five
years of the Cyc project is described in Douglas B. Lenat and R. V. Guha, Building Large
Knowledge-Based Systems, Reading, MA: Addison-Wesley, 1990. [448]36. January 13, 2009, e-mail from Doug Lenat. [449]37. The reader who is interested in more details about how Cyc represents knowledge andhow Cyc reasons can view a set of tutorial slides available online at
http://www.opencyc.org/releases/doc/tut/index html?tree-e=
eJyLLWTUCOVxhAJnIwPbQiYE38Ux2baQOVUPALHZCUs#AAAAAAAADAc=. [449]38. E-mail communication, January 25, 2008. [450]
39. See http://researchcyc.cyc.com/. [450]
40. See http://opencyc.org/. [450]41. http://www.cycfoundation.org/concepts. [450]42. E-mail communication, January 24, 2008. [450]453
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26

NOTES

454
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27.1

Chapter 27

Other Approaches toReasoning andRepresentation
27.1

Solving Constraint Satisfaction Problems

In addition to reasoning methods based on logic or semantic networks, several
other techniques have been explored. In this section, I’ll describe a class ofproblems called constraint satisfaction problems (or assignment problems) andmethods for solving them. In these problems, we have a set of objects thatmust be assigned values that satisfy a set of constraints. We have already seen
one example of an assignment problem – that of assigning labels to lines in animage. In that problem, the constraint is that each line in the image can be
assigned one and only one label.
Constraints can be expressed in the form of database relations, logicalformulas, equations, or inequalities. Thus, constraint satisfaction problems
arise naturally in many settings including scheduling, simulation, computer
vision, and robotics. (A spreadsheet is a simple constraint satisfaction system,
for example.) Fortunately, there are some general-purpose solution methods
for these problems that are independent of the application. I’ll illustrate one
such method with a small example.
Consider the problem of placing four queens on a 4 × 4 chessboard in sucha way that no queen can capture any other. In the Four-Queens problem, we
have four objects, c1 , c2 , c3 , and c4 , representing the columns 1 through 4,
respectively, in which a queen might be placed. Each of these objects can haveone of four values, 1, 2, 3, or 4, corresponding to the row numbers. So, forexample, when c3 has value 2, a queen is placed in the second row of the third
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27

Other Approaches to Reasoning and Representation

column. The Four-Queens problem constrains the values of these variables.
For example, if c1 has value 1, c2 cannot have value 1 or 2; c3 cannot havevalue 1 or 3; and c4 cannot have value 1 or 4. Constraints are represented as agraph called a constraint graph. Each node in this graph is labeled by anobject name together with a set of all of the values for that object. A pair ofnodes is connected by an arc (an edge that has a direction) if the possible
values of the object at the tail of the arc are constrained by any of the valuesof the object at the head of the arc. I show an example of such a graph for the
Four-Queens problem in Fig. 27.1. In this problem, each object constrains allof the others, so all of the nodes have arcs to all of the other nodes. (To makethis figure less cluttered, I represent two different arcs by a single line witharrow heads at each end.)
We start by assigning a value to one of the objects. This assignment is a“trial” value and the beginning of a search process. If it does not work out,
we’ll have to backtrack and try another value. Suppose we begin by assigningvalue 2 to object c1 (corresponding to placing a queen in column 1, row 2).
Now we iteratively examine all of the arcs in Fig. 27.1 and eliminate any valueof an object at the tail of an arc that is inconsistent (according to theconstraints) with all of the values at the head of the arc. This process, called
constraint propagation, halts when no more values can be eliminated. The
first few steps of the process might be as follows:
1. First, look at the arc from c2 to c1 : We can eliminate c2 = 1, c2 = 2, andc2 = 3 because each of those values is inconsistent with the values (there
being only one) of c1 .
2. Next, look at the arc from c3 to c1 : We can eliminate c3 = 2 and c3 = 4.
3. Next, look at the arc from c4 to c1 : We can eliminate c4 = 2.
Eliminating some of the values, as we just did, now renders even more values
susceptible to elimination. Revisiting the arcs to check again for consistency
will reveal which ones. Value elimination can be said to “propagate” over theconstraint graph. Continuing the propagation process eliminates all but one
value of a variable for each node. At this point, all of the arcs are consistentand no more values can be eliminated. The graph shown in Fig. 27.2 shows
how the process might go, starting with the values remaining after performing
the three steps listed. In this case, constraint propagation has solved the
problem (given that we started with c1 = 2, a lucky guess). The placement of
the four queens is shown in Fig. 27.3.
This process for dealing with constraint satisfaction problems is based on
AC-3 (short for Arc Consistency Algorithm No. 3), an algorithm proposed byAlan K. Mackworth (1945– ; Fig. 27.4), a professor at the University of
British Columbia.1 Mackworth has continued work on constraint problems
and their applications in robotics and agent control. (He also proposed andbuilt the first soccer-playing robots.)
456
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27.1

Solving Constraint Satisfaction ProblemscFigure 27.1: A constraint graph for the Four-Queens problem. (From Nils J.
Nilsson, Artificial Intelligence: A New Synthesis, p. 185, San Francisco: Morgan
Kaufmann Publishers, 1998.)
Various extensions and improvements to AC-3 have been proposed. These
are well described in a book by Rina Dechter2 (who has made substantial
contributions to the field herself) and in Chapter Five of the text by Russell
and Norvig.3 Vipin Kumar’s article surveys the entire field.4 Commercial
companies, such as ILOG (being acquired by IBM), routinely use constraint
programming languages for applications involving scheduling and simulation.
The Four-Queens example I used to illustrate constraint propagation
happened to find a solution without searching (because I started it with theselection of c1 = 2). But, if I had selected c1 = 1 initially instead, constraint
propagation would have eliminated all of the values in all of the nodes –457
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Other Approaches to Reasoning and Representation

Figure 27.2: A constraint graph illustrating constraint propagation. (From Nils
J. Nilsson, Artificial Intelligence: A New Synthesis, p. 187, San Francisco:
Morgan Kaufmann Publishers, 1998.)
indicating that there is no solution to the Four-Queens problem with a queen
in column 1, row 1. (You are invited to check that out.) Making that
selection, and finding out that there is then no solution, would have required ahigher level search process to backtrack to try another value. Also, it ispossible that a trial selection followed by constraint propagation would have

458
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27.2

Solving Constraint Satisfaction Problems

Figure 27.3: A solution to the Four-Queens problem.

Figure 27.4: Alan Mackworth. (Photograph courtesy of Alan Mackworth.)left unresolved the values of some of the objects. In that case, a selection
would have to be made for a value of one of these objects followed by moreconstraint propagation, possible backtracking, and so on. Thus solving
constraint satisfaction problems typically requires search, and several
backtracking procedures have been proposed and used.

459
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Other Approaches to Reasoning and Representation

27.2

Solving Problems Using Propositional
Logic

An important special case of logical knowledge representation and reasoning is
the case in which none of the logical formulas contains variables. Although
this case could not have formulas such as (∀x)[Man(x) ⊃ Mortal(x)], it could
have formulas such as [Man(Socrates) ⊃ Mortal(Socrates)] and[Man(Plato) ⊃ Mortal(Plato)] and so on. Because there are no variables, thisspecial case is essentially the same as propositional logic. That’s because
expressions such as Man(Socrates) and Mortal(Socrates), whenever theyoccur in the knowledge base, could be replaced by propositions, such as P014
and Q234, which have no internal structure and are thus completely unrelated.
The disadvantage of limiting ourselves to propositional logic is that we would
have to have a possibly very large number of formulas to cover all of theentities that we want to talk about – instead of using just single formulas withvariables covering them all. The compensating advantage however is that
extremely potent methods have been developed for reasoning with very large
numbers of propositional formulas.
I’ll illustrate how these methods work using a simple logical puzzle.
Suppose that among the invitees to a dinner party are three rather
troublesome individuals, Ann, Bill, and Charlie. A friend who is aware of thesocial dynamics among these people informs the hostess that at least one ofthese guests will definitely attend, but that if Ann attends, Bill will not, and if
Bill attends, Charlie will not, and if Charlie attends, Ann will not. Based onthat information, can the hostess figure out who might attend?
If she were a logician, she could convert her friend’s information into thefollowing set of formulas in propositional logic (where A stands for “Ann iscoming,” and so on):A ∨ B ∨ C,
¬A ∨ ¬B,¬B ∨ ¬C,¬C ∨ ¬A.
Recall from my previous use of logical formulas that “¬” stands for “not” andthat “∨” stands for “or.” Formulas like these that consist of propositions (ortheir negations) connected by “or” signs are called “clauses.” The individual
propositions themselves are called “variables” because their truth values are
yet to be assigned.
To solve her problem, our hostess must figure out how to assign truth
values (T or F) to the three propositions A, B, and C such that all of the clauses
have value T (because they come from statements presumed to be true). If aclause has value T, a logician would say that it is “satisfied.” For example, if A

460
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27.2

Solving Problems Using Propositional Logic

has value T, meaning Ann is coming, the first clause would be satisfied (nomatter what the values of B and C).
Logicians and computer scientists have figured out ways to tackle theproblem of whether or not there is an assignment of truth values to thevariables in a set of clauses such that all of the clauses are satisfied and what
those values might be. The difficulty is that the problem of determining
satisfiability, called the “SAT problem,” is NP-complete, which implies that, inthe worst case, the time taken by all known algorithms for solving SAT
problems grows exponentially with the size of the problem.
Of course the problem our hostess faces is not a large problem, and shewould have no difficulty solving it simply by trying out the (only) eight
possible ways of assigning truth values to A, B, and C to discover which of theseeight (if any) satisfies all of her clauses. But many computational problems
encoded as sets of clauses might involve hundreds of thousands of clauses
containing thousands of variables. Such problems would be intractable for a
trial-and-error method. Fortunately, more efficient methods have beendeveloped that are able to solve very large problems indeed. In fact, Bart
Selman, one of the inventors of some of these more efficient methods, says“. . . current solvers can solve instances with one million or more variables andseveral million clauses.” Furthermore, he claims that this is not “just a resultof faster hardware. . . it’s really 95% the result of better algorithms. We’re stilldealing with an NP-complete problem and an exponential search space. So,
hardware improvements without algorithmic ideas don’t have too much
impact.”5There are two main types of methods for solving SAT problems. One
class consists of what are called systematic methods, and the other classconsists of what are called local search methods. In fact, some of the best
solvers use techniques from both of these two methods. I’ll describe the basic
ideas in the next section.

27.2.1

Systematic Methods

Most of the systematic methods are based on a procedure called the DPLL
algorithm and its various enhancements.6 (The DPLL algorithm is derived
from an earlier algorithm, the DP algorithm, proposed by Martin Davis andHilary Putnam.7 ) The DPLL algorithm works by searching a tree of thepossible ways to assign truth values to variables. At each node of the searchtree a variable is assigned a value of T along one branch and a value of F along
another branch. These assignments convert the set of clauses at a node to newsets at the two successor nodes by the following simplification process:1. In each clause replace the variable just assigned by either a T or an F
depending on the branch taken.

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Other Approaches to Reasoning and Representation
2. Eliminate those clauses that contain a T or a ¬F. (These clauses are
already satisfied by this assignment.)
3. Eliminate any ¬T’s or F’s from any clauses in which they appear. (Forthe set of clauses to be satisfiable, at least one of the remaining variablesin these clauses must have value T.)
4. For any clause that contains just a single variable, set that variable tothe value that will satisfy that clause and continue to simplify if possible.
DPLL terminates when either one or the other of the following conditions

occurs:i. If the set of clauses arrived at is empty, DPLL finishes, having determinedthat the original set of clauses is satisfiable and that the truth values
that have been assigned so far satisfy these clauses.
ii. If any of the clauses arrived at along a branch of the tree is empty (thatis, there are no more variables left to try to satisfy it), then DPLL has
determined that the original set of clauses is unsatisfiable by the truth
values that have been assigned so far along that branch. In that casesearch continues along another branch of the tree if there are still
variables with unassigned truth values. If not, DPLL finishes having
determined that the original set of clauses is not satisfiable.
As an example, let’s look at the tree that would be associated with my“who-is-coming-to-dinner” problem. I show in Fig. 27.5 part of the search treethat would be produced by assigning truth values (in the order A, B, and C)
and simplifying.
One interesting thing to note from this example is that, depending on how
the search is ordered, DPLL can (and usually does) terminate before all of the
branches of the search tree have been explored. Chances for rapid termination
are improved by performing a depth-first (rather than a breadth-first) search.
DPLL achieves its high efficiency and speed by using what computer scientists
call a “recursive backtracking search.” Further improvements to DPLL haveresulted in much faster and powerful global methods for solving SAT
problems. These improvements involve making backtracking more“intelligent,” by using what are called “clause-learning” mechanisms, andtaking advantage of some strategies used by the local search methods.8 A Web
site for one of these programs, called zChaff, claims “We have success stories ofusing zChaff to solve problems with more than one million variables and 10
million clauses. (Of course, it can’t solve every such problem!)”9462
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27.2

Solving Problems Using Propositional Logic

Figure 27.5: A DPLL search tree.

27.2.2

Local Search Methods

Local search methods work by performing a hill-climbing search, making asequence of one-at-a-time modifications to a set of randomly chosen initial
truth values for all of the clauses. For SAT problems, each possible set of truth
values corresponds to a location in a landscape, and the number of clauses
satisfied (for that set of truth values) corresponds to the height or elevation of463
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Other Approaches to Reasoning and Representation

the corresponding location. A highest location in the landscape (of which there
may be more than one) corresponds to the maximum number of clauses thatcan be satisfied (which would be all of them if the set of clauses is satisfiable).
In 1992, Bart Selman (1959– ; Fig. 27.6), Hector Levesque, and David
Mitchell (1957– ; Fig. 27.6) introduced a method for attacking SAT problems
called GSAT.10 (The “G” stands for greedy, and we’ll see why in a moment.)
GSAT and its various extensions, such as WALKSAT, have been applied
successfully to problems with as many as 200,000 variables. GSAT conducts a
local hill-climbing search over the landscape of truth values.

Figure 27.6: Bart Selman (left) and David G. Mitchell (right). (Photographs
courtesy of Bart Selman and of David Mitchell.)In outline form, here is how it works. It starts with a random assignmentof truth values and evaluates how many clauses this assignment satisfies. If it
satisfies all of them, the process terminates with a solution. Otherwise, it flips
the truth value of each of the propositions one at a time in turn. It selectsthat flip that results in the largest (“greediest”) increase in the number ofclauses satisfied, and local search continues from the new set of truth values(with the flipped value).
It is often the case that no single flip can increase the number of clauses
satisfied. Even so, there are usually flips that at least maintain this number.
In that case, GSAT selects one of them (randomly) and takes the corresponding
step on the “plateau” that it has reached, hoping that it can later resume its
climb uphill. Or it might be that all possible steps taken in the landscapewould be downhill. (One paper11 describing these local techniques states thatsuch a result “almost never occur[s].”) In that rare case, GSAT has certainly
traversed as high as it can go and has reached a “local maximum.” In some
applications, an assignment of truth values that does not satisfy all of theclauses might be useful and acceptable, but if it is not, GSAT can be“restarted” with a different set of random truth assignments with the hope464
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27.2

Solving Problems Using Propositional Logic

that a greater local maximum might be obtained in the new traverse. In any
case, GSAT places limits on the number of flips that it tries so that it does notwander endlessly on a plateau. Because the SAT problem in general is
NP-complete, it is possible to find problems for which the local methods (orany methods) would take an exponential amount of time, but the GSAT
authors claim that such problems “appear to be extremely rare, and do notoccur naturally in the applications we have examined.”12Here is how GSAT might work on our “who-is-coming-to-dinner” problem,
whose clauses are repeated here:A ∨ B ∨ C,
¬A ∨ ¬B,¬B ∨ ¬C,¬C ∨ ¬A.
It selects a random set of truth values, say T for A, T for B, and T for C. Thisset satisfies only one clause, namely, A ∨ B ∨ C. If GSAT were to flip any one of
the truth values (from T to F), three clauses would be satisfied – all big steps“uphill.” Suppose GSAT decides to flip the value of A, resulting in the the first,
the second, and the last clause being satisfied. Flipping either the value of B or
C results in all four clauses being satisfied – each a step uphill to a solution.
Suppose it decides to flip the value of B. In that case GSAT would have found
one solution, namely, F for A, F for B, and T for C. (The logically inclined
reader will have noted that there are actually three solutions, corresponding toeither of the three invitees being the sole attendee among them. Of course, thehostess would not be able to decide among these three, but at least she would
know how many places to set at her table.)
It is not surprising that GSAT found a solution for this small problem. In
fact, for large randomly generated problems, when the number of variables(the A’s, B’s, and C’s) is much smaller than the number of clauses, there arelikely to be many satisfying truth assignments, and GSAT (as well as othermethods) would be likely to find a solution. However, when the number ofvariables is much greater than the number of clauses, it is likely that there areno solutions at all.13
One important extension to GSAT is WALKSAT (sometimes called WSAT)
in which instead of always flipping the truth value of that proposition leading
to the largest increase in the number of clauses satisfied, sometimes a random
choice is made. This addition of a small amount of randomness helps to avoid
getting stuck on local maxima of the landscape.14
In comparing global versus local search methods, Bart Selman claims“Local search methods are still competitive in many domains but. . . because
the DPLL methods are less sensitive to problem encodings, they are used more
often nowadays to solve structured problems [such as hardware and software
465
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27

Other Approaches to Reasoning and Representation

verification].” However, he says that “the use of randomization and restarts in
DPLL. . . [brings some of] the nonsystematic aspect[s] of local search to
DPLL.”15

27.2.3

Applications of SAT Solvers

Several important problems can be encoded as SAT problems. For example,
Henry Kautz and Bart Selman showed that generating a plan of actions can be
expressed as a SAT problem.16 SATPLAN17 and Blackbox18 are two systems
that encode planning tasks as SAT problems and then use SAT solvers to
produce plans. SATPLAN starts with specially devised logical formulas
describing effects of actions, and Blackbox starts with STRIPS planning rules.
(You will recall the STRIPS automatic planning system, which I described in
Section 12.1.3.) According to Bart Selman, SAT solvers working on logistics
planning problems, for example, can produce optimal plans of around 500
steps in a few minutes.19 Recent versions of SAT-based planning systems have
won first-place prizes in the biennial International Planning Competitions.20
Efficient SAT solvers have also been applied to problems in theverification of programs and digital circuitry21 and in genomics. A closely
related topic involves what are called “Binary Decision Diagrams” (BDDs)
used in the verification of logical circuit designs.22

27.3

Representing Text as Vectors

In previous chapters, I described question-answering systems in which aquestion is converted into a computationally manageable form (perhaps into a
logical formula), which is then used to query a computer database (perhaps aknowledge base of logical formulas). Probably the most familiar examples ofquestion answering today take place using World Wide Web search engines.
An AI person of the logicist persuasion might hope that ultimately the text in
Web pages could be represented as logical formulas and that a query could be
represented as a logical formula to be answered (proved) from formulas in one(or more) of those Web pages. There are some beginning attempts23 to answer
English-language queries in this manner, but most Web search engines use
simpler and more efficient techniques. I’ll give a rough idea of how some ofthem work. They convert the text in both documents and queries to vectors
and compare a query vector against competing document vectors. First, I’ll
say a few things about vectors, and then I’ll describe how text can be
represented as a vector. (You will recall my earlier discussions of the use ofvectors in pattern recognition.)
In mathematics, a vector is a quantity having magnitude and direction. In
three-dimensional space, for example, one portrays a vector as an arrow drawn
from the origin of that space to a point in that space. The arrow points in the466
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27.3

Representing Text as Vectors

vector’s direction, and the length of the arrow is the vector’s magnitude.
Because the point determines the vector (there being only one way to draw anarrow from the origin to a point), the words “point” and “vector” are often
used synonymously. Any ordered list of numbers can be thought of as thecoordinates of a point and thus as the components of a vector. For example,
the list (7, 4, 3, 20) is a vector, one in a four-dimensional space. One can havevectors of many dimensions; the vectors used to represent documents can havethousands of dimensions. The length of a vector is the square root of the sumof the squares of all of the components of the vector. (For two-dimensional
vectors, this calculation is just an application of Pythagoras’s theorem, namely,
the square of the length of the hypotenuse of a right triangle is the sum of thesquares of its sides.) For example, the length of the vector (7, 4, 3, 20) is 21.77.
One can measure the similarity between two vectors either by calculating
the distance between their endpoints (perhaps adjusted to take into accounttheir lengths) or by the “smallness” of the angle between their two directions –the smaller that angle, the more similar are the vectors. For the angle method,
one performs the following similarity computation: Multiply each componentof one of the vectors by the corresponding component of the other vector andthen add together all of these products. Then, divide that sum by the productof the lengths of each vector. This final number, which we will call S for
similarity, can be at most 1 when the two vectors are exactly aligned (that is,pointing in the same direction). It is 0 when the two vectors are perpendicular
to each other, and it is negative when they point in opposite directions. So,
the more similar the vectors, the closer to 1 is their S calculation. (Readersfamiliar with trigonometry will recognize this calculation as the cosine of theangle between the two vectors.)
As an example, the value of S for the vectors (7, 4, 3, 20) and (7, 0, 2, 15)
can be calculated to be (49 + 6 + 300)/(21.77 × 16.67) = 0.978, a value thatindicates that these two vectors are quite similar.
How can we convert text to a vector? People who have been involved incomputer retrieval of documents (so-called information retrieval) have come up
with a method.24 First, an ordered list of terms (words or phrases) is chosen
for the set of documents to be represented by vectors. If the documents areabout artificial intelligence, there could be several hundred terms that would
be appropriate, including “search,” “heuristic,” “computer vision,” and so on.
If the documents are all in English and could be about anything, there might
be hundreds of thousands of terms (essentially all of the words in the English
language). Usually, the terms chosen are word stems, so that “computing,”“computers,” and “computed” would all be covered by the term “compute.”(One has to be careful about this kind of conflation, called “stemming,” toavoid substituting “flow” for “flower” and such.) Also, because words such as“and,” “if,” and “therefore” and so on are seldom relevant to the content of adocument, these words are not used as terms.
Next, in the process of representing a document as a vector, all of the467
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27

Other Approaches to Reasoning and Representation

occurrences of each of these terms in the document are counted. A list of theseoccurrence numbers is then assembled (in the same order as the list of terms),
and this list is the vector representation of the document. So, for example, if
the term “search” does not occur at all in a document being represented, if theterm “heuristic” occurs seven times, and the term “computer vision” occurs
three times, then the list would be, say,(0, 0, 0, 0, 0, 7, 0, 0, 3, 0, 0, . . .),where the underlined numbers are the number of times the terms I justmentioned occur in that document. Of course, there might be many, many 0’s
because many of the terms in the chosen list of terms might not occur at all inthe document, and there might be many more nonzero numbers corresponding
to the numbers of times other terms occur in that document.
Now, suppose we are interested in the question “What heuristics are usedin computer vision?” and pose this query to an Internet search engine. If weassume that some kind of preprocessing is used on the query (and on the
documents) to change words to their “stems,” the vector representation of ourquery would be(0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, . . .).
The similarity S between our query and the document we just considered
would be 10 divided by the product of the lengths of the two vectors. Thisvalue would be compared with the similarity values against other documents
to determine which documents are the most similar and therefore should be
retrieved in response to our query.
This all sounds pretty simple, but, although the basic idea is simple,
several elaborations are needed (and have been added) to make document
retrieval and Internet retrieval of Web sites based on this idea practical anduseful. First, the count for a term in a document is usually adjusted to take
into account the length of the text in that document. Because longer
documents might contain relatively more occurrences of a given term, thecount for a term is computed as a percentage of the total number of all theterms in the document. Second, because a given term may be quite common
among all the documents being searched (and thus not very useful for
discrimination), the count is diminished by a factor that depends on theoverall frequency of that term among these documents. More sophisticatedretrieval programs also use various statistical methods to compute theprobability of a document’s relevance to a query. An innovation invented by
Google ranks Web sites according to an estimate related to their popularity or“centrality.” Increasingly, “machine learning” methods (some of which will be
described in a subsequent chapter) are also being used to improve theperformance of retrieval systems, and, of course, efficiency requires appropriate
indexing schemes and the use of many thousands of computers.

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27.4

27.4

Latent Semantic Analysis

Latent Semantic Analysis

Some researchers have suggested that representing text as vectors captures the“meaning” of the text. How can that be when the vector representations arecomputed only from how often various terms occur in documents and not at
all from the order in which those terms occur? (After all, the meaning of “Dogbites man” is quite different from that of “Man bites dog.”) Thomas K.
Landauer (1932– : Fig. 27.7) and colleagues, first in his Cognitive Science
Research Group at Bell Communications Research (a descendent of Bell
Laboratories) in the mid-1980s, and later at the University of Colorado, haveproposed a vector-based scheme for capturing meaning, which they call Latent
Semantic Analysis (LSA). I think I can explain the basic idea without using allof the mathematics that a full description would require.

Figure 27.7: Thomas K. Landauer. (Photograph courtesy of Libby Landauer.)
Here, in a scaled-down example, is basically how the LSA method works.
Let’s say we have a rather long document or other text material about acertain topic. We divide the material into sections, called “passages,” ofaround 100 or so terms each. Supposing that the vocabulary of the material is469
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Other Approaches to Reasoning and Representation

captured by 1,000 terms (which could consist of individual words or word
combinations), then each of these passages is represented by a
1,000-dimensional vector. (The term counts used in constructing these vectors
are adjusted by methods similar to those I have already explained.) Let’s
suppose we have 100 such vectors.
It is difficult (impossible really) to visualize a 1,000-dimensional space inwhich our vectors are embedded, but perhaps one can at least imagine thatsome lower dimensional “subspace” would contain all or most of the vectors. It
might help to consider a three-dimensional example as shown in Fig. 27.8. In
the diagram, I show five points that happen to lie on a plane (atwo-dimensional space) within a three-dimensional space. The
two-dimensional space is a subspace of a three-dimensional space. In that
two-dimensional space, the five points can be represented by two-dimensional
vectors instead of three-dimensional ones.

Figure 27.8: A two-dimensional subspace within a three-dimensional space.
Using various complex mathematical techniques, it is possible to construct
a lower dimensional space that adequately “contains” the 100 vectors(perhaps, say, a 50-dimensional space). LSA uses methods based on atechnique called “Singular Value Decomposition” (SVD), the details of which
470
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27.4

Latent Semantic Analysis

need not concern us here. Of course, the representation of these vectors in 50
dimensions will be different than it was in 1,000 dimensions. Many of theterms associated with dimensions in the larger space get conflated into newcomponents in the smaller space. Moreover, according to Landauer andcolleagues, it is this very conflation that allows extraction of the latent overall
meaning from the separate passages of the document. As they put it inexplaining an example of the process, “. . . if we were to change the entry in
any one cell of the original, the values in the reconstruction with reduced
dimensions might be changed everywhere; this is the mathematical sense inwhich LSA performs inference or induction.”25
Transforming vectors into ones with fewer components essentially links
together many of the terms occurring (and not occurring) in the original
passages from which the vectors were derived. This linking together can bethought of as creating a higher level “concept” based on the associated terms.
Expressing a text document in terms of these concepts (that is, in terms of thevectors of reduced dimension) has extracted, according to the LSA people, theessential “meaning” of the document. The reduced-dimension vectors can link
together terms from different sections of a text if they occur in passages
having a similar meaning even though they never occur in the same passage.
The LSA process allows the computation of the similarity between any
two passages in the document, say by computing the size of the angle between
the two corresponding reduced-dimension vectors. Along with the process ofrepresenting passages by vectors of reduced dimension, the LSA method also
produces a representation of each term in the entire set of terms by a vector
having the same reduced dimension. By using that representation, thesimilarity between two terms can also be computed, as well as the similarity
between a term and a passage. Finally, a document itself can be represented
as a vector consisting of the average of its passage vectors. Once so
represented, the similarity between documents can be computed. This step isused in one of the applications of LSA called “Latent Semantic Indexing”(LSI). The LSI method is reported to offer some improvement over standard
retrieval methods (although the point is still controversial).26
LSA has been used in several settings, including grading essays written bycollege-entrance-exam test-takers, helping students learn writing skills, helping
to diagnose schizophrenia from patient verbalizations, and creating key-wordsummaries of documents.27 In addition, it has been used to mimic somehuman abilities, such as scoring as well as average test-takers on the synonym
portion of TOEFL (the ETS TEst of English as a Foreign Language) andachieving a passing score on a multiple-choice exam using the vectors from an
LSA analysis of an introductory psychology textbook.28
A reader might object that an LSA system for grading essays could be
foiled by someone who wrote a large number of appropriate words in random
order without expressing any coherent thoughts at all. Landauer counters thisobjection by saying that it would be hard “to get the good words without
471
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27

NOTES

writing a good essay. . . . We’ve tried to write bad essays and get good grades
and we can sometimes do it if we know the material really well. The easiest
way to cheat this system is to study hard, know the material and write a good
essay.”29
In 1998, Landauer and colleagues formed Knowledge Analysis
Technologies (KAT) to develop educational applications of LSA. KAT was
acquired by Pearson Education in 2004 and markets LSA-based educational
products as Pearson Knowledge Technologies (PKT).
Some researchers have pointed out that the main power of the LSA
methods is in vector dimensionality reduction and that there are several othermethods (some of which are simpler than that used in LSA) for reducing
dimensionality. In fact, in one of their early papers about LSA, Landauer andSusan Dumais describe an LSA analog based on a neural network.30
A probabilistic extension to Latent Semantic Indexing has been proposed
and tested by Thomas Hofmann.31 A more general probabilistic model has
been developed by David Blei, Andrew Ng, and Michael Jordan.32
Probabilistic models of all sorts began to play a very prominent role inartificial intelligence beginning in the late 1980s. It is to that subject that I
turn next.

Notes
1. Alan K. Mackworth, “Consistency in Networks of Relations,” Artificial Intelligence, Vol.
8, No. 1, pp. 99–118, 1977. [456]
2. Rina Dechter, Constraint Processing, San Francisco: Morgan Kaufmann Publishers,
2003. [457]
3. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second
edition, Upper Saddle River, NJ: Prentice Hall, 2003. [457]
4. Vipin Kumar, “Algorithms for Constraint-Satisfaction Problems: A Survey,” AI
Magazine, pp. 32–44, Spring 1992. Available online at
http://www.cs.cinvestav.mx/∼constraint/papers/kumar-survey.pdf. [457]5. E-mail communication, April 8, 2008. [461]
6. The algorithm is named for Martin Davis, Hilary Putnam, George Logemann, andDonald W. Loveland. See Martin Davis, George Logemann, and Donald Loveland, “A
Machine Program for Theorem-Proving,” Communications of the ACM, Vol. 5, No. 7, pp.
394–397, 1962. [461]7. Martin Davis and Hilary Putnam, “A Computing Procedure for Quantification Theory,”
Journal of the ACM, Vol. 7, No. 3, pp. 201–215, 1960. [461]8. See, for example, Matthew Moskewicz et al., “Chaff : Engineering an Efficient SAT
Solver,” Proceedings of the 38th Design Automation Conference (DAC’01), 2001 (available
online at http://www.princeton.edu/∼chaff/publication/DAC2001v56.pdf), and Niklas Eénand Niklas Dörensson, “An Extensible SAT-Solver,” Theory and Applications of Satisfiability
Testing, Lecture Notes in Computer Science, Berlin and Heidelberg: Springer-Verlag, 2004
(available online at http://een.se/niklas/Satzoo/An Extensible SAT-solver.ps.gz). [462]472
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27.4

NOTES

9. See http://www.princeton.edu/∼chaff/zchaff.html. A Web site for another efficient SAT
solver, MiniSat, is at http://minisat.se/Main.html. [462]
10. Bart Selman, Hector Levesque, and David Mitchell, “A New Method for Solving Hard
Satisfiability Problems,” Proceedings of the Tenth National Conference on Artificial
Intelligence, pp. 440–446, Menlo Park, CA: AAAI Press, 1992. Available online at
http://www.cs.sfu.ca/∼mitchell/papers/ai92-gsat.ps. [464]11. Bart Selman, Henry Kautz, and Bram Cohen, “Local Search Strategies for Satisfiability
Testing,” in David S. Johnson and Michael A. Trick (eds.), Cliques, Coloring, andSatisfiability: Second DIMACS Implementation Challenge, October 11–13, 1993 (DIMACS
Series in Discrete Mathematics and Theoretical Computer Science, Vol. 26, Providence, RI:
AMS, 1996.) A version of the paper is available online at
http://www.cs.rochester.edu/u/kautz/papers/dimacs93.ps. Also see a Web page about local
search methods at http://www.cs.rochester.edu/u/kautz/walksat/. [464]12. Bart Selman, Hector Levesque, and David Mitchell, op. cit. [465]13. Bart Selman, Hector Levesque, and David Mitchell, op. cit. [465]14. See Bart Selman, Henry Kautz, and Bram Cohen, op. cit.. [465]15. Bart Selman, e-mail of April 8, 2008. [466]16. Henry Kautz and Bart Selman, “Planning as Satisability,” Proceedings of the 10th
European Conference on Artificial Intelligence, pp. 359–363, New York: John Wiley andSons, Inc., 1992 (available online at http://www.cs.rochester.edu/u/papers/satplan.ps), andHenry Kautz and Bart Selman, “Pushing the Envelope: Planning, Propositional Logic, andStochastic Search,” Proceedings of the 13th National Conference on Articial Intelligence(AAAI-96), pp. 1194–1201, Menlo Park, CA: AAAI Press, 1996 (available online athttps://eprints.kfupm.edu.sa/58089/1/58089.pdf). [466]17. Henry Kautz, Bart Selman, and Joerg Hoffmann, “SATPLAN: Planning as
Satisfiability,” Abstracts of the 5th International Planning Competition, 2006 (available
online at http://www.cs.rochester.edu/u/kautz/papers/kautz-satplan06.pdf). See
http://www.cs.rochester.edu/u/kautz/walksat/ for information about and downloads of
SATPLAN programs. [466]18. Henry A. Kautz and Bart Selman, “Unifying SAT-Based and Graph-Based Planning,”
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp.
318–325, San Francisco: Morgan Kaufmann Publishers, 1999. Available online at
http://www.cs.rochester.edu/u/kautz/satplan/blackbox/ijcai99blackbox.ps. See
http://www.cs.rochester.edu/u/kautz/satplan/blackbox/ for information about anddownloads of Blackbox programs. [466]19. Bart Selman E-mail of April 8, 2008. [466]20. See http://zeus.ing.unibs.it/ipc-5/ for information about the International Planning
Competitions. [466]21. See, for example, Armin Biere et al., “Bounded Model Checking,” Advances in
Computers, Vol. 58, San Diego: Academic Press, 2003. One of the co-authors of this paper,
Edmund M. Clarke, a computer science professor at Carnegie Mellon University, was aco-recipient of the 2007 ACM Turing Award for his work in this field. [466]22. See Randy E. Bryant, “Graph-Based Algorithms for Boolean Function Manipulation,”
IEEE Transactions on Computers, Vol. C-35, No. 8, pp. 677–691, August 1986. Available
online at http://www.cs.cmu.edu/∼bryant/pubdir/ieeetc86.pdf. [466]23. See, for example, http://www.powerset.com/. [466]24. The paper in which this method was first presented is Gerard Salton, A. Wong, and C.
S. Yang, “A Vector Space Model for Automatic Indexing,” Communications of the ACM,
Vol. 18, No. 11, pp. 613–620, November 1975. For some quibbles about this topic see

473
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27

NOTES

http://www.ideals.uiuc.edu/bitstream/2142/1697/2/Dubin748764.pdf. [467]25. Thomas K Landauer, Peter W. Foltz, and Darrell Laham, “Introduction to Latent
Semantic Analysis,” Discourse Processes, Vol. 25, pp. 25–284, 1998. Available online at
lsa.colorado.edu/papers/dp1.LSAintro.pdf. [471]26. Susan T. Dumais, “Latent Semantic Indexing (LSI) and TREC-2,” in D. Harman (ed.),The Second Text Retrieval Conference (TREC2), National Institute of Standards andTechnology Special Publication 500-215, pp. 105–116, 1994. A copy is available online at
trec.nist.gov/pubs/trec2/papers/txt/10.txt. [471]27. Thomas Landauer, e-mail communication, January 30, 2008. [471]28. For more information about these applications, see the LSA Web site at
http://lsa.colorado.edu/ and the various papers cited there. [471]29. From a press release available online at http://lsa.colorado.edu/essay press.html. [472]30. Thomas K Landauer and Susan T. Dumais, “Solution to Plato’s Problem: The Latent
Semantic Analysis Theory of Acquisition, Induction and Representation of Knowledge,”
Psychological Review, Vol. 104, No. 2, pp. 211–240, 1997. An online version, dated 1977, isavailable at http://lsi.research.telcordia.com/lsi/papers/PSYCHREV96.html. [472]
31. Thomas Hofmann, “Probabilistic Latent Semantic Indexing,” Proceedings of theTwenty-Second Annual International SIGIR Conference on Research and Development inInformation Retrieval, 1999. Available online at
http://www.cs.brown.edu/∼th/papers/Hofmann-SIGIR99.pdf. [472]
32. David M. Blei, Andrew Y. Ng, and Michael I. Jordan, “Latent Dirichlet Allocation,”
Journal of Machine Learning Research, Vol. 3, pp. 993–1022, 2003. Available online at
http://www.cs.princeton.edu/∼blei/papers/BleiNgJordan2003.pdf. [472]474
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28.1

Chapter 28

Bayesian Networks
28.1

Representing Probabilities in Networks

Much human reasoning is about propositions and quantities that are
uncertain. Our beliefs about many things are provisional (that is, subject tochange) and qualified (that is, having various levels of confidence). AI systems,
too, need to be able to deal with uncertain information. An AI agent’s facts,
statements, and rules should most appropriately be thought of as provisionaland qualified. After all, some of its information is provided by humans andsome originates from sensors with limited precision and reliability. Yet, muchof the early work in AI ignored the uncertain nature of knowledge. In fact,
Marvin Minsky observed that his edited volume of early AI papers contained“no explicit use of probabilistic notions.”1
Most AI researchers nowadays, however, acknowledge that much of theknowledge needed by machines needs to be qualified by probability values andthat reasoning with this knowledge can therefore most appropriately be donewith the tools of probability theory. But just as is the case with logicalreasoning, probabilistic reasoning is subject to AI’s old nemesis, thecombinatorial explosion. Suppose, for example, that an agent’s knowledge
consists of a set of propositions. Because of possible interdependencies among
the propositions, accurate probabilistic reasoning depends on knowing morethan just the probability of each of those propositions individually. Instead,probability values for various combinations of the propositions taken together,called “joint probabilities,” are usually required; this leads, in the general case,to impractically large representations and intractable computations.
Earlier AI systems that could deal with uncertainty, such as MYCIN and
PROSPECTOR, made simplifying assumptions to ease these representationaland computational difficulties. However, because these systems failed to take
into account important interdependencies among their beliefs, they often gave
475
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28

Bayesian Networks

inappropriate results owing to such things as overcounting of evidence. During
the 1980s some powerful new methods were invented (and imported from otherfields) that were better able to deal with dependencies. These methods greatly
simplified both the representational and the computational problems. They
involve representing uncertain beliefs and their dependencies in a graphicalform, called a “probabilistic graphical model.” I’ll describe the most importantversion of such models in this chapter.
First, to illustrate some of the difficulties involved in reasoning about
uncertain beliefs and how we might deal with them, let’s look at an example
involving various propositions about an automobile engine. Here are some of
the things we might say about an engine and its components:
P1: The starter motor is ok.
P2: The starter motor cranks the engine when the starter switch isturned on.
P3: The fuel system is ok.
P4: The car starts when the starter switch is turned on.
These propositions are quite obviously related. For one thing, P4 depends on
the other three – the sad observation that P4 is false would certainly change
our confidences about the other three. Moreover, it would not take an auto
mechanic to know that P1 and P2 are related.
A full account of the dependencies involved here requires a listing of all of
the possibilities for things being ok and not ok, and there are sixteen suchpossibilities. If we denote the opposite of a proposition by putting a negation
sign (¬) in front of it, then ¬P1 denotes “The starter motor is not ok.” Using
this notation, the sixteen possibilities are
P1, P2, P3, P4,
P1, P2, P3, ¬P4,
P1, P2, ¬P3, P4,
P1, P2, ¬P3, ¬P4,
P1, ¬P2, P3, P4,
P1, ¬P2, P3, ¬P4,
P1, ¬P2, ¬P3, P4,
P1, ¬P2, ¬P3, ¬P4,
¬P1, P2, P3, P4,¬P1, P2, P3, ¬P4,
476
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28.1

Representing Probabilities in Networks¬P1, P2, ¬P3, P4,¬P1, P2, ¬P3, ¬P4,
¬P1, ¬P2, P3, P4,¬P1, ¬P2, P3, ¬P4,
¬P1, ¬P2, ¬P3, P4,

and¬P1, ¬P2, ¬P3, ¬P4.
An expert who knows about engines and their expected reliabilities
would, presumably, be able to assign probability values to each of these sixteen“states” in which an engine system might find itself. For example, the expert
might specify that the overall joint probability that everything is ok, denotedby p(P1, P2, P3, P4), is 0.999. He or she would have to specify sixteen such
numbers. (Actually, only fifteen would be needed because the sixteen would
have to sum to one. These are the only possible states and one of them must
be the case.) Knowing these joint probabilities would enable a person(possessing patience and skills in probability theory) to calculate certain otherprobabilities, such as the probability that the car starts given only, say, thatthe fuel system is definitely ok.
Specifying the fifteen numbers for this small example does not seem too
arduous, but for a more realistic problem, say one with thirty different
propositions, one would have to specify 230 − 1 = 1,073,741,823 numbers.
Moreover, if there are also quantities that might take on several values (inaddition to propositions, which are binary-valued), the number of possibilities
expands even further.
Of course, I have assumed here the worst case, namely, the case in which
all four propositions might depend in complex ways on each other. At theother extreme is the case in which the propositions are completely independentof each other. Then, each of the sixteen probabilities could be computed byformulas such asp(P1, P2, P3, P4) = p(P1)p(P2)p(P3)p(P4)(with ¬ signs put in as required), and we would need only to specify
probabilities for each of the four propositions individually.
My example about automobile engines is actually somewhat in between
these two extremes. So also are many much larger and more realistic
problems. This “in-betweeness” is the key to making probabilistic reasoning
more tractable. Although there was previous recognition and exploitation ofthis fact by statisticians, it was Judea Pearl (1936– ; Fig. 28.1) who developed
some of the main representational and computational methods.
Pearl, a professor of computer science at the University of California at
Los Angeles, was puzzled by the contrast between, on the one hand, the ease477
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28

Bayesian Networks

with which humans reason and make inferences based on uncertain
information and, on the other hand, the computational difficulties of
duplicating those abilities using probability calculations. As he later put it, hestarted with the following conjectures:2
1. The consistent agreement between plausible reasoning [byhumans] and probability calculus could not be coincidental but
strongly suggests that human intuition invokes some crude form of
probabilistic computation.
2. In light of the speed and effectiveness of human reasoning, thecomputational difficulties that plagued earlier probabilistic systems
could not be very fundamental and should be overcome by making
the right choice of simplifying assumptions which humans store in
their head.

Figure 28.1: Judea Pearl. (Photograph courtesy of Judea Pearl.)
Pearl’s key insight was that beliefs about propositions and otherquantities could often be regarded as “direct causes” of other beliefs and thatthese causal linkages could be represented in graphical structures that encode
simplifying assumptions about relationships among probabilities.
To be sure, Pearl was not the first to suggest using graphical structures to
encode probabilistic information. He himself mentions earlier work.3 Russell
and Norvig4 wrote that work by the British statistician I. J. Good “could be
regarded as a forerunner of modern Bayesian networks. . . .”5 And physicists
point to closely related work by Hans A. Bethe.6For our automobile engine problem, the sort of graph that Pearl mightuse is as shown in Fig. 28.2. Each proposition of interest is represented by a“node” in the graph. The arrows show the direct influences among the various
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28.1

Representing Probabilities in Networks

propositions and also indicate certain probabilistic independencies among
them. For example, the probability of P4 (the car starts when the starterswitch is turned on) does not depend at all on the probability of P1 (thestarter motor is ok) if we already know (are given) P2 (the starter motor
cranks the engine when the starter switch is turned on) and P3 (the fuelsystem is ok). Knowing P1 does not tell us anything new about P4 if wealready know P2 and P3. In the language of probability theory, the probabilityof P4 is conditionally independent of P1, given P4’s parents, namely, P2 and P3.
In real-world reasoning tasks there are many such conditional independencies,
which can be revealed by these kinds of causally derived graphs. Taking
account of them greatly reduces the complexity of probabilistic reasoning.

Figure 28.2: A network representation.
For our automobile engine example, instead of the fifteen probabilities
that would be needed in the general case, now we need only eight. These areas follows: the probabilities of P4 given P2 and P3 for the four different statesof P2 and P3,
p(P4 | P2, P3),
p(P4 | P2, ¬P3),
p(P4 | ¬P2, P3),
p(P4 | ¬P2, ¬P3);
the probability of P2 given P1 for the two different states of P1, namely,p(P2 | P1) and p(P2 | ¬P1); and the probabilities of P1 and P3, namely, p(P1)and p(P3). Each of these sets of probability values is stored in what is called a“conditional probability table” (CPT) associated with its corresponding nodein the network. (The CPT of a node with no parents is just the unconditional
probability for that node.)
By using a result from probability theory all sixteen joint probabilities(required for accurate probabilistic reasoning) can be computed from theseeight. We aren’t actually getting something for nothing here. Instead we are
exploiting the added knowledge provided by the conditional independencies
made evident by the network.
479
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28

Bayesian Networks

Because Bayes’s rule plays a prominent role in computing probabilities ofthe various nodes given the probabilities of others, Pearl coined the phrase“Bayesian belief networks” (usually simplified to Bayesian networks or belief
networks) for these sorts of graphs.7 It has proven rather easy to constructlarge Bayesian networks by carefully noting which propositions directly
influence (“cause”) others. Networks thus constructed are what graphtheorists call “directed acyclic graphs” (DAGs): “directed” because arrows
point from cause nodes to caused nodes and “acyclic” because following thearrows outward from a node never leads back to that same node.
One might well ask, where do the probability values in the CPTs comefrom? For some networks, perhaps an expert familiar with how certain
propositions affect others might be able to make guesses about probabilities.
Such guesses are called “subjective probabilities,” based as they are on an
expert’s subjective notions about cause and effect. However, by far the mostuseful method for populating the CPTs with values is to estimate them from alarge database of actual cases. I’ll explain how that is done in the next section.
By whatever means they are obtained, the CPTs (together with thestructure of the network) are used in computations about how the
probabilities of some nodes in the network are affected by the probabilities ofothers. These computations are called “probabilistic inference.” Various
practical computational methods have been devised – even for the rather large
networks needed for realistic problems.
Without going through any actual computations, I’ll use the small engine
network to illustrate three main styles of probabilistic inference in Bayesian
networks. For example, if all we knew for certain was that the starter motor
was ok [that is, p(P1) = 1)], we could compute the probability that the car will
start. “Migrating” known probability values downward in the network (in the
direction of the arrows) is usually called “causality reasoning.” Conversely, if
we knew that the car would not start [that is, p(P4) = 0], we could computethe probabilities of the starter motor being ok and of the fuel system being ok.
Migrating probability values upward in the network (against the direction of
the arrows) is usually called “evidential” or “diagnostic” reasoning. It is what
physicians (and other trouble-shooters) do when they have a symptom andattempt to infer the probabilities of causes.
There is another important reasoning style also, and that is called“explaining away.” Here is an example: Suppose we know that the car does
not start and we have computed probability values for the fuel system being
the problem (that is, not ok) and for the starter motor being the problem.
Then, later, we find out that actually the starter motor has, in fact, failed.
Taking that additional information into account, we would find that theprobability of the fuel system being the problem would decrease. The starter
motor problem “explains” the fact that the car would not start, so we haveless reason to suspect the fuel system. The fact that the starter motor does
not start “explains away” the possible fuel system problem. The strategy of480
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28.1

Representing Probabilities in Networks

explaining away is commonly used by people in medicine, law, science, andeveryday reasoning. For example, a defense attorney might cite evidence thatsome other person (not his client) was identified on a bank’s TV monitoring
system, thus explaining away his client’s involvement in a bank robbery.
I can illustrate the explaining-away effect with actual inference
calculations performed on the somewhat larger network about engines shownin Fig. 28.3.8 After observing that the car does not start, the probability thatthe starter motor is the problem is computed to be 0.023 (by using the
network’s conditional probability tables, which are not shown in the diagram),and the probability that the fuel system is the problem is computed to be
0.283. But upon additionally observing that the starter motor has failed, theprobability that the fuel system is the cause of the problem drops by morethan half to 0.1.

Figure 28.3: A Bayesian network from an interactive Web site. (Used withpermission of Alan Mackworth and David Poole.)
If we wanted to build a Bayesian network about an automobile engine
that was more realistic and useful, we would have to mention many morecomponents and subsystems. Such a network might contain hundreds of nodes
along with their associated conditional probability tables. Even thoughconditional independencies would reduce the number of individual
probabilities that need to be specified, still their number can be so large that481
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28

Bayesian Networks

exact probabilistic inferences would still be computationally intractable –assuming that values for these probabilities could even be gathered.
Fortunately, various simplifications are possible that permit further reductionsin the number of probabilities needed. With them, computations forapproximate, but still useful, inference in large networks become practicable.
It is worth mentioning that some of these simplifications and approximate
computational methods involve rather complex mathematical tools, many ofthem stemming from adjacent fields such as statistics and control engineering.9
These kinds of Bayesian network calculations provide another instance of how
problems previously thought to be computationally intractable have yielded totechnical advances.
In Fig. 28.4, I show an example of a rather large Bayesian network.10 The
network represents knowledge about hepatobiliary disease (of the liver, gall
bladder, and related organs) and was developed as a tool to use with medical
students. This network was derived in part from the knowledge base of
INTERNIST-1 (see p. 301). It has 448 nodes and 908 arrows. If full conditional
probability tables were used, 133,931,430 probabilities would have to be
specified. The network’s developers were able to reduce this number to 8,254
values using various simplifications.
Bayesian networks containing hundreds of nodes have been used for
applications in biology, medicine, document classification, image processing,law, error-correction decoding, and many other fields.11 Many of these
networks are derived automatically from large data sets, a topic I’ll discuss inthe next section.

28.2

Automatic Construction of Bayesian
Networks

One of the reasons why Bayesian networks have become so important is thatthey can be automatically constructed from large databases. That is, they canbe “learned,” and the learned versions can be used to reason about the subject
area in question. Two of the pioneers in the development of these learning
methods were Greg Cooper and Edward Herskovits.12 The subject continues
to be an active research area, and there are several others who have madesignificant contributions.13
Here, in general terms, is how the process works. To learn a network
involves learning the structure of the network, that is, the disposition of its
nodes and links, as well as the network’s CPTs. First, I’ll explain how the
CPTs for a known structure can be learned and next how the structure itself
can be learned, even though the two processes are actually interlinked. Let’s
consider again the four-node Bayesian network for the automobile engine. How
might we learn the CPT for the node P4 (that is, Car Starts) – the one

482
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28.2

Automatic Construction of Bayesian Networks

Figure 28.4: A large medical Bayesian network. (Used with permission of Gregory Provan.)whose parents are P2 (that is, Car Cranks) and P3 (that is, Fuel SystemOK)? Using my abbreviations for those propositions, that CPT is composed of483
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28

Bayesian Networks

the following conditional probabilities:p(P4 | P2, P3),
p(P4 | P2, ¬P3),
p(P4 | ¬P2, P3),
andp(P4 | ¬P2, ¬P3).
If we had a large collection of samples of situations in which the car
sometimes starts and sometimes did not and in which the car sometimes
cranks and sometimes did not and in which the fuel system sometimes was ok
and sometimes was not, we could use them to tabulate what are called“sample statistics.” For example, we could note the number of times in thesesamples that the car did start when the car did not crank and the fuel system
was ok and divide that number by the total number of times that the car did
not crank and the fuel system was ok. That fraction could be used as an
estimate of p(P4 | ¬P2, P3). We could make similar estimates of the other threeprobabilities and similar estimates for the probabilities in the other CPTs in
the network. With a sufficiently large collection of samples, these estimateswould be reasonably reliable: The greater the number of samples, the betterthe estimates.
Compilation of the sample statistics (sometimes augmented by some
additional computations, which I won’t go into here) provides a means for
estimating the CPTs of a network with known structure. Now, how could welearn the structure of an unknown network? The method involves thefollowing sequence of steps:
1. Start with some basic candidate structure, such as one that has noconnections between nodes, and use the data collection to estimate its
CPTs. (Recall that the CPT of a node with no parents is just theunconditional probability for that node. It can be estimated by thefraction of times its associated proposition is true in the data set.)
2. Calculate a “goodness measure” for this network. One of the proposed
measures is based on how well the network, with its calculated CPTs,
could be used to transmit (that is, regenerate) the original datacollection.
3. Begin a “hill-climbing” search process by evaluating “nearby” networks
that differ from the previous one by small changes (which might involve
adding an arc, deleting one that is already there, and swapping nodes).
To evaluate the changed networks, their CPTs and goodnesses arecalculated. Settle on that changed network with the best improvement ingoodness.
484
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28.2

Automatic Construction of Bayesian Networks

4. Continue the hill-climbing process until no more improvements can bemade (or until some predefined stopping criterion is met).
Although this process appears to be quite tedious (and it is), computers can
execute this hill-climbing process reasonably efficiently, and some rather
complex networks have been learned.

Figure 28.5: Learning a Bayesian network. (From Nils J. Nilsson, Artificial
Intelligence: A New Synthesis, p. 350, San Francisco: Morgan Kaufmann Publishers, 1998.)
As an example, consider the networks in Fig. 28.5. Three networks are
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28

Bayesian Networks

shown. The first is a network encoding relationships among 37 variables for a
problem involving an alarm system used in ventilator management in a
hospital intensive-care unit. This known network was used to generate asize-10,000 training set of random values for the 37 nodes. By using thisrandom sample, and starting with the second network (the one without any
dependencies and thus no links between nodes), the third network was learned(in about five hours on a SUN SPARCstation 20) using methods similar to
those just described.14 Note the very close similarity in structure – only one
arc is missing.
Sometimes network structure can be simplified substantially by adding
nodes to the network that represent attributes that do not occur in the data
set. Network learning methods have been extended to be able to learn toinstall “hidden nodes” that represent these invented attributes. Attributes
invented from the data are often useful for deepening our understanding of thephenomena that gave rise to the data.

28.3

Probabilistic Relational Models

An important elaboration of Bayesian networks, called “Probabilistic
Relational Models” (PRMs),15 has been developed by Stanford professor
Daphne Koller (1968– ; Fig. 28.6), together with her students Avi Pfeffer andLise Getoor and a collaborator, Nir Friedman (a former Stanford student and
now a professor at Hebrew University). PRMs integrate probability withpredicate calculus. [Some earlier work on combining these two representational
forms was done by several researchers, notably by David Poole (1958– ) at the
University of British Columbia.]16 PRMs exploit the fact that some nodes in a
network might share the same attributes except for the values of variables
internal to those attributes (much like the fact that in the predicate calculus
the same predicate may be written with different values for its internalvariables).
For example, a network showing that a person’s blood type andchromosomal information depends on chromosomes inherited from his or her
parents would have repeated subnetworks. I show an example in Fig. 28.7. In
this case, a single “template” is used to make different subnetworks whose
attribute variables are instantiated to different individuals. Using PRMs
makes the design of Bayesian networks much more efficient than would be theprocess of having to design each (only slightly different) subnetwork separately.
Koller says she was motivated to think about PRMs in a conversation with astudent who was having to convert a Bayesian network modeling a three-lane
freeway to one modeling a four-lane freeway. She recalls saying “. . . but that’s
just adding one more lane, surely you can reuse some of the structure.”17
The structure and CPTs of a PRM can either be specified by a designer
or learned from data.18 An added benefit of PRMs is that objects resulting
486
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28.3

Probabilistic Relational Models

Figure 28.6: Daphne Koller. (Photograph courtesy of Daphne Koller.)

Figure 28.7: Example of a PRM.

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28

Bayesian Networks

from instantiating template variables can be linked in the resulting Bayesian
network (as some are in the diagram); relationships among these objects canbe specified by hand or learned. As with any Bayesian network, probabilistic
inference procedures can be used to answer queries about the probabilities ofsome nodes given those of others.
PRMs and related structures have been used in a variety of applications,
including one for recovering regulatory networks from gene expression data.19
As regards Bayesian networks in general, there are by now many, many
applications – too many to list here. To give a flavor of their variety, I’ll
mention their use in genomic studies,20 in automobile traffic forecasting androuting,21 in modeling disease outbreaks,22 and in guessing at a computeruser’s next actions to enable the Windows operating system to “prefetch”
application data into memory before it is demanded.23 There are also
companies that sell knowledge-capturing and reasoning systems based on
Bayesian networks.24
One thing that all of these applications has taught us is the importance ofmassive amounts of data, which according to Peter Norvig, the co-author of
the leading AI textbook and Director of Research at Google, has turned out tobe the major theme of modern AI.25 In fact, Peter told me that Google is theworld’s biggest AI system. I asked him why, and he simply replied “data,data, data,” and Google has more of it than anybody.”26

28.4

Temporal Bayesian Networks

The examples of Bayesian networks illustrated in the last sections, along withlarger ones used in many applications, are what one might call “static.” Thatis, the propositions and quantities represented by the nodes and CPTs aretimeless in the sense that they deal with the same moment in time (or all
moments in time). Yet, I have already described a probabilistic network that
does involve quantities at different times, namely, hidden Markov models. In
Section 17.3.2, I explained how HMMs were used in speech recognition. One
common form of an HMM is shown in Fig. 28.8.
This diagram is meant to show how a time sequence of entities,
x1 , x2 , . . . xn , causes a time sequence of other entities, y1 , y2 , . . . , yn . The
influence of each xi on subsequent x’s and on the y’s is governed byprobabilities. One can easily see that this network is a Bayesian network, eventhough the quantities involved occur in a temporal sequence. This particular
HMM is called a first-order Markov process because each state depends only
the immediately preceding state. In higher order processes, each state is
influenced by more than one preceding state.
In an HMM network, each xi is a “state variable,” and the yi are“observable variables.” It is presumed that the values of the states are
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28.4

Temporal Bayesian Networks

Figure 28.8: A hidden Markov model.
unknown (that is, “hidden”) but that the observables can be measured andthus known. Each state causes an associated observable and the next state.
We presume that we know the conditional probability tables of the network,that is, the probabilities of the observables and the next state given the valueof a state. Given the value of one or more observables, we can calculate
updated probabilities of states using any of the methods for computing
probabilities in a Bayesian network.
Here is an example. Suppose aircraft weather conditions at a remote
airport are either foggy or not. A sensor at the airport records the weather
and a transmitter broadcasts a signal every five minutes. This signal, as it isreceived by an airplane attempting to land at the airport, might occasionallybe in error. So, the states, that is, the x’s in the HMM modeling this process,can have values of 1 or 0, with a value of 1 indicating fog. The signals received
by the aircraft, the y’s in the HMM, also have values of 1 or 0, with a value of
1 indicating fog is observed. But the observations might be in error. To be
concrete, let us suppose the probability that the next state has the same value
as that of the present state is 75% (fog tends to persist) and the probability
that an observable is in error is 5%. These probabilities allow the constructionof Bayesian network conditional probability tables. (The example can be mademore realistic by allowing each state to reflect degrees of fogginess and todepend on states in addition to the single preceding state.)489
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28

Bayesian Networks

The pilot of an aircraft must make a decision about attempting to land or
not based on the sequence of y’s received. For example, he or she might wantto know the probability that the landing strip is foggy right now based on asequence of previous observations up to and including the present one. In
HMM parlance, the operation that computes this probability is called“filtering.” Alternatively, the pilot might want to compute the probability thatthe landing strip will be foggy 10 minutes from now based on theseobservations. This operation is called “prediction.” Although it would not beof much use to the pilot, he or she might be curious about the probability thatthe landing strip was foggy 10 minutes ago based on a sequence of observations
up to and including the present. This operation is called “smoothing.”
In my discussion of speech recognition in Section 17.3.2 I mentioned thatthe HMM states correspond to single words and that the observations
correspond to waveform segments. In that application we want to compute themost likely sequence of words given all of the observations of waveforms up to
the present.
All of these computations – filtering, prediction, smoothing, andmost-likely-state sequence – can be performed using Bayesian network
inference procedures. There are several specialized versions, some of which
originated in fields outside of AI. These depend on the application and on theparticulars of the networks involved. Of these, I might mention (but won’t try
to explain here) the forward-backward algorithm, the Viterbi algorithm, andKalman filtering. The mathematically brave reader can find clear explanations
in Russell and Norvig’s excellent textbook.27 The fact that full explanations
involve rather complex mathematics testifies again to the great increase in AI’s
technical depth that began in the 1980s.
HMMs, including my foggy/not-foggy example, have only a single statevariable at each instant of time. It is possible to construct networks in which
there are more state variables at every time instant – all of which affect eachother, the observations, and subsequent state variables. These are typicallycalled “dynamic Bayesian networks” (DBNs) and were first explored in AI byThomas Dean (1950– ) and Keiji Kanazawa.28 Additional state andobservation variables make exact computations intractable, but several
practical approximate methods, such as “particle filtering” (which I’ll describe
in more detail later) have been developed. And, just as with ordinary Bayesian
networks, DBNs can be learned from databases containing information about
temporal processes. They have been used in several applications, primarily
those involving perception. One such is the processing of movies in which
probabilistic frame-to-frame dependencies can be exploited for recognizing andtracking moving objects. Another is in the certification of collision avoidancesystems for manned and unmanned aircraft.29
Although this chapter has been about Bayesian networks, they are just
one type of an important general class called “probabilistic graphical models.”
Markov random fields, often called Markov networks, are another member of490
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28.4

NOTES

that class in which the links between nodes are nondirectional. They wereoriginally developed to deal with problems in statistical physics, and they now
find applications in many areas including image processing, sensory perception,and brain modeling. Boltzmann machines, which were mentioned in Section
25.5.1, are instances of Markov random fields. There are also ways to interpret
other neural networks as instances of probabilistic graphical models.30

Notes
1. Marvin Minsky (ed.), “Introduction,” Semantic Information Processing, p. 14,
Cambridge, MA: MIT Press, 1968. [475]2. Judea Pearl, “Two Journeys into Human Reasoning,” in Paul Cohen and Clayton
Morrison (eds.), Artificial Intelligence: The First Century, (to appear). Online version
available at http://ftp.cs.ucla.edu/pub/stat ser/r331.pdf. [478]
3. See p. 131ff of Pearl’s foundational book about such representations: Judea Pearl,Probabilistic Reasoning Systems: Networks of Plausible Inference, San Francisco: Morgan
Kaufmann Publishers, 1988. [478]
4. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second
edition, p. 528, Upper Saddle River, NJ: Prentice Hall, 2003 [478]5. They cite I. J. Good, “A Causal Calculus (I),” The British Journal for the Philosophyof Science, Vol. XI, No. 44, pp. 305–318, 1961. [478]
6. Hans A. Bethe, “Statistical Theory of Superlattices,” Proceedings of the Royal Sociteyof London, Series A, Vol. 150, No. 871, pp. 552–575, 1935. [478]7. Judea Pearl, “Fusion, Propagation, and Structuring in Belief Networks,” Artificial
Intelligence, Vol. 29, pp. 241–288, 1986. [480]
8. This network is the subject of an interactive demonstration available as one of the online
resources for the textbook by David Poole, Alan Mackworth, and Randy Goebel,
Computational Intelligence: A Logical Approach, New York: Oxford University Press, 1998.
See the applet available from http://aispace.org/bayes/. One can add and delete nodes,change the conditional probability tables, and query the values of probabilities after making
observations about the performance of components. [481]9. For a description of some of these techniques, see Stuart Russell and Peter Norvig, op.
cit. [482]10. Malcolm Pradhan et al., “Knowledge Engineering for Large Belief Networks,”
Proceedings of the 10th Annual Conference on Uncertainty in Artificial Intelligence(UAI-94), pp. 484–490, San Francisco: Morgan Kaufmann Publishers, 1994. Compressed
PostScript version available online at
ftp://ftp.ksl.stanford.edu/pub/KSL Reports/KSL-94-47.ps.gz. [482]11. See, for example, Olivier Pourret (ed.), Patrick Naı̈m (co-ed.), and Bruce Marcot(co-ed.), Bayesian Networks: A Practical Guide to Applications, New York: John Wiley andSons, Inc., 2008. [482]12. Greg F. Cooper and Edward Herskovits, “A Bayesian Method for the Induction of
Probabilistic Networks from Data,” Machine Learning, Vol. 9, pp. 309–347, 1992. Available
online at http://www.genetics.ucla.edu/labs/sabatti/Stat180/bayesNet.pdf and at
http://bmir.stanford.edu/file asset/index.php/610/SMI-91-0355.pdf. [482]13. See, for example, the tutorial by David Heckerman and his publications, all available
from his Web site at http://research.microsoft.com/∼heckerman/, and the edited volume by491
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28

NOTES

Michael Jordan (ed.), Learning in Graphical Models, Cambridge, MA: MIT Press, 1998.
[482]
14. For details, see Peter Spirtes and Christopher Meek, “Learning Bayesian Networks with
Discrete Variables from Data,” Proceedings of the First International Conference on
Knowledge Discovery and Data Mining, pp. 294–299, San Francisco: Morgan Kaufmann
Publishers, 1995. [486]
15. Daphne Koller and Avi Pfeffer, “Probabilistic Frame-Based Systems,” Proceedings of theFifteenth National Conference on Artificial Intelligence, pp. 580–587, Menlo Park, CA:
AAAI Press, 1998. [486]
16. David Poole, “Representing Diagnostic Knowledge for Probabilistic Horn Abduction,”
Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, pp.
1129–1135, Sydney, Australia, August 1991. Reprinted in W. Hamscher, L. Console, and J.
de Kleer (eds.), Readings in Model-based Diagnosis, San Francisco: Morgan Kaufmann
Publishers, 1992. Available online at http://www.cs.ubc.ca/spider/poole/papers/ijcai91.pdf.
[486]
17. E-mail from Daphne Koller, July 27, 2008. [486]18. Nir Friedman et al., “Learning Probabilistic Relational Models,” Proceedings of theInternational Joint Conference on Artificial Intelligence, pp. 1300–1309, 1999; available
online at http://www.cs.huji.ac.il/∼nirf/Papers/FGKP1.pdf. [486]19. Eran Segal et al., “Module Networks: Identifying Regulatory Modules and Their
Condition-Specific Regulators from Gene Expression Data,” Nature Genetics, Vol. 34, pp.
166–176, 2003. Available online at
http://www.wisdom.weizmann.ac.il/∼eran/ModuleNetworks.pdf. [488]
20. Nir Friedman, “Inferring Cellular Networks Using Probabilistic Graphical Models,”
Science, Vol. 303, No. 5659, pp. 799–805, February 6, 2004. [488]21. Microsoft uses a program called ClearFlow, based on Bayesian networks, in its driving
direction Web site, http://maps.live.com/. See Eric Horvitz et al., “Prediction, Expectation,and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service,”
Proceedings Twenty-First Conference on Uncertainty in Artificial Intelligence, UAI-2005,
pp. 275–283, Edinburgh, Scotland, July 2005; available online at
http://research.microsoft.com/en-us/um/people/horvitz/horvitz traffic uai2005.pdf. Also,
Cyril Furtlehner, Jean-Marc Lasgouttes, and Arnaud De La Fortelle, “Belief Propagation
and Bethe Approximation for Traffic Prediction,” Rapport de Recherche 6144, INRIA,
March 2007; available online at
http://www-rocq.inria.fr/∼lasgoutt/publications/RR-6144.pdf. [488]22. Gregory F. Cooper et al., “Bayesian Biosurveillance of Disease Outbreaks,” Proceedingsof the Twentieth Conference on Uncertainty in Artificial Intelligence, pp. 94–103, ACM
International Conference Proceeding Series; Vol. 70, 2004; available online at
http://www.dbmi.pitt.edu/panda/papers/UAI2004.pdf. [488]23. Eric Horvitz, “Continual Computation Policies for Utility-Directed Prefetching,”
Proceedings of the Seventh ACM Conference on Information and Knowledge Management(CIKM 98), pp. 175–184, New York: ACM Press, 1998. [488]24. One such is Hugin Expert. See http://www.hugin.com/info/. [488]25. Peter Norvig, private communication, November 12, 2007. [488]26. Peter Norvig, private communication, November 21, 2008. [488]27. Stuart Russell and Peter Norvig, op. cit. [490]
28. Thomas Dean and Keiji Kanazawa, “A Model for Reasoning about Persistence andCausation,” Computational Intelligence, Vol. 5, pp. 142–150, 1989. [490]
29. Mykel J. Kochenderfer et al., “A Bayesian Approach to Aircraft Encounter Modeling,”

492
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28.4

NOTES

Proceedings of the AIAA Guidance, Navigation and Control Conference, Honolulu, Hawaii,
18–21 August 2008. [490]30. For more about graphical models, see the book by Daphne Koller and Nir Friedman,
Structured Probabilistic Models: Principles and Techniques, Cambridge, MA: MIT Press,
2009; also helpful is Kevin Murphy’s Web page, “A Brief Introduction to Graphical Models
and Bayesian Networks” at http://people.cs.ubc.ca/∼murphyk/Bayes/bnintro.html. [491]493
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28

NOTES

494
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29.1

Chapter 29

Machine Learning
Automated data-gathering techniques, together with inexpensive
mass-memory storage apparatus, have allowed the acquisition and retention ofprodigious amounts of data. Point-of-sale customer purchases, temperatureand pressure readings (along with other weather data), news feeds, financialtransactions of all sorts, Web pages, and Web interaction records are just a
few of numerous examples. But the great volume of raw data calls for efficient“data-mining” techniques for classifying, quantifying, and extracting useful
information. Machine learning methods are playing an increasingly importantrole in data analysis because they can deal with massive amounts of data. In
fact, the more data the better.
Most machine learning methods construct hypotheses from data. So (touse a classic example), if a large set of data contains several instances of swans
being white and no instances of swans being of other colors, then a machinelearning algorithm might make the inference that “all swans are white.” Suchan inference is “inductive” rather than “deductive.” Deductive inferences
follow necessarily and logically from their premisses, whereas inductive ones
are hypotheses, which are always subject to falsification by additional data.
(There may still be an undiscovered island of black swans.) Still, inductive
inferences, based on large amounts of data, are extremely useful. Indeed,science itself is based on inductive inferences.
Whereas before about 1980 machine learning (represented mainly byneural network methods) was regarded by some as on the fringes of AI,
machine learning has lately become much more central in modern AI. I havealready described one example, namely, the use of Bayesian networks that areautomatically constructed from data. Other developments, beginning aroundthe 1980s, made machine learning one of the most prominent branches of AI.
I’ll describe some of this work in this chapter.

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29Machine Learning

29.1

Memory-Based Learning

The usual AI approach to dealing with large quantities of data is to reduce theamount of it in some way. For example, a neural network is able to represent
what is important about a large amount of training data by the network’s
structure and weight values. Similarly, learning a Bayesian network from data
condenses these data into the network’s node structure and its conditional
probability tables.
However, our growing abilities to store large amounts of data in
rapid-access computer memories and to compute with these data has enabledtechniques that store and use all of the data as they are needed – without any
prior condensation whatsoever. That is, these techniques do not attempt toreduce the amount of data before it is actually used for some task. All of thenecessary reduction, for example to a decision, is performed at the time adecision must be made. I’ll describe some of these memory-based learning
methods next.
In Section 4.3, I mentioned “nearest-neighbor” methods for classifying apoint in a multidimensional space. The “k-nearest-neighbor rule,” forexample, assigns a data point to the same category as that of the majority of
the k stored data points that are closest to it. A similar technique can be used
to associate a numerical value (or set of values) to a data point. For example,
the average of the stored values associated with the k nearest neighbors can be
assigned to the new point. This version of the rule can be used in control orestimation applications. The k-nearest-neighbor rule is a prototypical example
of memory-based learning, and it evokes several questions about possible
extensions.
First, to apply the nearest-neighbor rule (as I have presented it so far),each datum must be a list of numbers – a point or vector in a
multidimensional space. So, one question is “How to represent the data sothat something like the nearest-neighbor method can be applied?” Second,
how is “distance” to be measured between data points? When the data arerepresented by points in a multidimensional space, ordinary Euclidean distanceis the natural choice. Even in that case, however, it is usual to “scale” thedimensions so that undue weight is not given to those dimensions for which
the data are more “spread out.” If the data are not represented as points in aspace, some other way of measuring data “closeness” has to be employed.
Several methods have been proposed depending on the form of the data.
Third, among the k closest data points, should closer ones influence theoutcome more than distant ones? The basic k-nearest-neighbor method can beextended by weighting the importance of data points in a manner dependingon their closeness. Usually, something called a “kernel” is used that gives
gradually diminishing weight to data points that are farther and farther away.
Fourth, what should be the value of k? How many nearby neighbors are
496
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29.1

Memory-Based Learning

we going to use in making our decision about a new piece of data? Well, withthe right kind of kernel, all of the data points can be considered. The ones
that are farthest away would simply have zero or negligible influence on thedecision. The question about what value of k to use is now replaced by aquestion about how far away the influence of the kernel should extend.
Lastly, after all of the weighted neighbors are taken into account, how do
we make a decision or assign a numerical value or values? Should it be thesame as that associated with a majority vote of the neighbors or perhaps withsome “average” of the weighted neighbors? Various versions of what are calledstatistical regression methods can be implemented depending on this choice.1
Andrew W. Moore (1965– ; Fig. 29.1) and Christopher G. Atkeson (1959–
Fig. 29.1) are among the pioneers in the development of extensions to
k-nearest-neighbor rules and the application of these extensions to several
important problems in data mining and in robot control.

Figure 29.1: Andrew Moore (left) and Chris Atkeson (right). (Photographs
courtesy of Andrew Moore and of Christopher Atkeson.)Experiments in applying these ideas to control problems are described inseveral papers. One paper2 mentions the control of a robotic device for playing
a juggling game called “devil-sticking.” A memory-based system wasdeveloped to learn how to keep the stick in play. Figure 29.2 shows aschematic of a human doing the juggling. The robotic setup is also shown withsome of the sensory and control parameters.
Besides applications in robotics and control, memory-based learning
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29Machine Learning

Figure 29.2: “Devil Stick” as played by a human and by a robotic memorybased learning system. (Illustrations from Christopher G. Atkeson, Andrew W.
Moore, and Stefan Schall, “Locally Weighted Learning for Control,” Artificial
Intelligence Review, Vol. 11, pp. 75–113, 1997. Available online at http://www.
cs.cmu.edu/∼cga/papers/air1.ps.gz.)
methods have also been used in other areas including data mining and natural
language processing.3

29.2

Case-Based Reasoning

A subfield of AI, called “Case-Based Reasoning” (CBR), can be viewed as a
generalized kind of memory-based learning. In CBR a stored library of “cases”
is used to help in the analysis, interpretation, and solution of new cases. In
medicine, for example, the diagnostic and therapeutic records for patients
constitute a library of cases; when a new case is presented, similar cases can be
retrieved from the library to help guide diagnosis and therapy. In law,previous legal precedents are used in interpretations of and decisions about
new cases (following the legal practice of stare decisis, which mandates thatcases are to be decided based on the precedents set by previous cases).
Cases that are similar to a new case can be thought of as its “neighbors”
in a generalized “space” of cases. To retrieve close neighbors, the idea of498
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29.2

Case-Based Reasoning

Figure 29.3: Janet Kolodner (left) and Edwina Rissland (right). (Photographs
courtesy of Janet Kolodner and of Edwina Rissland.)closeness in this space must be based on some measure of similarity. One ofthe pioneers in case-based reasoning, Janet Kolodner (1954– ; Fig. 29.3), a
professor of computing and cognitive science at the Georgia Institute ofTechnology, describes the process as follows:4
Good cases [for retrieval] are those that have the potential to makerelevant predictions about the new case. Retrieval is done by using
features of the new case as indexes into the case library. Cases
labeled by subsets of those features or by features that can bederived from those features are recalled.
[We then select from among these] the most promising case orcases to reason with. . . Sometimes it is appropriate to choose one
best case; sometimes a small set is needed.
When the retrieved case (or cases) is adapted to apply to a new case it mightthen (if it is successful) be revised so that the parts that might be useful for
future problem solving can be retained in the ever-growing case library.
Case-based reasoning has roots in Roger Schank’s model of dynamic
memory (see p. 209). Early work was done by two of Schank’s Ph.D. students,
Janet Kolodner and Michael Lebowitz.5 Another important source of ideas for
CBR comes from Minsky’s ideas about frames. Edwina Rissland (1947– ; Fig.
29.3), a professor at the University of Massachusetts at Amherst and anotherpioneer in CBR, writes6 that her CBR work is a direct outgrowth of her “workon ‘constrained example generation,’. . . which modeled the construction of new(counter) examples by modification of existing past ‘close’ examples(represented as frames) retrieved from a network of examples.”7 Rissland and499
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29Machine Learning

her students have made important contributions to the use of CBR in thelaw.8 She wrote me that the CBR process is sometimes summarized by thefour “R’s,” Retrieve, Reuse, Revise, and Retain.9
According to a Web page maintained by the Artificial Intelligence
Applications Institute at the University of Edinburgh, “Case-based Reasoning
is one of the most successful applied AI technologies of recent years.
Commercial and industrial applications can be developed rapidly, and existing
corporate databases can be used as knowledge sources. Helpdesks anddiagnostic systems are the most common applications.”10

29.3

Decision Trees

Next on my list of new developments in machine learning is the automaticconstruction of structures called “decision trees” from large databases.
Decision trees consist of sequences of tests for determining a category or anumerical value to assign to a data record. Decision trees are particularly well
suited for use with non-numeric as well as numeric data. For example, apersonnel database might include information about an employee’s
department, say, marketing, manufacturing, or accounting. In databaseparlance, data items like these are called “categorical” (to distinguish themfrom numerical data). In this section, I’ll describe these structures, learning
methods for automatically constructing them, and some of their applications.

29.3.1

Data Mining and Decision Trees

Data mining is the process of extracting useful information from large
databases. For example, consider a database about peoples’ credit cardbehavior. It might include payment records, average purchase amounts, late
fee charges, average balances, and so on. Appropriate data-mining methods
might reveal, among other things, that people with high late fee charges, highaverage purchases, and other identified features tended to have high averagebalances.
One important data-mining method uses data to construct decision trees.
Let’s consider a very simple database to illustrate how decision trees work.
Suppose a company, say Wal-Mart, maintains a database in which it storesinformation about households to which it had previously mailed discountcoupons for some of its products. Suppose the database has information about
the location of the household (urban, suburban, or rural), the type of house(either ranch or multistory), whether or not the household is a previous
Wal-Mart customer, and whether or not the household responded to any of its
previous coupon mailings. (Obviously, this is just a made-up illustrative
example; I don’t actually know anything about Wal-Mart’s real databases.)

500
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29.3

Decision Trees
A tabular representation of such a database would look like this:Household
3014
3015
...
5489

Locationsuburban
rural
...
urban

Type
ranch
multistory
...
ranch

Customer
yes
no
...
yes

Response
no
yes
...
no

Each row in the table is called a “record.” The items at the top of eachcolumn are called “attributes,” and the items in a column are called the“values” of the corresponding attribute.
Analysis of this database, by methods I’ll be explaining later, might revealthat the decision tree shown in Fig. 29.4 captured information about whichhouseholds responded to the coupon mailing and which did not. The tests on
attribute values are at the interior nodes of the tree (in boxes), and the results(whether or not there was a response) are at the tips (or leaves) of the tree (inovals). Such a tree might be useful for making predictions about expected
responses prior to sending out another mailing.

Figure 29.4: A decision tree for predicting responses.
Methods have been developed to construct (that is, learn) decision trees
like this one (and much larger ones too) automatically from large databases.
501
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29Machine Learning

I’ll describe some of the history and how the major methods work.

29.3.2

Constructing Decision Trees

A. EPAM
Probably the earliest system for constructing decision trees was developed in
the late 1950s by Edward Feigenbaum as part of his Ph.D. dissertation under
Herbert Simon at Carnegie Mellon University (then called Carnegie Instituteof Technology).11 His system was called EPAM, an acronym for Elementary
Perceiver and Memorizer. The goal of the research was to “explain and predictthe phenomenon of [human] verbal learning.” A standard psychologicalexperiment for testing this ability involved showing people pairs of nonsense
syllables, such as DAX-JIR and PIB-JUX. The first member of a pair wascalled a “stimulus” and the second a “response.” After seeing a number ofsuch pairs repeatedly, the subject is then shown a random stimulus and tested
on his or her ability to generate the correct response.
Pairs like these were shown to EPAM during its “learning phase.” Learning
consisted in growing what Feigenbaum called a “discrimination net” forstoring associations between stimuli and responses. The net was what wewould now call a decision tree with tests on features of the letters at theinternal nodes and responses stored at the tips or leaves of the tree. In EPAM’s“testing phase,” a nonsense stimulus syllable was filtered through the tests
down the tree until a leaf was reached where (one hopes) the correct response
was stored. A sample EPAM discrimination net is shown in Fig. 29.5. The
round nodes are tests, and the boxed nodes are responses.
Not only did EPAM successfully model the performance of humans in this“paired-associate” learning task, it also modeled forgetting. Feigenbaum
claimed that “As far as we know, [EPAM] is the first concrete demonstration ofthis type of forgetting in a learning machine.”12 EPAM was written in
Carnegie’s list-processing language, IPL-V. In fact, the list-processing featuresof languages such as IPL-V were required to write programs that could growdecision trees. Thus, it is not surprising that EPAM was the first such program.
Feigenbaum’s program is still regarded as a major contribution both totheories of human intelligence and to AI research. Simon, Feigenbaum, andothers continued work on EPAM programs, culminating in EPAM-VI, coded in
IPL-V and running on a PC.13
B. CLS
The next significant work on learning decision trees was done at Yale
University around 1960. There, psychologist Carl I. Hovland and his Ph.D.
student Earl B. (Buz) Hunt developed a computer model of human concept502
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29.3

Decision Trees

Figure 29.5: An EPAM discrimination net. (From Edward A. Feigenbaum,“An Information Processing Theory of Verbal Learning,” Ph.D. dissertation,
Carnegie Institute of Technology, p. 99, 1959, published as Report P-1817 by
The RAND Corporation, Santa Monica, CA, October 9, 1959. Used with permission of Edward Feigenbaum.)learning.14 After Hovland succumbed to cancer in 1961, Hunt continued workon concept learning and collaborated with Janet Marin and Philip Stone indeveloping a series of decision-tree learning programs called CLS, an acronym
for Concept Learning System.15 Hunt and his colleagues acknowledged therelated prior work on EPAM.
For AI purposes at least, the CLS systems were soon eclipsed by otherdecision-tree learning systems, namely, ID3, CART, and related programs. I’ll
describe how ID3 works as a way of explaining the main ideas behind these
programs.
C. ID3
J. Ross Quinlan (1943– ; Fig. 29.6) developed ID3,16 an acronym for Iterative
Dichotomizer, in the late 1970s while he was on sabbatical leave (from the
University of Sydney) at Stanford. (The name derived from the fact that the
program constructed decision trees by iteratively dividing sets of data records
until they could be classified into one of two distinct categories. Later versions
allowed classification into more than two categories, but the “D” persisted in503
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29Machine Learning

the name.) Quinlan had previously been a Ph.D. student (the first, actually)in the Computer Science Department at the University of Washington,
working under Earl Hunt. Quinlan explained the genesis of ID3 in an e-mailnote to me:17
I sat in on a course given by Donald Michie [also visiting Stanford
at that time] and became intrigued with a task he proposed,
namely, learning a rule for deciding the result of a simple chessendgame. ID3 started out as a recoding of Buz’s [that is, Earl B.
Hunt’s] CLS, but I changed some of the innards (such as thecriterion for splitting a set of cases) and incorporated the iterative
approach that allowed ID3 to handle the then-enormous set of
29,000 training cases.

Figure 29.6: J. Ross Quinlan. (Photograph courtesy of Ross Quinlan.)Here, in brief, is how ID3 would proceed to construct a decision tree forpredicting the value of the response attribute using my fictitious Wal-Mart
database. First, ID3 would look for that single attribute to use as the “best”
test in distinguishing between those data records having the value yes for the
response attribute from those having the value no. (I will have more to say
about how “best” is defined momentarily.) No single test separates the data
perfectly, but let us suppose that location does better than the others. Afterall, in this example all of the data records having value rural for the attribute
location have the value yes for the response attribute, and none of those have504
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29.3

Decision Trees

the value no. Let’s assume that the preponderance (but not all) of the data
records having the value suburban have the value yes for the response attributeand that the preponderance (but again not all) of the data records having thevalue urban have the value no for the response attribute. Thus, the location
attribute does a pretty good (but imperfect) job of separating data records
with respect to the response attribute. A test for the value of the location
attribute would thus be used as the first test in the decision tree being
constructed.
So far then, we would have split the database into three subsets, two of
which have data records with mixed values for the response attribute. ID3
would then apply the same splitting technique to each of these two
mixed-value subsets, finding for each one of them the best next feature to use
as a test. In this simple and rather nonrealistic example, the two tests thatwould be used, namely, type and customer, would each produce “pure” splits(that is, ones with no mixed values), and we would end up with the decision
tree already shown in Fig. 29.4.
If the splits were not pure or not otherwise acceptable, however, ID3
would have gone on selecting tests on the resulting subsets of databases until
the splits did give either pure or acceptable results.
The choice of which attribute to test on is critical in producing usefuldecision trees. In his original ID3 program,18 Quinlan used a measure related
to the “accuracy” of the resulting split in determining which attribute to use
for testing. In later work, he used a measure called “information gain,”19
whose precise definition I won’t go into here except to say that it is that
attribute whose values convey the most “information” about the categorization
being sought. Quinlan used Claude Shannon’s definition for measuring theamount of information.20 Still later, he used a normalized measure of
information gain in order not to bias in favor of tests with many outcomes.21
In my discussion of expert systems in Section 18.2, I mentioned that they
were based on IF–THEN rules. Interest in symbolically based machinelearning by Quinlan and others was mainly directed at learning these sorts ofrules from data. It is quite easy to construct rules from a decision tree by
tracing down the tests to generate the “IF” part and using what lies at thetips for the “THEN” part. For example, in the Wal-Mart database example,
we could derive the following rules from the decision tree:
IF (location = suburban) and (type = ranch), THEN (response = no)
IF (location = suburban) and (type = multi-story), THEN (response = yes)
IF (location = rural), THEN (response = yes)
IF (location = urban) and (customer = yes), THEN (response = no)
IF (location = urban) and (customer = no), THEN (response = yes)
505
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29Machine Learning

In Quinlan’s work at Stanford, ID3 was able to generate rather large
decision trees, and thus rule sets, for predicting whether certain endgame chesspositions would end in a loss for black. For a problem of this type suggested
by Donald Michie, ID3 used twenty-five attributes (involving features of thepositions of pieces on the board) and a database of 29,236 different piecearrangements to construct a tree with 393 nodes whose predictions were
99.74% correct.22
One problem that must be avoided in constructing decision trees is that of“overfitting,” that is, selecting tests based on so little data that the test results
don’t capture meaningful relationships in the data as a whole. No matter how
large the original database, if a succession of tests eventually produces a
subset that is still not pure but has been reduced to too few data records, any
attempt to split that subset would overfit the data and thus not be useful. For
that reason, decision-tree learning techniques typically halt tree construction
just before data subsets would have too few records but would still give
acceptable results.
D. C4.5, CART, and Successors
Quinlan continued his work on decision-tree-constructing systems, improving
their power and applicability. He told me that “ID3 was pretty simple – about
600 lines of PASCAL.”23 His system C4.5 (which had about 9,000 lines of C)
could work with databases whose attributes had continuous numerical values
in addition to categorical ones. It could even deal with databases some ofwhose records had missing values for some of their attributes. Finally, it had
methods for improving overall performance by pruning away some parts of thetree and for simplifying IF–THEN rules derived from trees.24 A commercial
company Quinlan founded in 1983 markets an improved version of C4.5 called
C5.0 (along with a Windows version called See5).25 Donald Michie also
founded a company,26 which independently developed a commercial version of
ID3 called ACLS.
One of the significant developments in machine learning during thisperiod was a fruitful collaboration between AI people and statisticians who
were doing foundational as well as applied research on classification,
estimation, and prediction. Each group has learned from the other, andmachine learning is much richer for it. Although several people were involvedin this collaboration, I might mention in particular the Stanford statistician
Jerome Friedman (1939– ), who began working with some Stanford AI Ph.D.
students in the 1990s. Following his earlier work on decision-tree
construction,27 Friedman, in collaboration with Leo Breiman, Richard Olshen,
and Charles Stone, had helped develop a system called CART, an acronym forClassification and Regression Trees.28 CART shares many features with C4.5
(and, in fact, C4.5 used CART’s techniques for dealing with numeric
attributes). At the time of this writing, the latest version, CART 5, is available
506
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29.4

Neural Networks

from a commercial company.29
Systems for learning decision trees have been applied to a wide variety ofdata-mining problems.
E. Inductive Logic Programming
Expressed in the language of propositional logic, the IF–THEN rules produced
from decision trees have the form P1 ∧ P2 ∧ . . . PN → Q. The P’s and Q’s arepropositions with no internal structure. Earlier, I spoke of the predicate
calculus in which propositions, called predicates, had internal arguments. In
that language, one could have much more expressive rules such as∀(x, y, z)[Father(x, y) ∧ Sibling(z, y) → Father(x, z)], for example. Several
techniques have been developed to learn these types of “relational” rules fromdatabases and from other “background knowledge.” (I mentioned a related
topic before, namely, learning “probabilistic relational models,” which areversions of Bayesian networks that permitted predicates with variables.) Oneof the early systems for learning relational rules was developed by Quinlan andcalled FOIL.30 Because the rules learned have the same form as do statementsin the computer language PROLOG (a language based on logic), the fielddevoted to learning these rules is called “Inductive Logic Programming”(ILP). Although ILP methods involve logical apparatus too complex for me totry to explain here, some of them bear a close relationship to decision-tree
construction.31 There are several applications of ILP, including learning
relational rules for drug activity, for protein secondary structure, and for
finite-element mesh design. These are all examples of what can be called“relational data mining.”32

29.4

Neural Networks

During the 1960s, neural net researchers employed various methods forchanging a network’s adjustable weights so that the entire network madeappropriate output responses to a set of “training” inputs. For example, FrankRosenblatt at Cornell adjusted weight values in the final layer of what hecalled the three-layer alpha-perceptron. Bill Ridgway (one of Bernard
Widrow’s Stanford students) adjusted weights in the first layer of what hecalled a MADALINE. We had a similar scheme for adjusting weights in thefirst layer of the MINOS II and MINOS III neural network machines at SRI.
Others used various statistical techniques to set weight values. But what
stymied us all was how to change weights in more than one layer of multilayer
networks. (I recall Charles Rosen, the leader of our group, sitting in his officewith yellow quadrille tablets hand-simulating his ever-inventive schemes formaking weight changes; none seemed to work out.)

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29Machine Learning

29.4.1The Backprop Algorithm

That problem was solved in the mid-1980s by the invention of a techniquecalled “back propagation” (backprop for short) introduced by David
Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams.33 The basic idea
behind backprop is simple, but the mathematics (which I’ll skip) is rather
complicated. In response to an error in the network’s output, backprop makes
small adjustments in all of the weights so as to reduce that error. It can be
regarded as a hill-climbing (or rather hill-descending) method – searching forlow values of error over the landscape of weights. But rather than actuallytrying out all possible small weight changes and deciding on that set of themthat corresponds to the steepest descent downhill, backprop uses calculus toprecompute the best set of weight changes.
Readers who remember a bit of college (or perhaps high school) calculus
will have no trouble recalling that it can be used to calculate the slope of acurve or surface. The error in the output of a neural network can be thoughtof as a function of the network’s weights, that is, a surface in “weight space.”
This function can be written down and “differentiated” (an operation in
calculus) with respect to the weights to yield the set of weight changes thatwill take us downhill in the steepest direction. The problem with
implementing this idea in a straightforward fashion for neural networks lies in
the fact that these networks have “thresholds,” whose effect is to populate theerror surface with abrupt “cliffs.” (The outputs of a network with thresholds
can change from a 1 to a 0 or from a 0 to a 1 with infinitesimally smallchanges in some of the weight values.) Calculus operations require smoothly
changing surfaces and are frustrated by cliffs.
Rumelhart and colleagues dealt with this problem by replacing thethresholds with components whose outputs can only change smoothly, eventhough they change quite steeply enough for the network to do approximately
the same thing as a network with thresholds. With these replacements,
calculus can be used to propagate the error function backward (from output toinput) through the network to calculate the best set of changes to the weightvalues in all of the network’s layers. Although this process of zeroing in on
acceptable weight values is slow, it has been used with impressive results formany neural-network learning problems.
Why didn’t we think of that? Actually, some people apparently did thinkof a similar idea before Rumelhart and his colleagues did. The earliest wasprobably Arthur E. Bryson Jr. and Y. C. Ho who used iterative gradientmethods for solving Euler–Lagrange equations.34 Paul Werbos, in his Harvard
Ph.D. thesis, also proposed back-propagating errors to train multilayer neuralnetworks.35
As with all local search techniques, backprop might get stuck on one of
the local minima of the error surface. Of course, the learning process can be
repeated, starting with different initial values of the weights, to attempt to find508
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29.4

Neural Networks

a lower (or perhaps the lowest) error value. In any case, the backprop method
still is, as Laveen Kanal wrote in 1993, “probably the most widely usedgeneral procedure for training neural networks for pattern classification.”36
Neural network learning methods have been applied in a variety of areas
including aircraft control, credit card fraud detection, vending machinecurrency recognition, and data mining. I’ll describe a couple of otherapplications next.

29.4.2

NETtalk

One very interesting application of the backprop learning method wasdeveloped by Terrence J. Sejnowski (1947– ) and Charles Rosenberg (1961– ).
They taught a neural network to talk!37 In one of their experiments, theirsystem, called NETtalk, learned to “read” text that was transcribed frominformal, continuous speech of a six-year-old child and produced acoustic
output (that sounded remarkably like that of a child). (You can listen to an
audio demo at http://www.cnl.salk.edu/ParallelNetsPronounce/.) The
network structure is shown in Fig. 29.7.
The network had 203 input units designed to encode a string of sevenletters. Text was streamed through these seven units letter by letter. There
were 80 “hidden units” that were connected to the inputs by adjustableweights. It was hoped that the hidden units would “form internal
representations that were appropriate for solving the mapping problem ofletters to phonemes.” There were 26 output units that were supposed to
produce coded versions of phonemes, the basic units of speech sounds. The
output units were connected to the hidden units by additional adjustableweights. (Altogether, there were 18,629 adjustable weights.) Finally, thephonemic codes were fed to a commercial speech synthesizer to produce
audible output.
The network was trained by comparing, at every time step, the phonemiccode at the output units against what that code should have been for the text
input at that time step. Backprop was used to modify the weights in a waythat tended to reduce this error. The authors claim that “it proved possible totrain a network with a seven letter window in a few days.” (Remember thatcomputers were much slower in 1987.) They concluded that “overall, theintelligibility of the speech was quite good” and that “the more words the
network learns, the better it is at generalizing and correctly pronouncing new
words.” After training on a corpus of 1,024 words, the network “was tested[without further training] on a 439 word continuation from the same speaker.
The performance was 78%, which indicates that much of the learning wastransferred to novel words even after a small sample of English words.” In
addition to the specific network shown in Fig. 29.7, experiments were alsodone on networks with more hidden units and with two layers of hidden units.
In general, the larger networks performed better.
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29Machine Learning

Figure 29.7: Terrance Sejnowski (top) and the neural network used in NETtalk(bottom). (Photograph and illustration courtesy of Terrance Sejnowski.)

29.4.3

ALVINN

Another neural network application, this one for steering a van, was developed
by Dean Pomerleau, a Ph.D. student at Carnegie Mellon University.38 The
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29.4

Neural Networks

system, which included the van, a TV camera for looking at the road ahead,
and interface apparatus, was called ALVINN, an acronym for Autonomous
Land Vehicle in a Neural Network. ALVINN used the CMU Navlab vehicle,
which was built on a commercial van chassis with hydraulic drive and electricsteering. According to a CMU paper, “Computers can steer and drive the van
by electric and hydraulic servos, or a human driver can take control to drive toa test site or to override the computer.”39 A picture of Navlab is shown in Fig.
29.8.

Figure 29.8: CMU’s Navlab vehicle used by ALVINN. (Photograph courtesy ofCarnegie Mellon University.)
The input to ALVINN’s neural network was a low-resolution 30 × 32 arrayof gray-scale image intensity values produced by a video camera mounted on
top of the van. Each of these 960 inputs was connected to each of four hidden
units through adjustable weights. The hidden units, in turn, were connected
to a left-to-right line of 30 output units through adjustable weights. The
output units controlled the van’s steering mechanism as follows:40
The centermost output unit represents the “travel straight ahead”
condition, while units to the left and right of center represent
successively sharper left and right turns. The units on the extreme
left and right of the output vector represent turns with a 20 m
radius to the left and right respectively, and the units in between
represent turns which decrease linearly in their curvature down to
the “straight ahead” middle unit. . .
The steering direction dictated by the network is taken to be the511
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29Machine Learning
center of mass of the“hill”of activation surrounding the output unit
with the highest activation level. Using the center of mass ofactivation instead of the most active output unit when determining
the direction to steer permits finer steering corrections, thus
improving ALVINN’s driving accuracy.

Figure 29.9 shows the arrangement of the network and a typical
low-resolution road image as presented to the network.

Figure 29.9: The ALVINN network (left) and a typical road image (right).
(From Dean A. Pomerleau, “Neural Network Vision for Robot Driving,” Michael
Arbib (ed.), The Handbook of Brain Theory and Neural Networks, Cambridge, MA: MIT Press, 1995. A version of this paper is available online
at http://www.ri.cmu.edu/pub files/pub2/pomerleau dean 1995 1/pomerleaudean 1995 1.pdf.)
There were various versions of ALVINN. In one, training of the network
was “on-the-fly,” meaning that the network was trained in real time as the vanwas steered by a human driver along various roads and paths. The desired
steering angle was the one selected by the driver, and the network weights
were adjusted by backprop to attempt to mimic the driver’s performance. One
problem with this method was that the network was never exposed to possible“going-off-the-road” images. Simulations of what such images would look like(labeled by what the steering angle should be in those cases) were added tothe training set.
In summarizing a typical test of ALVINN’s performance, Pomerleau
wrote41
Over three runs, with the network driving at 5 miles per houralong the 100 meter test section of road, the average position of the512
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29.5

Unsupervised Learning
vehicle was 1.6 cm right of center, with a standard deviation of 7.2
cm. Under human control, the average position of the vehicle was
4.0 cm right of center, with a standard deviation of 5.47 cm.

Carnegie Mellon’s Robotics Institute continued (and still continues) towork on autonomous vehicles, although the neural-network approach toimage-guided steering was replaced by more robust computer-visionalgorithms. Their 1995 visual perception system RALPH (an acronym for
Rapidly Adapting Lateral Position Handler) used special image-processing
routines to determine road boundary curvature. According toPomerleau,42 “RALPH has been able to locate the road and steer
autonomously on a wide variety of road types under many different conditions.
RALPH has driven our Navlab 5 testbed vehicle over 3000 miles on roads
ranging from single lane bike paths, to rural highways, to interstate freeways.”
In the summer of 1995, one of their specially outfitted vehicles, a 1990
Pontiac Trans Sport (Navlab 5) donated by Delco Electronics, steered
autonomously (using RALPH) for 2,797 of the 2,849 miles from Pittsburgh,
PA to San Diego, CA. (Only the steering was autonomous – Pomerleau and
Ph.D. student Todd Jochem handled the throttle and brake.) The average
speed was above 60 miles per hour.43

29.5

Unsupervised Learning

The decision tree and neural network learning methods described so far in thischapter are examples of “supervised learning,” a type of learning in which one
attempts to learn to classify data from a large sample of training data whoseclassifications are known. The “supervision” that directs learning in thesesystems involves informing the system about the classification of each datum
in the training set. Yet, it is sometimes possible to construct usefulclassifications of data based just on the data alone. Techniques for doing sofall under the heading of “unsupervised learning.”
Recall that in Section 4.3 I showed a diagram (Fig. 4.11) in which data to
be classified were represented by points in a two-dimensional “feature space.”
The coordinates of the points corresponded to the values of two numericallyvalued features, f1 and f2 , of the data. In Fig. 4.11, the category of each point
was indicated by small squares for points belonging to one category and small
circles for points belong to another category. Because the points were thuslabeled, they could be used as training examples for a supervised learning
procedure.
But suppose we have a set of unlabeled sample points, such as thoseshown in Fig. 29.10. Can anything be learned from data of that sort? Byvisual inspection, we see that the points seem to be arranged in three clusters.
Perhaps each cluster contains points that could be thought to belong to the513
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29Machine Learning

same category. So, if we could automatically process data samples to identify
clusters and the boundaries between them, we would have a method of
unsupervised learning.

Figure 29.10: Unlabeled points in a feature space.
AI researchers have used several methods for identifying clusters of
training samples. A popular one, and one that is easy to explain, is theso-called k-means method. It works by repeating over and over the following
steps:
1. Install, perhaps at random locations, some number, say k, of “cluster
seekers” in the space of samples.
2. For each of these cluster seekers, group together those training samples
that are closer to it than to any other cluster seekers.
3. Compute the centroid (the “center of gravity”) of each of these groups ofsamples.

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29.6

Reinforcement Learning

4. Move each of the cluster seekers to the centroid of its corresponding
group.
5. Repeat these steps until none of the cluster seekers needs to be moved
again.
At the end of this process, the cluster seekers will all be at the centroids ofgroups of training samples that can be considered to be clusters or separate
categories of data. Now to classify some new data point not in the training
set, we simply compute to which cluster seeker it is closest. The processdepends, of course, on being able to guess the number of clusters, k. Methods
for doing so generally involve adjusting the number of them so that points
within clusters are closer together than the distances between clusters.
Statisticians and others have developed several methods for clustering
data, including variations related to the k-means method. One prominenttechnique, AutoClass, was developed by Peter Cheeseman and colleagues atNASA.44 According to a Web site about AutoClass,45
AutoClass takes a database of cases described by a combination ofreal and discrete valued attributes, and automatically finds thenatural classes in that data. It does not need to be told how many
classes are present or what they look like – it extracts thisinformation from the data itself. The classes are describedprobabilistically, so that an object can have partial membership inthe different classes, and the class definitions can overlap.
AutoClass is famous for having discovered a new class of infrared stars. It has
also discovered new classes of proteins, introns, and other patterns in
DNA/protein sequence data.
There are even techniques that can be applied to non-numeric data.
Statisticians group all of these methods (numeric and non-numeric) under thegeneral heading of “cluster analysis.” A good overview can be found in the
online Electronic Textbook StatSoft at
http://www.statsoft.com/textbook/stcluan.html. The textbook by Duda,
Hart, and Stork has a thorough discussion of unsupervised learning (as well asother topics in data classification).46

29.6

Reinforcement Learning

29.6.1

Learning Optimal Policies

There is another style of learning that lies somewhat in between the
supervised and unsupervised varieties. An example would be learning which of
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29Machine Learning

several possible actions a robot, say, should execute at every stage in an
ongoing sequence of experiences given only what final result of all of its
actions. An extreme case would be learning to play excellent chess given only
information about a win or a loss at the end of play. No system has yet been
built that can learn to play chess that way, but it is possible for a program tolearn to play backgammon that way and to learn to perform other interestingtasks, such as controlling the flight of helicopters. Borrowing terms frompsychological learning theory, we can call the win or loss information (or ingeneral the good-result or bad-result information) a “reward” or a“reinforcement,” and this style of learning is called “reinforcement learning” or(sometimes) “trial-and-error learning.”
Reinforcement learning has a long and varied history. The psychologist
Edward L. Thorndike (1874–1949) studied this style of learning in animals.47
In their book Reinforcement Learning: An Introduction,48 Richard S. Sutton(1957– ; Fig. 29.11) and Andrew G. Barto (1948– ; Fig. 29.11), two of the
field’s pioneers, mention some additional historical milestones, including
Arthur Samuel’s method for learning evaluation functions in checkers, the useof Richard Bellman’s dynamic programming techniques in optimal control,John Andreae’s trial-and-error learning system STeLLA,49 Donald Michie’s
learning systems for tic-tac-toe (MENACE50 ) and pole-balancing (BOXES51 ),and A. Harry Klopf’s work on “hedonistic neurons.”52 Reinforcement learning
is another one of those subdisciplines of AI that has become highly technicaland multibranched. I’ll attempt a gentle and nonmathematical description of
how it works.

Figure 29.11: Andrew Barto (left) and Richard Sutton (right). (Photographs
courtesy of Andrew Barto and of Richard Sutton.)

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29.6

Reinforcement Learning

In its simplest setting, reinforcement learning is about learning how totraverse a collection of states, going from one state to another and so on, toreach a state in which a reward is obtained. The problem is much like one thata rat faces in learning how to run a maze (or one that a robot faces in learning
how to carry out a task). In fact, let us use a maze example to describe someof the aspects of reinforcement learning. A typical maze is shown in Fig. 29.12.

Figure 29.12: A maze.
The rat’s problem is to go from its starting position to the cheese at thegoal position. The gray dots in the figure are meant to depict situations thatthe rat might find itself in and recognize. In reinforcement learningterminology, these situations are called “states.” At each state, the rat canselect from among, say, four actions, namely, turn left, turn right, go forward,or go back. Depending on the state, only some of the actions are possible –one cannot go forward when up against a dead end for example. Each possible
action takes the rat from one state to an adjacent one in the maze. The
collection of states and the actions that link them can be thought of as a
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29Machine Learning

graph, similar to those I discussed when I talked about search methods.
So as not to stray too far from what is known about real rats runningmazes, let us switch now to describe how a fictional “robotic rat” might learn
how to run this maze. The main problem for the robot is that it starts out by
not having a map of the maze nor having any idea about the effects of its
actions. That is, for any given state that it finds itself in, it does not know
which next states would result for the various actions it could take in thatstate. For if it did have such a map, say one represented by a graph, it could
search the graph (using a method like A∗ ) to find a path to the goal node.
One way to proceed would be to attempt to learn a graph of states and theirconnections by trial-and-error methods and then to use graph-searching
methods to figure out how to navigate the maze.
An alternative, and the one used by most reinforcement learning methods,
involves naming all of the states that the robot encounters as it wanders
randomly in search of the goal. (We assume that eventually it does reach thegoal.) In reinforcement learning terminology, a “policy” for running the mazeassociates some single action with each named state. A best or “optimal
policy” would associate with each state that action that would lead to ashortest (or otherwise least costly) path through the maze. Reinforcement
learning is about learning the best policy, or, at least, good policies.
One method for learning a policy involves associating a “valuation”
number with every possible action at each state and then adjusting thesenumbers (based on experience) until they point the way toward the goal. Thismethod is called “Q-learning” and was originally suggested by Christopher
Watkins (1959– ) in his Cambridge University Ph.D. thesis.53 The robot
begins its learning process by assigning a name to the state in which it begins
and by assigning randomly selected valuation numbers to every action it cantake in that state. The learning process will expand this table by assigningnames and valuation numbers to all of the actions it can take in every newstate encountered. (We assume that the robot remembers, in its table, thenames of all the states it has already visited in its learning process and candistinguish these from new states.) The robot’s initial state, with a randomly
selected valuation number assigned to its only action possible, is shown in theleft-hand sketch of Fig. 29.13. At every stage of the learning process, the robot
takes that action having the highest valuation number. Because there is only
one action in the robot’s initial state, it takes that action, finds itself in a newstate, and assigns random valuation numbers to the actions possible in thatnew state. This step is shown in the middle sketch of Fig. 29.13. Now comes
the key step in learning. Because the robot now “knows” that it can reach a
new state having actions whose highest valuation number is 6, it updates thevaluation number, namely, 3, of the action leading to that state by adjusting it
to a number more consistent with being able now to take an action that it
imagines is worth 6. To account for the “cost” of its just-completed action, theadjustment of 3 does not go all the way to 6 but just to 5, say. The result is

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29.6

Reinforcement Learning

shown in the right-hand sketch of Fig. 29.13 in which the adjusted valuation isshown a bit larger than the other numbers and shaded.

Figure 29.13: Initial stages of the Q-learning process.
This process continues. In each state, take that action whose valuation
number is largest and then adjust that valuation number by making its value
closer to the value of the action with the highest valuation number in the state
just entered. And, even though the process starts with randomly selectedvaluation numbers, eventually the trial-and-error process will stumble into thegoal state where a high “reward” will be obtained. At that stage, the action
just taken, which led to that reward, has its valuation number raised to thesame value (or maybe just a little bit less) than the value of the reward. I
illustrate this step in Fig. 29.14. The sketch on the left of the figure showssome of the states and action valuations at the time the robot takes the actionthat achieves the goal. In the sketch on the right of the figure, I show theadjusted valuation (shaded) for that goal-achieving action. Now, for the firsttime, an action valuation is based on getting a reward rather than being set
randomly. If the robot ever finds itself in the state adjacent to the goal stateagain, it will certainly take the same action. More importantly, when it
reaches this penultimate state in a subsequent experience, it will propagate
this reward-based value backward.
I illustrate how backward propagation works in Fig. 29.15. Suppose, inthe sketch on the left, the robot finds itself in the state marked by an arrow.
From that state, it takes that action with the largest valuation, which leads it
to a state adjacent to the goal. The action with the largest valuation leadingout of that state has a valuation of 99, so the valuation of the action just taken
is changed from 11 to 98, as shown in the sketch on the right. Increasing thevaluations of actions in states close to the goal by backward propagation, in
effect, makes those states intrinsically “rewarding” just as if they were goalstates themselves.
The astute reader may complain that I have cleverly set the “random”
valuation numbers to values that would lead to the goal once the robot gets tostates close to the goal. What if these values were such, as they most probably
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29Machine Learning

Figure 29.14: Stages leading to the goal.

Figure 29.15: Backward propagation of goal-achieving action valuations.
would be, that, upon getting close, the robot wanders away from the nearly
achieved goal? If the valuation numbers are adjusted as I prescribed, always
taking into account the cost of a move, a little thought will convince one thateventually the numbers will be such as to force the robot toward the goal, withall other avenues eventually being closed off.
With continued experience, the valuations of actions involved in achieving
the goal gradually propagate backward from the goal. Eventually, after muchtrial-and-error experience (and with some “reasonable” assumptions), thevalues will converge to those that implement an optimal policy, that is, onethat always gets the robot to the goal in the most efficient manner.
Most versions of reinforcement learning have the following elaborations:• Rewards might be given at more than one of the states. That is, there is
not necessarily a single goal state but many states that might contributeto reward. Rewards are represented by numerical values, which could bepositive (true “rewards”), zero, or negative (“punishments”).
520
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29.6

Reinforcement Learning• Rather than attempting to find a policy that corresponds to an optimal
path to a single goal state, one tries to learn policies that maximize theamount of reward expected over time. Usually in learning a policy,
rewards that are anticipated in the distant future are “discounted,” thatis, they don’t count as much as rewards expected more immediately.
• Any given action taken at a state might not always lead to the samestate. One could attempt to learn the probabilities that certain actions
taken in a state lead to which other states, and some reinforcement
learning methods, such as “prioritized sweeping,”54 do that. The
Q-learning process avoids the need to learn these probabilities explicitly
because, whatever they are, they (along with rewards) appropriately
affect the values that the learning process assigns to state–action pairs.
• As a further complication, it might be that the robot has only imperfectknowledge of what state it is in because its sensory apparatus is not
sufficiently accurate or informative. In that case, the actual state thatthe robot is in is said to be “hidden” from it, which adds additional
complications to the problem of learning an optimal policy.
With these elaborations, the problem becomes one of what is called a“Markov Decision Process” (MDP). With imperfect state knowledge, it iscalled a “Partially-Observable Markov Decision Process” (POMDP). MDPs
and POMDPs have been well studied by people in control theory as well as inAI.55
I can use the robot maze example to mention several things that areimportant in the use of reinforcement learning in practical applications. First,
I assumed that the robot’s random exploration eventually would land it in thegoal state. In complex problems, the chance of randomly achieving a goal (orother rewards) might be slim to none. Breaking the problem down into ahierarchy of subproblems in which rewards are more easily obtained issometimes used to speed up learning. Additionally, “shaping” strategies canbe used in which the robot is first placed in a situation sufficiently close to thegoal that random exploration will find the goal. Then, after some actions close
to the goal have been assigned goal-relevant evaluations, the starting
situations can be gradually moved farther and farther from the goal.
Alternatively, hints might be given, perhaps in the form of intermediate
rewards given to let the robot know that it is doing well so far. Strategies suchas these are used in teaching skills to humans and animals.
Another problem concerns the tradeoff between “exploiting” an alreadylearned policy versus “exploring” to find better policies. It is often the casethat a set of action valuations obtained early in the learning process might not
be the best set possible. To learn a better set, the robot must be encouraged
in some way to strike out randomly away from a known policy to lock on to abetter one. Finally, many problems might have “state spaces” so large thatthe entire set of all of the states and their actions and valuations cannot be
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29Machine Learning

explicitly listed in a table like the one I assumed for the robot maze problem.
In that case, the valuations of actions that can be taken in a state must be
computed rather than stored. I’ll show an example of how that might be donein the next few pages.

29.6.2

TD-GAMMON

One of the most impressive demonstrations of the power of machine-learning
methods is the TD-GAMMON system developed by Gerald Tesauro at IBM.56
Versions of TD-GAMMON learned to play excellent backgammon after playing
against themselves during millions of games. TD-GAMMON used a combinationof neural net learning and a type of reinforcement learning called “temporal
difference learning” (which explains the prefix TD).
TD-GAMMON’s neural network consisted of three layers. In one version
there were 198 input units, 40 hidden units, and 4 output units. Each of theoutput units could have an output value between 0 and 1. (Instead ofthreshold units, the network had the kinds of components I talked about
earlier, namely, those whose outputs changed smoothly, but still abruptly,
between 0 and 1.) Each of the outputs was charged with the task of estimatinga probability of a particular outcome of the game. The four possible outcomesconsidered were white wins, white gammons, black wins, or black gammons.
The input units were coded to represent the configuration of pieces on theboard. The values of the four outputs were combined to yield a number giving
the estimated “value” of a board position from white’s point of view.

I’ll describe how the network learned in a moment. First, here is how the
network was used to select a move. (I’m assuming here that the reader has
some familiarity with backgammon, but my description should make senseeven for those who do not.) At each stage of play, the dice are thrown, and theprogram considers all of the possible moves that it might make given thatthrow of the dice. The network computes the value of each possible resulting
board, and the program selects the move producing the board with the bestvalue (which is the highest value when it is white’s move and the lowest value
when it is black’s move).
Now, here’s how the network learns: For each board position encounteredduring actual play, the network’s weights are adjusted, using backprop, so thatthe value computed for that board position is closer to the value computed forthe temporally next board position (and thus we see why the term “temporal
difference” arises). The network starts with randomly selected weight values,
so the moves early in the learning process, as well as the weight adjustments,
are random. But eventually, even randomly selected moves result in a win for
one of the players. After a win occurs, the four probability values are thenknown for sure – one of them is “1,” and the rest are “0.” The network’s
weights can then be adjusted so that the value of the penultimate board ismade closer to the value of this final, winning board position. As in all
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29.6

Reinforcement Learning

reinforcement learning procedures, values are gradually propagated backwardfrom the end of the game toward the starting position. After millions ofgames, the network weights take on values that result in expert play. In
commenting on a version of TD-GAMMON that uses search in addition tolearning, Sutton and Barto wrote57
TD-GAMMON 3.0 appears to be at, or very near, the playing

strength of the best human players in the world. It may already be
the world champion. These programs have also already changed
the way the best human players play the game. For example,
TD-GAMMON learned to play certain opening positions differently
than was the convention among the best human players. Based on
TD-GAMMON’s success and further analysis, the best humanplayers now play these positions as TD-GAMMON does.

29.6.3

Other Applications

There are probably hundreds of important applications of reinforcement
learning methods. A typical, as well as dramatic, example is the work of
Andrew Ng (1976– ) and his group at Stanford on learning to perform
aerobatic helicopter maneuvers.58 Some photographs of a model helicopterthat has learned to “roll” are shown in Fig. 29.16. Other applications havebeen in elevator dispatching, job-shop scheduling, managing power
consumption, and four-legged walking robots.
As a final comment about reinforcement learning, it is interesting toobserve that part of the technology of machine learning, a part whose namewas borrowed from psychology, now pays back its debt by providing atheoretical framework for how animal brains learn at the neurophysiologicallevel. In an article in The Journal of Neuroscience, Christopher H. Donahueand Hyojung Seo wrote59
To make effective decisions while navigating uncertain
environments, animals must develop the ability to accurately
predict the consequences of their actions. Reinforcement learning
has emerged as a key theoretical paradigm for understanding how
animals accomplish this feat. . .
In addition to successfully predicting the animal’s choice behavior,
the reinforcement learning model has been successfully used toelucidate the function of the basal ganglia in goal-directedbehavior. Dopaminergic neurons in the ventral tegmental area andthe substantia nigra have been shown to encode areward-prediction error, which is used to improve the outcomes ofan animal’s future choices. Another study in monkeys engaged in afree-choice task showed that the activity of striatal neurons is
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29Machine Learning

Figure 29.16: Andrew Ng (top) and his model helicopter during a roll maneuver(bottom). (Photographs courtesy of Andrew Ng. Helicopter photographs fromhttp://heli.stanford.edu/images/roll mosaic.eps.)
correlated with action values, which were estimated by integrating
the previous outcome history associated with each action.

29.7

Enhancements

Many of the machine learning methods I have mentioned can be enhanced invarious ways. Some of these are based on work by statisticians and others bypeople working on what is called “computational learning theory.” Onetechnique, called “bagging” (an acronym for bootstrap aggregating) is due toProfessor Leo Breiman of the University of California, Berkeley.60 For
classification problems, bagging works by combining the outputs of a number,say m, of separate classifiers. Each classifier is trained by using a different
subset of the original training set. These subsets are obtained from the originalby randomly selecting (with replacement) some of its examples. (Statisticians
call these samples “bootstrap samples.”) After each of the m classifiers istrained, final classification is made by a majority vote. The technique can beapplied independently of the kind of individual classifier used – neural524
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29.7

Enhancements

network, decision tree, nearest-neighbor, or what have you. Bagging can also
be used for the problem of associating a number (rather than a category) withan example. In that case, outputs are averaged rather than participating in avote. The voting and averaging operations help avoid overfitting the data andthus yield better performance than would have been obtained with oneclassifier trained on all of the data. [One wonders how the performance of the
1960s MADALINE neural network (see p. 98) might have been improved hadeach of its threshold units been trained on bootstrap samples.]A related idea, called “boosting,” was proposed by Robert E. Schapire.61
Although there are many versions, here in outline is how it works. Using anyof the supervised machine learning methods, a classifier is trained on theoriginal training set in which each sample is equally “weighted.” (The ith
sample’s weight, say wi , can be set, for example, by including that sample witimes in the training set.) Then a new training set is constructed in whichthose samples that were misclassified have their “weights” increased, and thosesamples that were correctly classified have their weights decreased. Using thisnew training set, another classifier is trained. (That one will, presumably,
work harder on the earlier misclassified samples.) This process is repeateduntil we have some number, say m, of classifiers. Now, each of the classifiers
votes on the categorization of new samples. Their votes are weighted by how
well they performed on the original training set. Votes of the more reliableclassifiers count more than do those of less reliable classifiers. Even when theoriginal classifiers are “weak” (that is, not very reliable at all), the overall
accuracy of the combined set of m classifiers can be quite good, thus
“boosting” the results.
Several ways of doing boosting have been proposed. One of the popular
ones, due to Yoav Freund and Robert Schapire, is called “Adaboost.”62 It isalso possible to combine bagging and boosting.63
Finally, I’ll mention “Support Vector Machines” (SVMs). A complete
description of them would involve more mathematics than we want to get into
here, but I can give a rough-and-ready idea of how they work by using ageometric example. On the left-hand side of Fig. 29.17 I show the same points
that I used in Fig. 4.11 to illustrate a separating boundary in feature space.
The points indicated by small squares correspond to samples in one category,
and the points indicated by small circles correspond to samples in anothercategory.
As a reminder, the points in the diagrams have coordinates equal to thefeatures, f1 and f2 , computed from items (such as speech sounds, images, orother data) that we want to classify. It happens in this case that there exists
many straight-line (that is, linear) boundaries that would separate the points
in the two categories perfectly. Therefore an attempt to train a neural element
to classify the points (considered as “training samples”) would be successful.
If we used the standard error-correction procedure for training, we would
certainly get some linear boundary, but with SVMs we ask more of the
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29Machine Learning

Figure 29.17: Points and a linear separating boundary in a two-dimensional
space.
boundary than that it merely separate the training samples. We want it to besuch that the distances (called the “margin”) from it to the closest points of
opposite categories are as large as possible. Such a linear boundary is shownon the right-hand side of Fig. 29.17. The parallel dashed lines on either side
go through these closest points, which are called “support vectors.”
Boundaries with margins as large as possible are desirable because they are
better at classifying new points not in the training set. That is, they havebetter “generalizing” properties.
Our early work on pattern recognition (of the supervised learning variety)at SRI included some experiments in which we attempted to find separatingboundaries that were insulated away from the training samples. One of themethods for doing so involved including training samples derived from theoriginal ones by adding small amounts of “noise” to them. The idea was thatthe error-correction training procedure applied to this augmented set would beforced away from the original samples. A more elegant method was proposedby H. Glucksman, in which error-correction training continued until someminimum allowed distance between training samples and separatingboundaries was achieved.64 To ensure margins as large as possible, however,requires some complex optimization procedures. (Mathematically inclined
readers can refer to an online tutorial by Tristan Fletcher at
http://www.tristanfletcher.co.uk/SVMExplained.pdf or to a textbook by
Nello Cristianini and John Shawe-Taylor about SVMs.65 )Now, you might ask, how does one get feature spaces that are linearly
separable? One way is to use something like Rosenblatt’s alpha-perceptron.
Recall that the elements in the alpha-perceptron’s first layer of threshold
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29.7

NOTES

elements, say N of them, each received its own input from a random collectionof data measurements (such as pixels or speech waveform values). The binary
outputs of these “association units” (as these first-layer elements were called)were then features like those I used in the two-dimensional example. They
determined points in an N -dimensional feature space, which (Rosenblatt
hoped) was linearly separable. Often, in Rosenblatt’s work, they were.
The people working with SVMs use a different method for defining
features. Their method ensures that the resulting feature space is linearly
separable (or, at least, nearly so). Their features involve the use of what theycall “kernels,” and machines using such features are called “kernel machines.”
Again, the mathematics is too complex to be described here, but theinterested reader can look at the book by Nello Cristianini and John
Shawe-Taylor. As that book points out, the history of the mathematics
leading up to kernel machines and SVMs goes as far back as the beginning of
the twentieth century and has involved people in optimization theory,
statistics, and computational learning theory.
SVMs and kernel machines are superb examples of how work in several
disciplines, using highly technical mathematical apparatus, has contributed to
powerful new techniques in artificial intelligence. Important venues fordescribing new work in machine learning are the Neural Information
Processing Systems (NIPS) Conferences sponsored annually by the Neural
Information Processing Systems Foundation.66
After hearing about all of the methods for machine learning described inthis chapter, you might reasonably ask, which method is best? Should one use
the nearest-neighbor method, a decision tree, a neural network, or somethingelse? Researchers have asked that question also, and there have been
“bake-offs” in which different methods have competed on various standard
problems, such as character recognition. One such competition, organized by
the European Community ESPRIT project StatLog, is described in a bookedited by Donald Michie, D. J. Spiegelhalter, and C. C. Taylor.67 Anothercomparison of several methods was reported in the AI text by Russell andNorvig.68 Some methods work better for some problems than for others, but
often these differences are only marginal, and most people in the field agree
that having lots and lots of data is, in the end, more important than theparticular machine learning algorithm used. That is, spend time gathering
more data rather than tuning a particular method.69

Notes
1. For a nice review, see the online tutorial put together by Andrew H. Moore, one of thepioneers of memory-based learning, at http://www.autonlab.org/tutorials/mbl08.pdf. [497]
2. Christopher G. Atkeson, Andrew W. Moore, and Stefan Schall, “Locally Weighted
Learning for Control,” Artificial Intelligence Review, Vol. 11, pp. 75–113, 1997; available

527
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29

NOTES

online at http://www.cs.cmu.edu/∼cga/papers/air1.ps.gz. See also Stefan Schaal andChristopher G. Atkeson, “Robot Juggling: An Implementation of Memory-based Learning,”
IEEE Control Systems Magazine, Vol. 14, No. 1, pp. 57–71, February 1994; available online
at http://www-clmc.usc.edu/publications/S/schaal-CSM1994.pdf. [497]3. Walter Daelemans and Antal van den Bosch, Memory-Based Language Processing,Cambridge: Cambridge University Press, 2005. [498]4. Janet Kolodner, Case-Based Reasoning, pp. 18–19, San Francisco: Morgan Kaufmann
Publishers, 1993. [499]
5. See Janet Kolodner, “Reconstructive Memory: A Computer Model,” Cognitive Science,Vol. 7, No. 4, pp. 281–328, 1983, and Michael Lebowitz, “Memory-Based Parsing,”
Artificial Intelligence, Vol. 21, pp. 363–404, 1983. [499]
6. Edwina L. Rissland, unpublished notes. [499]
7. Edwina L. Rissland, “Example Generation,” Proceedings Third National Conference of
the Canadian Society for Computational Studies of Intelligence, pp. 280–288, Victoria, BC,
1980. [499]
8. See, for example, Edwina L. Rissland, “Examples in the Legal Domain: Hypotheticals inContract Law,” Proceedings Fourth Annual Cognitive Science Conference, pp. 96–99,
University of Michigan, Ann Arbor, 1982. [500]9. E-mail of February 17, 2009. [500]
10. http://www.aiai.ed.ac.uk/technology/casebasedreasoning.html. [500]11. Feigenbaum told me about another tree-construction system developed independently(and for different purposes) around 1959 by Edward Fredkin. See Edward Fredkin, “TrieMemory,” Communications of the ACM, Vol. 3, No. 9, pp. 490–499, September 1960. [502]
12. Edward A. Feigenbaum, “The Simulation of Verbal Learning Behavior,” Proceedings ofthe Western Joint Computer Conference, Vol. 19, pp. 121–132, 1961. Reprinted in Edward
A. Feigenbaum and Julian Feldman (eds.), Computers and Thought, New York:
McGraw-Hill, 1963. [502]
13. See, for example, Edward A. Feigenbaum and Herbert Simon, “EPAM-like Models of
Recognition and Learning,” Cognitive Science, Vol. 8, No. 4, pp. 305–336, 1984; Howard B.
Richman, J. J. Staszewski, and Herbert A. Simon, “Simulation of Expert Memory Using
EPAM-IV,” Psychological Review, Vol. 102, No. 2, pp. 305–330, 1995; and Howard B.
Richman, Herbert A. Simon, and Edward A. Feigenbaum, “Simulations of Paired Associate
Learning Using EPAM-VI,” Complex Information Processing Working Paper #553,
Department of Psychology, Carnegie Mellon University, March 7, 2002. The latter paper isavailable online at http://www.pahomeschoolers.com/epam/cip553.pdf. [502]
14. Carl I. Hovland and Earl B. Hunt, “Programming a Model of Human Concept
Formulation,” Proceedings of the Western Joint Computer Conference, pp. 145–155, May
9–11, 1961. Reprinted in Edward A. Feigenbaum and Julian Feldman (eds.), Computers andThought, pp. 310–325, New York: McGraw-Hill, 1963. [503]
15. Earl B. Hunt, Janet Marin, and Philip J. Stone, Experiments in Induction, New York:
Academic Press, 1966. [503]16. For Quinlan’s descriptions of ID3, see J. Ross Quinlan, “Discovering Rules by Induction
from Large Collections of Examples,” in Donald Michie (ed.), Expert Systems in the Micro
Electronic Age, pp. 168–201, Edinburgh: Edinburgh University Press, 1979, and J. Ross
Quinlan, “Induction of Decision Trees,” Machine Learning, Vol. 1, pp. 81–106, 1986.
Available online at http://www.cs.toronto.edu/∼roweis/csc2515-2006/readings/quinlan.pdf.
[503]
17. From an e-mail from Quinlan to me dated March 18, 2008. [504]18. That is, the one described in J. Ross Quinlan, op. cit. [505]528
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29.7

NOTES

19. See, for example, J. Ross Quinlan, “Learning Efficient Classification Procedures andTheir Application to Chess End Games,” Ryszard S. Michalski, Jaime G. Carbonell, andTom M. Mitchell (eds), Machine Learning: An Artificial Intelligence Approach, pp.
463–482, San Francisco: Morgan Kaufmann Publishers, 1983. (By the way, the very title ofthat volume indicates, I think, that the editors wanted to contrast the approach used in thevolume’s papers with neural network approaches to machine learning.) [505]
20. The classic paper is Claude E. Shannon, “A Mathematical Theory of Communication,”
Bell System Technical Journal, Vol. 27, pp. 379–423 and 623–656, July and October 1948.
Available online at http://cm.bell-labs.com/cm/ms/what/shannonday/shannon1948.pdf.
[505]
21. See, for example, J. Ross Quinlan, “Decision Trees and Multi-Valued Attributes,” inJean E. Hayes, Donald Michie, and J. Richards (Eds.), Machine Intelligence 11, pp.
305–318, Oxford: Oxford University Press, 1988. All of these measures are described in J.
Ross Quinlan, C4.5: Programs for Machine Learning, San Francisco: Morgan Kaufmann
Publishers, 1993. [505]22. J. Ross Quinlan, “Discovering Rules by Induction from Large Collections of Examples,”in Donald Michie (ed.), Expert Systems in the Micro Electronic Age, pp. 168–201,
Edinburgh: Edinburgh University Press, 1979. [506]23. E-mail communication, March 18, 2008. [506]24. See J. Ross Quinlan, C4.5: Programs for Machine Learning, San Francisco: Morgan
Kaufmann Publishers, 1993. [506]
25. A complete version of C4.5 can be downloaded free of charge from Ross Quinlan’s
homepage, http://www.rulequest.com/Personal/. Scaled-down versions of C5.0 and See5
can be downloaded free of charge from http://www.rulequest.com/download.html. [506]26. Intelligent Terminals, Ltd. [506]27. See, for example, Jerome H. Friedman, “A Recursive Partitioning Decision Rule for
Nonparametric Classification,” IEEE Transactions on Computers, Vol. 26, No. 4, pp.
404–408, 1977. [506]28. See Leo Breiman, Jerome H. Friedman, Richard A. Olshen, and Charles J. Stone,
Classification and Regression Trees, Pacific Grove, CA: Wadsworth, 1984. [506]29. CART 5 is available from Salford Systems. See
http://www.salford-systems.com/1112.php. [507]30. J. Ross Quinlan, “Learning Logical Definitions from Relations,” Machine Learning, Vol.
5, pp. 239–266, 1990. [507]
31. The interested reader who is comfortable with logic theory might consult Stephen
Muggleton and Luc De Raedt, “Inductive Logic Programming, Theory and Methods,”
Journal of Logic Programming, Vols. 19–20, pp. 629–679, 1994, and Nada Lavrac and Saso
Dzeroski, Inductive Logic Programming: Techniques and Applications, New York: Ellis
Horwood, 1994 (available online at http://www-ai.ijs.si/SasoDzeroski/ILPBook/). See also
Claude Sammut, “The Origins of Inductive Logic Programming: A Prehistoric Tale,” inStephen Muggleton (ed.), Proceedings of the Third International Workshop on Inductive
Logic Programming, pp. 127–147, Bled, Slovenia, 1993. [507]
32. See Saso Dzeroski and Nada Lavrac (eds.), Relational Data Mining, Berlin:
Springer-Verlag, 2001. [507]33. David Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams, in James L. McClelland,David E. Rumelhart, and the PDP Research Group (eds.), Parallel Distributed Processing:
Explorations in the Microstructure of Cognition, Vol. 1: Foundations, pp. 318–362,
Cambridge, MA: MIT Press, 1986. See also David Rumelhart, Geoffrey E. Hinton, andRonald J. Williams, “Learning Representations by Back-Propagating Errors,” Nature, Vol.
323, Letters, pp. 533–536, October 9, 1986. [508]529
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29

NOTES

34. Arthur E. Bryson Jr. and Y. C. Ho, Applied Optimal Control: Optimization,
Estimation, and Control, Waltham, MA: Blaisdell, 1969. [508]35. Paul Werbos, “Beyond Regression: New Tools for Prediction and Analysis in the
Behavioral Sciences,” Ph.D. thesis, Harvard University, Cambridge, MA, 1974. Laveen
Kanal, on the occasion of his acceptance of the 1992 King-Sun Fu award of the International
Association for Pattern Recognition (IAPR), recalls a 1975 conversation with Werbos. See
Laveen N. Kanal, “On Pattern, Categories, and Alternate Realities,” Pattern Recognition
Letters, Vol. 14, pp. 241–255, 1993. Available online at http://www.lnk.com/prl14.pdf.
[508]
36. Laveen N. Kanal, “On Pattern, Categories, and Alternate Realities,” Pattern
Recognition Letters, Vol. 14, pp. 241–255, 1993. Available online at
http://www.lnk.com/prl14.pdf. [509]37. Terrence J. Sejnowski and Charles R. Rosenberg, “Parallel Networks That Learn to
Pronounce English Text,” Complex Systems, Vol. 1, pp. 145–168, 1987. Available online at
http://www.cnl.salk.edu/ParallelNetsPronounce/ParallelNetsPronounce-TJSejnowski.pdf.
[509]
38. An early paper was Dean Pomerleau, “ALVINN: An Autonomous Land Vehicle in a
Neural Network,” Advances in Neural Information Processing Systems, Vol. 1, pp. 305–313,
San Francisco: Morgan Kaufmann Publishers, 1989. Pomerleau’s thesis was “Neural
Network Perception for Mobile Robot Guidance,” Carnegie Mellon University, February
1992. [510]39. Charles Thorpe et al., “Vision and Navigation for the Carnegie Mellon Navlab,” IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 10, No. 3, pp. 362–373,
May 1988. [511]
40. Dean A. Pomerleau, “Neural Network Vision for Robot Driving,” in Michael Arbib(ed.), The Handbook of Brain Theory and Neural Networks, Cambridge, MA: MIT Press,
1995. A version of this paper is available online at
http://www.ri.cmu.edu/pub files/pub2/pomerleau dean 1995 1/pomerleau dean 1995 1.pdf.
[511]
41. Ibid. [512]42. Dean Pomerleau, “RALPH: Rapidly Adapting Lateral Position Handler,” Proceedings ofthe IEEE Symposium on Intelligent Vehicles, pp. 506–511, September 1995. Available
online at
http://www.ri.cmu.edu/pub files/pub2/pomerleau dean 1995 2/pomerleau dean 1995 2.pdf.
[513]
43. The “No Hands Across America” homepage is at
http://www.cs.cmu.edu/afs/cs/usr/tjochem/www/nhaa/nhaa home page.html. There arepointers on that site to the trip’s journal and photos. For more recent work, see the
NavLab’s homepage at http://www.ri.cmu.edu/labs/lab 28.html. [513]44. Peter Cheeseman et al., “AutoClass: A Bayesian Classification System,” Proceedings ofthe Fifth International Conference on Machine Learning, pp. 54–64, San Francisco: Morgan
Kaufmann Publishers, 1988. See also Peter Cheeseman and J. Stutz, “BayesianClassification (AutoClass): Theory and Results,” in Usama M. Fayyad et al. (eds.),
Advances in Knowledge Discovery and Data Mining, Menlo Park, CA: AAAI Press andCambridge, MA: MIT Press, 1996. Available online at
http://ti.arc.nasa.gov/m/project/autoclass/kdd-95.ps. [515]45. http://ti.arc.nasa.gov/project/autoclass/. [515]46. Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, New York:John Wiley and Sons, Inc., 2001. [515]47. Edward L. Thorndike, Animal Intelligence, New York: The Macmillan Co., 1911. [516]530
Copyright c 2010 Nils J. Nilsson
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29.7

NOTES

48. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction,
Section 1.6, Cambridge, MA: MIT Press, 1998; available online at
http://www.cs.ualberta.ca/∼sutton/book/ebook/the-book.html. [516]49. John H. Andreae, “STeLLA: A Scheme for a Learning Machine,” Proceedings of the 2nd
IFAC Congress, 1963. Published in Automation and Remote Control, London:
Butterworths, 1964. [516]50. Donald Michie, “Experiments on the Mechanisation of Game Learning: 1.
Characterization of the Model and its Parameters,” Computer Journal, Vol. 1, pp. 232–263,
1963. [516]
51. Donald Michie and R. Chambers, “BOXES: An Experiment in Adaptive Control,” in E.
Dale and Donald Michie (eds.), Machine Intelligence 2, pp. 137–152, Edinburgh: Oliver andBoyd, 1968. [516]
52. A. Harry Klopf, The Hedonistic Neuron: A Theory of Memory, Learning, andIntelligence, Washington, DC: Hemisphere, 1982. [516]53. Christopher J. C. H. Watkins, “Learning from Delayed Rewards,” Ph.D. thesis,
Cambridge University, Cambridge, England, 1989. [518]
54. Andrew Moore and Christopher G. Atkeson, “Prioritized Sweeping: Reinforcement
Learning with Less Data and Less Real Time,” Machine Learning, Vol. 13, October 1993.
Online version available at
http://www.ri.cmu.edu/pub files/pub1/moore andrew 1993 1/moore andrew 1993 1.pdf.
[521]
55. For more information, see, for example, the following: Richard S. Sutton and Andrew G.
Barto, Reinforcement Learning: An Introduction, Cambridge, MA: MIT Press, 1998 (anhtml version of the book is available online at
http://www.cs.ualberta.ca/∼sutton/book/ebook/the-book.html.), and Leslie P. Kaelbling,
Michael L. Littman, and Andrew W. Moore, “Reinforcement Learning: A Survey,” Journalof Artificial Intelligence Research, Vol. 4, pp. 237–285, 1996. A Web page with lots ofpointers to papers and demonstrations is at http://rlai.cs.ualberta.ca/RLAI/rlai.html. [521]56. Gerald Tesauro, “Temporal Difference Learning and TD-GAMMON,” Communicationsof the ACM, Vol. 38, No. 3, March 1995. An html version of the paper is available online at
http://www.research.ibm.com/massive/tdl.html. [522]57. The quotation is taken from the html version of their book on reinforcement learning at
http://www.cs.ualberta.ca/∼sutton/book/11/node2.html. [523]58. Pieter Abbeel et al., “An Application of Reinforcement Learning to Aerobatic Helicopter
Flight,” in Bernhard Scholkopf, John Platt, and Thomas Hofmann (eds.), Advances in
Neural Information Processing Systems 19: Proceedings of the 2006 Conference, pp. 1–8,
Cambridge, MA: MIT Press, 2007. A pdf version is available at
http://www.cs.stanford.edu/∼ang/papers/nips06-aerobatichelicopter.pdf. Videos areavailable at http://www.cs.stanford.edu/group/helicopter See the roll video at
http://www.cs.stanford.edu/group/helicopter/video/rolls 080130 web960.mp4. [523]59. Chrisopher H. Donahue and Hyojung Seo, “Attaching Values to Actions: Action andOutcome Encoding in the Primate Caudate Nucleus,” The Journal of Neuroscience, Vol. 28,
No. 18, pp. 4579–4580, April 30, 2008. The authors refer to Sutton and Barto’s book as wellas to the earlier paper by Wolfram Schultz, Peter Dayan, and P. Read Montague, “A Neural
Substrate of Prediction and Reward,” Science, Vol. 275, No. 5306, pp. 1593–1599, March
14, 1997. [523]60. Leo Breiman, “Bagging Predictors,” Department of Statistics Technical Report No. 421,
University of California, Berkeley, September 1994. Available online at
http://salford-systems.com/doc/BAGGING PREDICTORS.pdf; and Leo Breiman,“Bagging Predictors,” Machine Learning, Vol. 24, No. 2, pp. 123–140, 1996. [524]61. Robert E. Schapire, “The Strength of Weak Learnability,” Machine Learning, Vol. 5,531
Copyright c 2010 Nils J. Nilsson
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NOTES

pp. 197–227, 1990. Available online at
http://www.cs.princeton.edu/∼schapire/papers/strengthofweak.pdf. [525]62. Yoav Freund and Robert E. Schapire, “A Decision-Theoretic Generalization of On-Line
Learning and an Application to Boosting,” Journal of Computer and System Sciences, Vol.
55, No. 1, pp. 119–139, 1997. Compressed PostScript version available online at
http://www.cs.princeton.edu/∼schapire/papers/FreundSc95.ps.Z. [525]63. See S. B. Kotsiantis and P. E. Pintelas. “Combining Bagging and Boosting,”
International Journal of Computational Intelligence, Vol. 1, No. 4, pp. 324–333, 2004.
Available online at
http://www.math.upatras.gr/∼esdlab/en/members/kotsiantis/ijcipaperkotsiantis.pdf. [525]64. H. Glucksman, “On the Improvement of a Linear Separation by Extending the Adaptive
Process with a Stricter Condition,” IEEE Transactions on Electronic Computers, Vol.
EC-15, No. 6, pp. 941–944, 1966. [526]65. Nello Cristianini and John Shawe-Taylor, An Introduction to Support Vector Machines:
And Other Kernel-based Learning Methods, Cambridge, UK: Cambridge University Press,
2000. [526]66. See http://nips.cc/. [527]67. Donald Michie, D.J. Spiegelhalter, and C.C. Taylor (eds.), Machine Learning, Neuraland Statistical Classification, Chichester: Ellis Horwood, 1994. The book is now out of print
but is available online from http://www.maths.leeds.ac.uk/∼charles/statlog/. [527]68. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, second
edition, pp. 752–754, Upper Saddle River, NJ: Prentice Hall, 2003. [527]69. For additional perspective on comparing different algorithms, see David J. Hand,“Classifier Technology and the Illusion of Progress,” Statistical Science, Vol. 21, No. 1, pp.
1–15, 2006. Available online at http://arxiv.org/pdf/math.ST/0606441. [527]532
Copyright c 2010 Nils J. Nilsson
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30.1

Chapter 30

Natural Languages andNatural Scenes
In some of the just-preceding chapters, we have seen the important role ofdata and of machine learning techniques for distilling and using these data. If
one single theme has lately begun to unite the several disparate approaches toAI, ranging from logical representations and reasoning in Cyc to decision andestimation by neural networks, it is their dependence on massive amounts ofdata. Moving beyond toy problems and simple puzzles into real-world
problems requires real-world data. In this chapter, I’ll explore how the latest
systems for natural language processing and computer vision exploit data thatare representative of the inputs they must deal with.

30.1

Natural Language Processing

The growing need for systems able to deal with written and spoken languages,
together with new technical advances, large databases, and increased
computational power, has led to improved systems for performing such tasks
as summarizing pieces of text, answering queries, and translating languages.
In this section, I’ll describe some of the technical developments in NLP during
the past two or three decades. Impressive as they are though, they have not
yet allowed us to realize Terry Winograd’s hope back in 1971 that “We willtalk to [computer systems] just as we talk to a research assistant, librarian, or
secretary, and they will carry out our commands and provide us with theinformation we ask for.” Many people say that the problem of realizing such
systems is “AI complete,” in the sense that they must be generally as
intelligent as humans, being able to reason and to solve problems as well as
humans do those things. In any case, it is probable that such systems, when
we finally do have them, will employ some or all of the technology I’ll be
533
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30

Natural Languages and Natural Scenes

describing here.

30.1.1

Grammars and Parsing Algorithms

Earlier I described some of the basic ideas of linguistic theory. For example, I
mentioned that sentences can be analyzed in terms of their syntactic structureusing context-free grammars (CFGs). I also mentioned more complex
grammars such as definite clause grammars (DCGs), systemic grammars,transition network grammars, and DIAGRAM. Systems that use grammars foranalyzing natural language sentences must use parsing algorithms to searchamong candidate “parse trees” to find one or more that fits an input sentence.
For realistic grammars that “accept” those word strings we think of as legal
sentences and reject those strings we take to be nonsentences, it is often thecase that there are many possible parses, each conveying a different meaning.
Choosing one “best” parse tree from among all of these then depends on
semantic and pragmatic analyses, which take into account the context inwhich the sentence occurs and common-sense world knowledge.
As a humorous example of how one can get into trouble by failing to take
into account common-sense knowledge, Daniel Jurafsky and James Martin
quote a sentence from the 1930 movie Animal Crackers: Groucho Marx says
“One morning I shot an elephant in my pajamas. How he got into my pajamas
I don’t know.”1
Work on natural language processing continues to explore new and more
complex grammars, parsing algorithms, and semantic processing techniques.
The newer grammars are able to deal more efficiently with larger subsets ofEnglish, and many of them can handle languages other than English. Some
examples are lexical functional grammars (LFGs),2 tree adjoining grammars(TAGs ),3 dependency grammars, head-driven phrase structure grammars(HPSGs)4 , government and binding,5 and categorical grammars.6
Many improvements have been made to parsing algorithms also. Whenused with realistic grammars, breadth-first search (either with a bottom-up or
top-down method) quickly exhausts storage space. Backtracking depth-firstsearch, although more economical of memory, risks having to do much of thesearch over if the search runs into trouble and must unwind back to earlier
parts of a sentence. To avoid having to reparse parts of a sentence after
unwinding, parsers have been invented that employ charts and otherconstructs in which to store, for possible reuse, already computed parses ofsegments of sentences. Martin Kay developed the first chart parser.7 Other
parsers that use chart-like structures are the Earley parser (invented by Jay
Earley)8 and the Cocke–Younger–Kasami (CYK) algorithm.9 Modern parsers
use one version or another of dynamic programming, a technique I mentioned
previously. It permits saving of intermediate results. I list these examples ofgrammars and parsers, without attempting descriptions (which are quite
technical), just to illustrate the breadth and depth of activity in these aspects
534
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30.1

Natural Language Processingof NLP.10
Natural language processing research and applications have benefitted
greatly by having large text files. Such files contain millions of sentences andexist in many languages. They include newspaper articles, literary texts, andother materials. Large files of sentences are called corpora (the plural of
corpus, meaning body). One of the NLP Web sites at Stanford,
http://nlp.stanford.edu/links/statnlp.html, provides examples. Other corpora
can be found at the “Linguistic Data Consortium” Web page at
http://www.ldc.upenn.edu/. Sentences from these files can be parsed andannotated by humans, sometimes aided by parsing algorithms, and the parses
can be stored along with their associated sentences in structures called “treebanks.” Prominent examples are those developed at the University ofPennsylvania, called the “Penn Treebanks.”11 The Penn Treebank Project
maintains a Web site at http://www.cis.upenn.edu/∼treebank/. Tree banks,with their annotations, can be used to induce more powerful grammars
covering the sentences in them. As usual, the larger the tree bank, the betterthe induced grammar. Statistically based machine learning techniques areused in this process, and that brings me to my next topic.

30.1.2

Statistical NLP

A. Context-Free Rules with Probabilities
As I mentioned earlier, a grammar is supposed to be able to distinguishbetween sentences that are acceptable in a language and those that are not.
But as Christopher Manning and Hinrich Schütze point out, “It is just notpossible to provide an exact and complete characterization of well-formed
utterances that cleanly divides them from all other sequences of words, which
are regarded as ill-formed utterances. This is because people are always
stretching and bending the ‘rules’ to meet their communicative needs.”12 This
fact was recognized quite early in the study of language. In his 1921 book, thelinguist and anthropologist Edward Sapir wrote “Unfortunately, or luckily, no
language is tyrannically consistent. All grammars leak.”13 Sapir meant, ofcourse, that any grammar, no matter how complex, will accept some sentencesthat people find unacceptable and reject some that people find acceptable.
Eugene Charniak, one of the first AI researchers who recognized thisdifficulty, proposed that syntactic analyses should be qualified by probabilities.
Some sentences are “probably” ok, and some are probably not, and there are
all gradations in between.14 An immediate advantage of such an approach isthat the probability of a parse can be used to choose among alternative parses
for ambiguous sentences. Consider, for example, two alternative ways to readthe Groucho-like sentence “John shot elephants in pajamas”:• John (while in pajamas) shot elephants.
535
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30

Natural Languages and Natural Scenes• John shot elephants (which were in pajamas).

Each of these interpretations of the sentence has a different parse tree. Is there
a way to consider one of them more probable than the other?
In 1969, the automata theorist Taylor L. Booth proposed a variation oncontext-free grammars that assigned probabilities to the rules used to define agrammar.15 Such grammars are called “Probabilistic Context-Free Grammars”(PCFGs). I’ll use the following very simple (and quite incomplete) grammar
just to illustrate the idea:16
S → NP VP (1.0)
PP → P NP (1.0)
VP → VP NP (0.7)
VP → VP PP (0.3)
P → in (1.0)
V → shot (1.0)

NP → NP PP (0.4)
NP → John (0.1)
NP → pajamas (0.18)
NP → shot (0.04)
NP → elephants (0.18)
NP → uniforms (0.1)

The number in parentheses following a rule represents the probability of that
rule. Thus, according to this grammar for example, the probability is 0.18 thata noun phrase in a sentence is the word “elephants.” Because a noun phrase
has to be something, the sum of all of the noun phrase probabilities is 1.0.
B. Probabilities of Parse Trees
Assuming that the probabilities of these rules are independent (a wildly
inappropriate assumption for realistic grammars), we can calculate theprobability of a parse tree by taking the product of the probabilities of all of
the rules used in the tree. Two parse trees for this sentence are shown in Fig.
30.1. The one on the right, in which it is John who is in pajamas whileshooting, would seem to be the more appropriate one in most settings otherthan, perhaps, cartoons.
The numbers subscripting each grammar term in the trees are the probabilitiesof the corresponding rules. The parse tree on the left has probability
Probleft = 1.0 × 0.1 × 0.7 × 1.0 × 0.4 × 0.18 × 1.0 × 1.0 × 0.18 = 0.0009072.
The parse tree on the right has probability
Probright = 1.0 × 0.1 × 0.3 × 0.7 × 1.0 × 0.18 × 1.0 × 1.0 × 0.18 = 0.0006804.
The one on the left would therefore be preferred. (Well, I would not want tobe talking about actually shooting elephants. I had in mind cartoon elephantsthat were wearing pajamas.)
Another important aspect of PCFGs is that they can be used to predict
the overall probability of a sentence. That is, how likely is it that a sentence
like “John shot elephants in pajamas” would occur? We can compute that536
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30.1

Natural Language Processing

Figure 30.1: Two parse trees for “John shot elephants in pajamas.”probability simply by adding the probabilities of all of the possible parses ofthat sentence. In this case, we just add two probabilities to obtain 0.0015876.
The probabilities in this example are contrived for illustrative purposes onlyand should not be taken seriously. More realistic probability values would bebased on a much larger grammar and corpus of sentences, which brings me tomy next topic.
C. Learning PCFGs
How does one obtain values for the probabilities of the rules in a PCFG? In
particular, how does one obtain values that appropriately model actual
sentences? An annotated tree bank provides a way to get values appropriate
for the sentences in the tree bank because each of its sentences has an
associated parse tree. The parse trees use rules all of the form l → r, where l is
the left-hand side of the rule (such as VP) and r is the right-hand side of therule (such as VP NP). To obtain a probability value for a rule l → r, we counthow many times that very rule occurs in the tree bank and divide that countby the number of times l occurs. The PCFG so obtained can then be used toparse new sentences.
A PCFG can also be generated without having a tree bank if one has an
ordinary (nonprobabilistic) context-free parser that can be applied to a corpus
of sentences. But unlike a tree bank, it is likely that each sentence in the
corpus will have multiple parses, with some having many. When counting theoccurrences of rules how can we avoid overweighting the rules in thosesentences with lots of parses? Here’s a method that seems to work well:
1. Convert the original CGF into a PCGF with equal rule probabilities.

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Natural Languages and Natural Scenes
2. Parse the sentences with this PCFG, computing a probability for each
ambiguous parse.
3. Count the rules in each parse for each sentence, and weight the count by
the probability of that parse.
4. Use these weighted counts to compute new probabilities for the rules andthus a new PCFG.
5. Repeat this process until the rule probabilities cease to change (which
will happen eventually).

This procedure is a version of an algorithm often used in machine learning,called the Expectation Maximization (EM) algorithm.
To account for non-context-free aspects of sentence structure and for
detailed information about specific words, practical applications typically use
PCFGs that have been augmented in various ways. Several parsers forversions of PCFGs have been developed. I can’t resist mentioning one basedon the A∗ search algorithm. In presenting it, Dan Klein and Christopher D.
Manning wrote17
On average-length Penn treebank sentences, our most detailed
estimate [for use as the heuristic function] reduces the total
number of edges processed [using A∗ search] to less than 3% ofthat required by exhaustive parsing, and a simpler estimate, which
requires less than a minute of precomputation, reduces the work toless than 5%.
Several other statistically based methods for analyzing sentences havebeen developed. I’ll mention just a few of these.18 Rens Bod and colleagues at
the University of Amsterdam have been developing a technique they call
“Data-Oriented Parsing” (DOP), which is based on the idea that “humanlanguage perception and production work with representations of concrete
language experiences, rather than with abstract grammatical rules.”19
Statistical methods have also enhanced Lexical Functional Grammars (LFGs),
both by using DOP ideas20 and by the work of Josef van Genabith and his
group at Dublin City University on learning LFG grammars from annotatedtree-bank data.21 Finally, Ron Kaplan and his group at a commercial natural
language query company, Powerset (now part of Microsoft), are trying to learn
how to assign probability orderings to the multiple parse trees of a sentencethat are produced by a parser using a handwritten (rather than a learned)
grammar.
Other uses of statistics in natural language processing include using data
about how frequently certain combinations of words occur in various text
sources. Such combinations are called “n-grams.” For example, a two-word
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Computer Vision

sequence such as “just now” is a 2-gram, and a five-word sequence such as“put it on the shelf” is a 5-gram. Using the slogan “that there’s no data likemore data,” Google has analyzed a corpus of one-trillion words from public
Web pages, for example, to publish “the counts for all 1,176,470,663 five-wordsequences that appear at least 40 times.”22
Summarizing the impact of the use of statistical methods in NLP
Manning and Schütze wrote “Indeed, much of the recent enthusiasm forstatistical methods in natural language processing derives from people seeing
the prospect of statistical methods providing practical solutions to real
problems that have eluded solution using traditional NLP methods.” They
even mention some possible new names for the field, such as “Language
Technology” or “Language Engineering” instead of NLP.23

30.2

Computer Vision

In this section I’ll discuss a few representative samples of recent work incomputer vision, much of which builds on the fundamental image-processing
techniques I described in previous chapters. In fact this debt to previous work
is acknowledged by most researchers, as in the following excerpt from a recent
paper:24
It is interesting to note that a lot of what are considered modern
ideas in computer vision – region and boundary descriptors,
superpixels, combining bottom-up and top-down processing,
Bayesian formulation, feature selection, etc. – were well-known
three decades ago! . . . However, it seems that the early pioneers
were simply ahead of their time. They had no choice but to rely onheuristics because they lacked the large amounts of data and thecomputational resources to learn the relationships governing thestructure of our visual world. The advancement of learning
methods in the last decade brings renewed hope for a complete
scene understanding solution.
Now we have the needed data and computational resources. Besides
these, computer vision has benefitted from contributions from several otherfields, including optics, mathematics, computer graphics, electricalengineering, physics, neuroscience, and statistics. All of these disciplines
continue to provide ideas and techniques, but one in particular has begun todominate, namely, machine learning.
Some people distinguish between “computer vision” and “machine
vision,” – confining computer vision mainly to robotics and using machine
vision to include that application and many others as well. Because we’llultimately want robots to be involved in most of these applications, I don’t
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Natural Languages and Natural Scenes

think the distinction is very useful, so I’ll continue to refer to the whole fieldas computer vision.
Another distinction is between what is called “scene analysis vision” on
the one hand and “purposive (or active) vision” on the other. The scene
analysis approach guided much vision research since its earliest days. Thisview held that the goal of computer vision was to transform a two-dimensional
image into a description of a three-dimensional scene. For example, the visionsystem for the MIT “Copy Demo” constructed a three-dimensional model ofan arrangement of toy blocks. (See Fig. 10.1.) In contrast, some researchers
pointed out that the purpose of vision was to provide just and only thatspecific information needed for motor control. We can see that approach
followed in the various vision routines used by Shakey, for example. Rather
than construct a complete model of its visual world, Shakey used vision to giveit information needed to guide motor actions and to make plans. This kind of“purposive vision” is usually less demanding of computational resources than acomplete scene analysis would be.
People who study the visual processes of animals (including humans) havealso argued about these two approaches. David Marr, who was interested inmodeling human visual processes, advocated the scene analysis approach.
However, the people who analyzed visual perception in the frog (see p. 171),
noted that its visual system was organized more purposively, to catch insects
for example. The computational neurobiologist Terrence Sejnowski (the same
Sejnowski who worked on NETtalk) and colleagues describe biological andpsychological evidence that human vision is purposive, not scenereconstructive, the latter of which they call “pure vision.” They wrote25
What is vision for? Is a perfect internal recreation of thethree-dimensional world really necessary? Biological andcomputational answers to these questions lead to a conception ofvision quite different from pure vision. Interactive vision, as
outlined [in this paper], includes vision with other sensory systems
as partners in helping to guide actions.
As I look at many of the computer vision systems produced in the past
twenty years or so, I see both kinds. There are systems that are proficient at
guiding autonomous vehicles along roads – paying attention only to the roadand to other vehicles on the road without analyzing or even being aware ofhouses along the way that, although they might be in the scene, are irrelevantto the driving task. There are also systems that analyze photographs toconstruct three-dimensional models of the buildings and other objects in them.
In addition, there are systems that have aspects of both approaches, as I will
discuss in the next section.

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30.2

30.2.1

Computer Vision

Recovering Surface and Depth Information

Derek Hoiem, Alexei Efros, and Martial Hebert at the Robotics Institute ofCarnegie Mellon University developed a program that was able to classify
segments of a single image as belonging to surfaces of various types andorientations.26 Although these classifications do not constitute a
three-dimensional model of the scene that gave rise to the image, they do giveinformation about important physical properties of the scene, somewhat likeDavid Marr’s 2 12 -D sketch does. Such information that might be useful for arobot having to navigate and recognize objects in the scene, for example.
Their work used images of outdoor subjects such as “forests, cities, roads,
beaches, lakes, etc.” taken under a variety of conditions “(snowy, sunny,cloudy, twilight).” Two examples are shown in Fig. 30.2.

Figure 30.2: Typical outdoor images. (Images courtesy of Derek Hoiem.)
Their program classified regions of an image into one of three major
surface categories: “support,” “vertical,” or “sky.” As the authors define thesecategories, “Support surfaces are roughly parallel to the ground and could
potentially support a solid object. Examples include road surfaces, lawns, dirt
paths, lakes, and table tops. Vertical surfaces are solid surfaces that are too
steep to support an object, such as walls, cliffs, the curb sides, people, trees, orcows. The sky is simply the image region corresponding to the open air andclouds.” To justify this classification, the authors point out that in 300 images
that they collected using Google image search, “over 97% of the pixels belong
to horizontal (support), nearly vertical surfaces, or the sky” (as established byhuman inspection of the images).
The program further classified each vertical surface into one of thefollowing subclasses: “planar surfaces facing to the ‘left,’ ‘center,’ or ‘right’ ofthe viewer, and nonplanar surfaces that are either ‘porous’ or ‘solid.’ Planar
surfaces include building walls, cliff faces, and other vertical surfaces that are
roughly planar. Porous surfaces are those which do not have a solid continuous
surface. Tree leaves, shrubs, telephone wires, and chain link fences are all
examples of porous surfaces. Solid surfaces are nonplanar vertical surfaces that
541
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Natural Languages and Natural Scenes

do have a solid continuous surface, including automobiles, people, beach balls,
and tree trunks.”
Their program learned to make these classifications (andsubclassifications) by using a training set of the 300 Google images. Groups ofadjacent pixels in each of the training images in this set were assembled intonearly uniform regions, called “superpixels,” on the basis of similarity of colorand intensity. Then each superpixel was (tediously!) manually assigned aclassification and subclassification. Superpixels were further grouped intolarger regions called segments, which inherited classifications from theirconstituent superpixels. From here on the mathematics gets more complex
than I want to describe here (or than you would care to read), but in essence
the learning process constructed a decision tree that could adequately matchthe hand-classified regions in the training set images. The trained decision tree
could then be used to classify the regions of any images. The nodes of thedecision tree were based on pixel and segment features involving location,color, texture, and perspective, all of which could be computed using
previously invented techniques (some of which I have described in previous
chapters).
Although not entirely representative of overall results, the images in Fig.
30.3 give an indication of how well their program performed. In the images inFig. 30.3, green indicates a support surface, red indicates a vertical surface,and blue indicates sky. The subclasses for vertical surfaces are indicated byleft arrows for left-facing planes, up arrows for center-facing planes, and rightarrows for right-facing planes, “O” for porous surfaces, and “X” for solid
surfaces.

Figure 30.3: Original image (left), hand-labeled image (center), and system’s
output (right). (Images courtesy of Derek Hoiem.)
Stanford professor Andrew Ng and his students have gone farther,
extracting actual depth information and scene-structure information from542
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30.2

Computer Visionmonocular images. “Ground truth” depth information for a set of training
images is first gathered by a three-dimensional laser scanner. A learningalgorithm attempts to match its estimates of depth against ground-truth
depth using several image features.27 These include texture variations, texture
gradients, color, and occlusion information. Because depth information aboutclose objects is captured at larger scales than that of distant objects, features
are extracted at multiple image scales. The learning process trains ahierarchical, multiscale Markov random field network to represent therelationships between the depth of an image patch and the depths ofneighboring patches. Figure 30.4 is a condensed illustration of two of the threelevels of such a network. (Again, the details of how the system learns fromexamples are more complex than I can explain here.)28

Figure 30.4: A multiscale Markov random field network. (Photographs courtesyof Andrew Ng and Ashutosh Saxena.)
Figure 30.5 shows some images downloaded from the Internet together
with associated “depth maps” (with different depths indicated by different
colors) predicted by their system.
Ashutosh Saxena and Andrew Ng continue to perfect these techniques.
They have a Web site, http://make3d.stanford.edu/, at which you can use
YouTube or other software to “fly around” three-dimensional models
constructed by their system from various monocular images. (These fly-arounddemonstrations are an impressive illustration of just how far computer vision,
based on large numbers of images and statistical methods, has progressed.)543
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Natural Languages and Natural Scenes

Figure 30.5: Typical images (left) and predicted depth maps (right). (Photographs courtesy of Andrew Ng and Ashutosh Saxena.)
Also from this Web site, you can upload your own photographs to have themconverted to three-dimensional versions.29

30.2.2

Tracking Moving Objects

If vision systems are to work on natural scenes in the real world, one of thethings they will have to deal with is moving objects. Several researchers haveworked on the problem of tracking objects visually, with some of the earliest
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30.2

Computer Vision

work dating back to the late 1970s. One example, which I’ll use to explainsome of the methods employed, is the work of Michael Isard (1971– ; Fig.
30.6) and Andrew Blake (1956– : Fig. 30.6) at the University of Oxford. They
have developed an algorithm, called CONDENSATION (for Conditional
Density Propagation), for tracking moving objects.30 The algorithm is able
“to track outlines and features of foreground objects, modeled as curves, as
they move in substantial clutter, and to do it at, or close to, video frame-rate.”

Figure 30.6: Michael Isard (left) and Andrew Blake (right). (Photographs courtesy of Michael Isard and of Andrew Blake.)

Figure 30.7: Tracking a leaf in the wind. (From the initial still frame and froma frame one-half second later of the movie at http://homepages.inf.ed.ac.uk/
rbf/CVonline/LOCAL COPIES/ISARD1/images/leafmv.mpg. Used with permission of Michael Isard and Andrew Blake.)
Here, in brief overview, is how their system works on one of their several
examples – a movie of a leaf on a bush blowing in the wind against abackground of similar leaves. It starts with a beginning frame of the movie in
which the particular leaf of interest is outlined by a hand-drawn curve as in
the left-hand part of Fig. 30.7. Tracking the outline of the leaf as it moves
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Natural Languages and Natural Scenes

requires knowledge about the leaf’s dynamics. That is, given its position andshape in the image at one instant of time, what position and shape is it likely
to have at the next instant of time? And at subsequent instants of time? We
can’t know for sure, but we can use dynamic Bayesian networks (DBNs),suitably modified to use continuous probability distributions instead ofprobability distributions over discrete variables, to make estimates. The
required probabilities are estimated by a learning process, and these are
gradually refined by observing the leaf as it actually moves. However, toobserve it, we have to track it, and that requires knowing the probabilities – a“chicken-and-egg” problem that Isard and Blake have been able to workthrough.
As time marches on, the probabilities about the leaf’s position and shapein the image diffuse, causing more and more uncertainty about the outline of
the leaf. But we do make observations – taking in a new image at every time
instant. These observations, being imprecise themselves, also provide
probabilities (using Bayes’s rule) about the the leaf’s position and shape.
These latter probabilities help to sharpen the diffusing ones about the leaf’s
dynamics – to the point that rather precise estimates can be made. For
example, at twenty-five time steps later (0.5 s), the system guesses at theoutline shown in the right-hand side of Fig. 30.7. (You can see the movie withthe leaf being tracked at http://homepages.inf.ed.ac.uk/rbf/CVonline/
LOCAL COPIES/ISARD1/images/leafmv.mpg.)
Isard and Blake use an array of complex technology to achieve all of this.
One problem is how to represent probability functions for the leaf’s dynamics
and how to move this representation forward from one time step to another.
They have adopted a technique called “particle filtering,” which represents theprobability of an outlining curve by a large set of weighted samples, called
particles, of outlines. At each time step the group of particles is brought
forward to the next time step and the whole lot is rerepresented as aprobability function. Particle filtering is used exensively for image processing
and other perception problems.
I have used the Isard–Blake work to illustrate object tracking, but there
are many other projects. Dieter Fox and colleagues, at the University ofWashington Robotics and State Estimation Lab, have used particle filtering inmany applications. At one of their Web sites,
http://www.cs.washington.edu/ai/Mobile Robotics/mcl/, you can see“particle filters in action.” A particularly impressive demonstration available
there is a movie of simultaneous tracking of a changing number of people using
a moving robot’s laser range finders.31 A typical screen shot is shown in Fig.
30.8. The image on the left (not used by the robot; it’s just for us) shows theactual locations of the people and the robot. The image on the right shows thecomputed locations of the people and the robot, represented by graphicalobjects. This application uses an extension to particle filtering, which theauthors call “sample-based joint probabilistic data association filters.”32

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30.2

Computer Vision

Figure 30.8: Tracking multiple people. (From the movie at http://www.
cs.washington.edu/ai/Mobile Robotics/mcl/animations/floor3D.avi. Used with
Permission of Dieter Fox.)
A group headed by Ernst D. Dickmanns (1936– ) at the Institut für
Systemdynamik und Flugmechanik at the Universität der Bundeswehr in
Munich, Germany, has been working on vision and control systems fordriverless automobiles since the late 1970s. Their dynamic vision systems areable to detect and track adjacent vehicles using spatio-temporal models ofobject motion, what they call a “4-D” approach.33 They are perhaps the firstgroup to use Kalman filtering for visual object tracking. In fact, their work has
been called “the first significant real world application of computer vision.”34
Installed in various Mercedes-Benz vehicles, their vision and control
systems have been able to drive autonomously for long distances, changing
lanes and overtaking slower vehicles. In 1995, their VaMP vehicle (aMercedes-Benz 500 SEL) drove the 1,758-km trip from Munich to Odense,
Denmark, and back at speeds exceeding 175 km/hour. About 95% of the trip
was driven fully autonomously with a total of 400 lane-change maneuvers.
Some additional details about their autonomous vehicle and vision projects
can be found in Dickmann’s book about “Dynamic Vision.”35
Space does not permit describing several other object-tracking projects,
but I’ll mention just two more. Jitendra Malik heads a vision group at the
University of California, Berkeley, where object-tracking research (along withother vision work) has been done.36 In the Vision Group at the University ofLeeds in the United Kingdom work has been done on tracking soccer players
and automobiles, for example.37 Other work at Leeds has as its goal
improving object-tracking accuracy by reasoning about “fundamental
constraints on the spatio-temporal continuity of objects.”38

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Natural Languages and Natural Scenes

30.2.3

Hierarchical Models

I believe that one of the potentially most promising developments in computer
vision (and maybe even for all of AI) involves hierarchical models. There aredifferent versions of these models, and different ways to construct them, but if
we stand far enough back from the details, they have similar structures andfeatures. First, the raw pixels are aggregated spatially (and in some systems
temporally) to form higher level groupings. These groupings might constitutesmall edges, or corners, or other primitive components appropriate for thekinds of images being processed. At the next level of the hierarchy, thefirst-level groupings are aggregated again into somewhat higher levelcomponents, and so on until, say, recognizable objects in the image arerepresented at the highest level.
Many of the ideas used in these systems harken back to certain features of
earlier systems (such as Pandemonium, the Neocognitron,39 Blackboard
architectures, speech recognition systems, and PDP recurrent networks), butmany of the newer systems combine and extend these features in ways that noindividual earlier system did. Specifically, let me mention the following:
1. The aggregations at the various levels are learned using massive data sets
– not predesigned by hand. And, in some systems, the learning is“unsupervised” – relying on the continuity of an object’s appearance
within a temporal stream of images to provide information about object
identities.
2. Occurrences of aggregations at each level are qualified by probabilities
with probabilistic graphical models (such as Markov random fields)
providing the main representational and computational mechanisms.
3. The probabilities of aggregations at one level can affect not only theprobabilities of aggregations at higher levels but also the probabilities ofaggregations at the same and at lower levels. That is, unlike as in
Pandemonium and in feed-forward neural networks, in these newer
systems there are “backward” connections from higher levels to lower
levels. These backward connections allow the systems to makepredictions about what was probably in the scene even though it might
have been obscured or absent in the image.
Several researchers have been involved in the development of hierarchicalmodels. Some are motivated mainly by attempts to model the storage andinference mechanisms in the visual cortex of humans and primates. Even so,
their models are nonetheless quite interesting to AI people, combining, as theydo, insights and evidence from neuroscience with quite elaborate
computational apparatus – including hierarchical graphical models andstatistical sampling techniques. Others use hierarchical models and advanced

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30.2

Computer Visioncomputational methods, without particular concern for their biologicalplausibility, to build more powerful computer vision systems.
To begin, I’ll describe the proposal by Tai Sing Lee (1961– ) and David
Mumford (1937– ) that the hierarchy of processing layers of the visual cortex
can be modeled as in Fig. 30.9.40

Figure 30.9: Tai Sing Lee (top left), David Mumford (top right), and their layersof visual processing (bottom). (Photographs courtesy of Tai Sing Lee and ofDavid Mumford. Diagram adapted from Tai Sing Lee and David Mumford,“Hierarchical Inference in the Visual Cortex,” Journal of the Optical Society ofAmerica, A , Vol. 20, No. 7, July 2003.)
In the Lee–Mumford model (based partly on the pattern theory work by
Ulf Grenander41 ), “bottom-up” visual observations coming in from the left are
integrated with “top-down” hypotheses formed at the right. In the diagram,
think of x0 as standing for a representation of the image as an array of pixels.
Think of x1 as a more abstract representation of the image, say in terms offeatures such as short line segments. As we move one step to the right, thecomputations produce a yet more abstract representation, x2 , which thenserves as a hypothesis about x1 . The formulas in the boxes (which I won’t
attempt to explain here), and the arrows connecting them, are meant to showthat at every level the probability of a representation, xi , is dependent both on
xi−1 (regarded as input) and on xi+1 (regarded as a hypothesis about xi ).
Lee and Mumford describe this feed-forward–feedback process as follows:549
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The feedforward input drives the generation of the hypotheses, andthe feedback from higher inference areas provides the priors toshape the inference at the earlier levels. Neither the feedforward
messages nor the feedback messages are static: As theinterpretation of an image proceeds, new high-level interpretations
emerge that feed back new priors, and as low-level interpretations
are refined, the feedforward message is modified. Such hierarchical
Bayesian inference can proceed concurrently across multiple
areas. . . [with] successive cortical areas in the visual hierarchy[constraining] one another’s inference in small loops rapidly andcontinuously as the interpretation evolved. One might hope thatsuch a system, as a whole, would converge rapidly to a consistentinterpretation of the visual scene incorporating all low-level andhigh-level sources of information; but there are problems. . .

One of the “problems” is that because none of the levels can becompletely sure of its interpretation there might be multiple high-probability
global interpretations. Lee and Mumford suggest a remedy based on otherongoing AI work, namely, “not to jump to a conclusion” at any level but toallow several high-probability interpretations to “stay alive” until one overall
interpretation for the whole chain emerges as the most probable. (You mightrecall that two of Barrow and Tenenbaum’s systems, namely, MSYS and theone that used intrinsic images, attempted to do just that back in the 1970s.)
To implement their idea, Lee and Mumford suggest using particle filtering,which, as I have already mentioned, uses a weighted set of samples torepresent the probability distribution over interpretations at each level. Using
these distributions, which are to be learned from experience, and the formulas
linking the levels, the system can settle on a most probable interpretation ateach level.
Although Lee and Mumford suggest implementational ideas for theirprobability calculations, such as the use of Markov random fields, they did not
implement their model. As they explain,
We have not offered a simulation to accompany our proposal,
partly because many details remain to be worked out and partly
because the choice of model is still quite unconstrained and any
specific simulation provides only weak support for a high-level
hypothesis like ours.
They do, however, cite neurophysiological and psychophysical evidence
supporting their model. They use the illustration in Fig. 30.10 to help explain
how models like theirs might work to improve processing of visual images. The
brightly illuminated part of the image suggests that the image might be of aface. That hypothesis, in turn, makes lower level processing of the image moresensitive to the occurrence of a faint edge of the face – allowing its detection.
(Humans might say, “Oh yes, now I see that edge.”)
550
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30.2

Computer Vision

Figure 30.10: Seeing a face more clearly. (Adapted from Tai Sing Lee and David
Mumford, “Hierarchical Inference in the Visual Cortex,” Journal of the OpticalSociety of America, A , Vol. 20, No. 7, July 2003.)
Geoffrey E. Hinton (1947– ), Simon Osindero (1977– ), and Yee-Whye Teh(1977– ) devised (rather complex) unsupervised learning strategies for anotherhierarchical model called a “deep belief network.”42 They conducted
experiments with the version shown in Fig. 30.11. The overall structure is alayered neural network, with the top level consisting of 2,000 units each withbidirectional connections to the units in the level below. Training of the
network proceeds from the bottom in steps, level by level. As each level istrained, its weights are “frozen,” and its results are used as inputs for training
the next higher level, and so on. This so-called greedy method of training
results in a good hierarchical model of the distribution of the images seen.
The authors also describe experiments in which ten decision units areadded to the top of the previously trained hierarchical network. The decisionunits are then trained to discriminate among handwritten digits, eachpresented as a 28 × 28 pixel image. A large, standard database of digits wasused for training and another large one for testing. Results surpassed those of
more conventional techniques. To see what the top level of the trained network“has in mind,” the downward-directed arrows are used to generate images atthe bottom level based on label encodings entered at the top level. Some
examples of these generated images are shown in Fig. 30.12.

551
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30

Natural Languages and Natural Scenes

Figure 30.11: Geoffrey Hinton (left) and the deep belief network (right). (Photograph from http://www.scholarpedia.org/article/Image:Geoffnew3.jpg. Network diagram and photograph used with permission of Geoffrey Hinton.)

Figure 30.12: Some images generated by the trained network. (Used with permission of Geoffrey Hinton.)
Jeff Hawkins (1957– ), the designer of the original Palm Pilot, hassuggested that the neocortex is a hierarchical temporal memory whose layers(from bottom to top) store increasingly abstract representations of sensory
input sequences and whose function (from top to bottom) is to makeincreasingly detailed predictions of future experience.43 He proposes that thevisual cortex learns in unsupervised fashion by being subjected to sequences ofimages in time. Because we see images as they occur continuously in time,
there are bound to be stretches in which each image is of the same object
moving across our visual field – albeit appearing at different translations,scales, and orientations. This sameness provides an implicit labeling that is
exploited in learning representations at all levels of the hierarchy. Furthermore,
Hawkins claims, the hierarchical memory and its learning procedures are used
not only for visual input but for other sensory modalities as well. At thehighest levels of the hierarchy these separate modalities combine to give an
integrated model of our sensory world based on vision, touch, and hearing – amodel we use to make predictions about what might be happening next.
Based on these ideas, he and Dileep George (1977– ), a Stanford Ph.D.
552
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30.2

Computer Vision

student, developed a network model they call a “Hierarchical Temporal
Memory” (HTM).” In his dissertation,44 George implemented a version of thismodel illustrated in Fig. 30.13. The bottom level is a 32 × 32 array of pixels
on which a sequence of images is presented. Level 1 consists of an 8 × 8 arrayof network nodes, with each node receiving inputs from a 4 × 4 patch of input
image pixels. For example, node “a” receives inputs from its “receptive field,”namely, the pixel patch marked “A,” and node “b” receives inputs from thepixel patch marked “B.” Level 2 is a 4 × 4 array of nodes, with each node
receiving inputs from a 2 × 2 set of level 1 nodes. This sort of set up continues
up to the single node at level 3. That node is meant to recognize the classlabels or categories of input images.

Figure 30.13: The HTM model. (Used with permission of Dileep George.)

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30

Natural Languages and Natural Scenes

The nodes in each layer are trained to recognize commonly occurring
sequences in its receptive field in the layer below. For example, the level 1
node marked “a” in Fig. 30.13 is trained to represent the probabilities offrequently occurring sequences of pixel groups in its receptive field, “A.” Onesuch high-probability sequence, for example, might be small corners moving to
the right. The nodes in level 2 are, in turn, trained to represent theprobabilities of frequently occurring sequences of the high-probability
sequences in their receptive fields in level 1, and so on. Training involves
presenting movies as input images and proceeds level by level up the hierarchy.
After training, the probabilities of the sequences represented at each level areconditioned by feedback from above. For example, if a movie is presented inwhich a small corner is moving from left to right in the pixel patch marked“A,” and if such a small corner moved in this way frequently during training,then node “a” in level 1 would predict that it will continue its motion.
As we proceed up the hierarchy of levels, each node receives inputs, albeit
indirectly, from larger and larger segments of the image. Finally, the node at
the top (level 3 in the diagram) represents a probability distribution over the
categories of images that the network has seen. When the network is operatingin “recognition mode” (after training), the top node identifies the mostprobable category of the image on the retina. The network was able to learn
to recognize a variety of simple images used by George in his dissertation
work. George is continuing his work at Numenta, a company founded by
Hawkins for the purpose of developing these kinds of networks.
Although the models described so far have been developed for perceptiontasks, they could, with some elaboration, serve as foundations for generalarchitectural schemes for intelligent agents. (See the next chapter.) To do so,
the elaborations would have to include, among other things, provisions forthem to plan and execute actions guided by their existing provisions forperception. Of course, if these models are at all relevant to how the neocortex
might work (as their proponents claim they are), then they would need to beable to do more of what the neocortex does, including planning and executing
actions. In any case, the cortical models research provides an avenue forcollaboration between AI researchers and neuroscientists. As Thomas Dean,
who has built probabilistic models of the neocortex, points out, “Theavailability of cortex-scale models will facilitate not only our understanding of
the brain but enable researchers to combine lessons learned from biology withstate-of-the-art machine-learning techniques to design hybrid systems thatcombine the best of biological and traditional computing approaches.”45
Space does not permit me to describe the work of several other prominent
vision researchers who have developed hierarchical models, but I’ll briefly
mention just a few more; the interested reader can look at their Web sites.
Tomaso Poggio and colleagues at the McGovern Institute for Brain
Research at MIT apply mathematical and statistical learning mechanisms tohelp model how the brain learns to recognize visual objects.46 One of their554
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30.2

Computer Visionapplication areas has been face recognition.47
Yann LeCun at the Computational and Biological Learning Laboratory at
the Courant Institute of Mathematical Sciences, New York University, studies
what he calls “deep architectures,” namely, ones “composed of multiple layersof trainable nonlinear modules.” One emphasis of his group is on“energy-based models” (EBMs), which are graphical models in which aconcept related to physical energy is associated with the variables (instead of
the usual probabilities).48

30.2.4

Image Grammars

Because of the successful use of grammars and syntactic analyses in natural
language processing, it is not surprising that there would be attempts to use
similar ideas for processing pictures and images. In fact, Russell Kirsch isquoted in an interview as saying “by 1957 I was intrigued by what the
linguists were able to do with grammar on computers. . . So I asked what
seemed to me to be sort of an obvious question: Could you do the same thingwith pictures?”49 Kirsch and his wife, Joan, did go on to develop a grammar
for analyzing (and producing) pictures.50 According to the interview justmentioned they used their grammar in a computer program that could “createlines and patterns in the style of [the artist Richard Diebenkorn]. When
finished, the Kirsches showed their generated image to the artist himself, who
agreed it looked strikingly similar to something he would be likely to paint. In
fact, the computer simulation was almost identical to one that Diebenkorn had
already painted.”
Other work on “picture grammars” has been done by Professor Azriel
Rosenfeld and his group at the University of Maryland.”51
Song-Chun Zhu (1969– ), who directs the UCLA Center for Image andVision Science, has applied a variety of statistical and physics-based
techniques to vision problems. He and colleagues have developed “stochastic
grammars of images,” which can be used to decompose images into theircomponent parts.52 (The decomposition method realizes some of the ideas
described in Section 30.2.3 in the work by Lee and Mumford.) Figure 30.14
shows an example of decomposing an image, represented as a parse tree.
Work on computer vision has made amazing progress in the past several
years and is an important part of many applications, including53 detecting
events (such as traffic violations), medical imaging, tracking objects (such as
faces, pedestrians, and vehicles), visual prostheses, finding objects inphotographs, inventory control in warehouses, robot vehicle navigation andmapping, character and handwriting recognition, danger warning systems,
process control, circuit board inspection, grading fruits and vegetables,
topographic mapping, forest surveys, recognizing and identifying faces in a
crowd, Internet image search, image compression, and agricultural crop
555
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30

NOTES

Figure 30.14: Parsing an image. (From Song-Chun Zhu and David Mumford, “A
Stochastic Grammar of Images,” Foundations and Trends in Computer Graphics
and Vision, Vol. 2, No. 4, pp. 259–362, 2006. Available online at http://www.stat.ucla.edu/∼sczhu/papers/Reprint Grammar.pdf.)
inspection.
Readers who would like to learn more will find a wealth of material in
textbooks, in computer vision publications, and on the Internet. A
recommended text is Computer Vision – A Modern Approach.54 A
recommended Web site is the “On-Line Compendium of Computer Vision”
maintained by Robert B. Fisher at the University of Edinburgh at
http://homepages.inf.ed.ac.uk/rbf/CVonline/. It is full of links to interestingmaterial.
After seeing all of the new AI technical apparatus described in this part of
my history of the field, you might be wondering how it can all be put together
to control agents that can reason, plan, perceive, act, and communicate in an
intelligent manner. Researchers have come up with several ways to integratecomponent technologies in what they call “architectures.” I’ll be describing
some of them in the next chapter.

Notes
1. Quoted in Daniel Jurafsky and James H. Martin, Speech and Language Processing: An

556
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30.2

NOTES

Introduction to Natural Language Processing, Computational Linguistics, and Speech
Recognition, second edition, p. 432, Upper Saddle River, NJ: Pearson Prentice Hall, 2008.
[534]
2. Ronald M. Kaplan and Joan Bresnan, “Lexical-Functional Grammar: A Formal System
for Grammatical Representation,” in Joan Bresnan (ed.), The Mental Representation ofGrammatical Relations, pp. 173–281, Cambridge, MA: MIT Press, 1982. [534]3. Aravind K. Joshi, L. S. Levy, and M. Takahashi, “Tree Adjunct Grammars,” Journal
Computer Systems Science, Vol. 10, No. 1, 1975. [534]
4. Carl Pollard and Ivan A. Sag, Head-Driven Phrase Structure Grammar, Chicago:
University of Chicago Press, 1994. [534]5. Noam Chomsky, “Some Concepts and Consequences of the Theory of Government andBinding,” Linguistic Inquiry Monograph 6, Cambridge, MA: MIT Press, 1982. [534]
6. Mark Steedman, “Categorial Grammar (Tutorial Overview),” Lingua, Vol. 90, pp.
221–258, 1993. [534]7. See Martin Kay, “The MIND System,” in Randall Rustin (ed.), Natural Language
Processing, pp. 155–188, New York: Algorithmics Press, 1973. [534]8. Jay Earley, “An Efficient Context-Free Parsing Algorithm,” Communications of the
Association for Computing Machinery, Vol. 13, No. 2, pp. 94–102, 1970. [534]9. See John Cocke and Jacob T. Schwartz, “Programming Languages and Their Compilers:Preliminary Notes,” Technical Report, Courant Institute of Mathematical Sciences, NewYork University, 1970; Tadao Kasami, “An Efficient Recognition and Syntax-Analysis
Algorithm for Context-Free Languages, Scientific Report AFCRL-65-758, Air Force
Cambridge Research Lab, Bedford, MA, 1965; and Daniel H. Younger, “Recognition andParsing of Context-Free Languages in Time n3 ,” Information and Control, Vol. 10, No. 2,
pp. 189–208, 1967. [534]
10. The reader wanting to dig deeper can consult a textbook on NLP such as Daniel
Jurafsky and James H. Martin, op. cit. [535]11. Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz, “Building a Large
Annotated Corpus of English: The Penn Treebank,” Computational Linguistics, Vol. 19, pp.
313–330, 1993. Compressed PostScript file available online at
ftp://ftp.cis.upenn.edu/pub/treebank/doc/cl93.ps.gz. [535]12. Christopher D. Manning and Hinrich Schütze, Foundations of Statistical Natural
Language Processing, Cambridge, MA: MIT Press, 1999. [535]13. Edward Sapir, Language: An Introduction to the Study of Speech, Chapter II, The
Elements of Speech, New York: Harcourt Brace, 1921. [535]
14. Eugene Charniak, Statistical Language Learning, Cambridge, MA: MIT Press, 1993.
[535]
15. Taylor L. Booth, “Probabilistic Representation of Formal Languages,” Tenth AnnualIEEE Symposium on Switching and Automata Theory, pp 74–81, 1969. [536]16. The grammar is an adaptation of one from Chapter 11 of the Manning and Schütze
book with accompanying slides available online at
nlp.stanford.edu/fsnlp/pcfg/fsnlp-pcfg-slides.pdf. As Manning and Schütze point out, theNP rules are a bit unusual because the grammar is in what is called “Chomsky Normal
Form.” [536]17. Dan Klein and Christopher D. Manning, “A∗ Parsing: Fast Exact Viterbi Parse
Selection,” Proceedings of the 2003 Conference of the North American Chapter of the
Association for Computational Linguistics on Human Language Technology – Vol. 1, pp.
40–47, Morristown, NJ: Association for Computational Linguistics, 2003. [538]557
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30

NOTES

18. Ronald Kaplan mentioned these to me in an e-mail dated June 9, 2008. [538]
19. Rens Bod, 1 “Data Oriented Parsing (DOP),” Proceedings COLING ’92, Nantes, France,
1992. See also the Data-Oriented Web page at staff.science.uva.nl/∼rens/dop.html. [538]
20. Rens Bod et al., “A Data-Oriented Approach to Lexical-Functional Grammar,” in Jan
Landsbergen (ed.), Computational Linguistics in the Netherlands 1996, Eindhoven, The
Netherlands, 1996. [538]
21. Anette Frank et al., “From Treebank Resources to LFG F-Structures: Automatic
F-Structure Annotation of Treebank Trees and CFGs extracted from Treebanks,” in Anne
Abeille (ed.), Treebanks: Building and Using Syntactically Annotated Corpora,
Dordrecht/Boston/London: Kluwer Academic Publishers, 2003. [538]22. See
http://googleresearch.blogspot.com/2006/08/all-our-n-gram-are-belong-to-you.html. [539]
23. Christopher D. Manning and Hinrich Schütze, op. cit., p. 7. [539]
24. Derek Hoiem, Alexei Efros, and Martial Hebert, “Recovering Surface Layout from an
Image,” International Journal of Computer Vision, Vol. 75, No. 1, pp. 151–172, 2007.
Available online at http://www.ri.cmu.edu/pubs/pub 5818.html. [539]25. Patricia S. Churchland, V. S. Ramachandran, and Terrence J. Sejnowski, “A Critique ofPure Vision,” in Christof Koch and Joel L. Davis (eds.), Large-Scale Neuronal Theories of
the Brain, pp. 23–60, Cambridge, MA: MIT Press, 1994. Available online at
http://papers.cnl.salk.edu/PDFs/ACritiqueofPureVision1994-2933.pdf. [540]26. Derek Hoiem, Alexei Efros, and Martial Hebert, op. cit. [541]27. I base my description on one of their many papers: Ashutosh Saxena, Sung H. Chung,and Andrew Y. Ng, “3-D Depth Reconstruction from a Single Still Image,” International
Journal of Computer Vision (IJCV), August 2007. Available online at
http://ai.stanford.edu/∼asaxena/learningdepth/saxena ijcv07 learningdepth.pdf. [543]
28. Readers interested in these details can refer to Saxena et al., ibid. [543]29. Their computer code and image data are available at
http://make3d.stanford.edu/code.html. [544]30. See Michael Isard and Andrew Blake, “CONDENSATION: Conditional Density
Propagation for Visual Tracking,” International Journal of Computer Vision, Vol. 29, pp.
5–28, 1998. Available online at
http://www.cs.cmu.edu/∼efros/courses/AP06/Papers/isard-blake-98.pdf. Also see ahomepage for the algorithm, with pointers to examples and papers, at
http://homepages.inf.ed.ac.uk/rbf/CVonline/LOCAL COPIES/ISARD1/condensation.html.
[545]
31. See http://www.cs.washington.edu/ai/Mobile Robotics/mcl/animations/floor3D.avi.
[546]
32. Dirk Schulz, Wolfram Burgard, Dieter Fox, and Armin B. Cremers, “People Tracking
with a Mobile Robot Using Sample-based Joint Probabilistic Data Association Filters,” TheInternational Journal of Robotics Research (IJRR), Vol. 22, No. 2, pp. 99–116, 2003. [546]33. See Ernst D. Dickmanns, “Dynamic Vision-Based Intelligence,” AI Magazine, Vol. 25,
No. 2, pp. 10–30, 2004. Available online at
http://www.aaai.org/ojs/index.php/aimagazine/article/viewFile/1758/1656. [547]34. The quote is from an e-mail from Sebastian Thrun, June 27, 2008. [547]35. Ernst D. Dickmanns, Dynamic Vision for Perception and Control of Motion, Berlin:
Springer-Verlag, 2007. [547]36. See http://www.eecs.berkeley.edu/Research/Projects/CS/vision/vision group.html.
[547]

558
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30.2

NOTES

37. See http://www.comp.leeds.ac.uk/vision/behaviour.html. [547]38. See http://www.comp.leeds.ac.uk/vision/cogvis/continuity.html and Brandon Bennett
et al., “Enhanced Tracking and Recognition of Moving Objects by Reasoning about
Spatio-Temporal Continuity,” Image and Vision Computing, Vol. 26, No. 1, pp. 67–81,
January 2008. Available online at
http://www.comp.leeds.ac.uk/qsr/pub/Bennett08imavis.pdf. [547]39. K. Fukushima, “Neocognitron: A Self-organizing Neural Network Model for a
Mechanism of Pattern Recognition Unaffected by Shift in Position,” Biological Cybernetics,
Vol. 36, No. 4, pp. 93–202, 1980. [548]40. Tai Sing Lee and David Mumford, “Hierarchical Inference in the Visual Cortex,”
Journal of the Optical Society of America A, Vol. 20, No. 7, July 2003. [549]41. Ulf Grenander, General Pattern Theory, Oxford: Oxford University Press, 1993. [549]42. Geoffrey E. Hinton, Simon Osindero, and Yee-Whye Teh, “A Fast Learning Algorithm
for Deep Belief Nets,” Neural Computation, Vol. 18, No. 7, pp. 1527–1554, July 2006.
Available online at http://www.cs.utoronto.ca/∼hinton/absps/ncfast.pdf. [551]43. Jeff Hawkins with Sandra Blakeslee, On Intelligence, New York: Times Books, 2004.
[552]
44. Dileep George, “How the Brain Might Work: A Hierarchical and Temporal Model for
Learning and Recognition,” Ph.D. dissertation, Department of Electrical Engineering,
Stanford University, June 2008. Available online at
http://www.numenta.com/for-developers/education/DileepThesis.pdf. [553]
45. Dean, formerly a computer science professor at Brown University, now is a scientist at
Google. His Web page at Brown is http://www.cs.brown.edu/research/projects/cortex.html.
[554]
46. See, for example, Thomas Serre, Aude Oliva, and Tomaso Poggio, “A Feedforward
Architecture Accounts for Rapid Categorization,” Proceedings of the National Academy of
Sciences (PNAS), Vol. 104, No. 15, pp. 6424–6429, 2007. Available online at
http://cbcl.mit.edu/projects/cbcl/publications/ps/serre-PNAS-4-07.pdf. [554]47. See, for example, R. Brunelli, and Tomaso Poggio, “Face Recognition: Features Versus
Templates,” IEEE PAMI, Vol. 15, pp. 1042–1052, 1993. Available online at
http://cbcl.mit.edu/people/poggio/journals/brunelli-poggio-IEEE-PAMI-1993.pdf. [555]48. See Yann LeCun et al., “A Tutorial on Energy-Based Learning,” in G. Bakir et al.
(eds.), Predicting Structured Data, Cambridge, MA: MIT Press, 2006. Available online at
http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf. [555]49. From an interview titled “Russell Kirsch: The Language of Shapes,” by Kennedy Smith,Portland’s Daily Journal of Commerce, July 28, 2006. Available at
http://www.mel.nist.gov/msid/shape.pdf. [555]50. Russell Kirsch and Joan Kirsch, “The Structure of Paintings: Formal Grammar and
Design,” Environment and Planning B: Planning and Design, Vol. 13, pp. 163–176, 1986.
Available online at http://www.nist.gov/msidlibrary/doc/kirsch 1986 structure.pdf. [555]
51. Azriel Rosenfeld, “Isotonic Grammars, Parallel Grammars, and Picture Grammars,” inBernard Meltzer and Donald Michie (eds.), Machine Intelligence 6, pp. 281–294, Edinburgh:
Edinburgh University Press, 1971. [555]52. Song-Chun Zhu and David Mumford, “A Stochastic Grammar of Images,” Foundations
and Trends in Computer Graphics and Vision, Vol. 2, No. 4, pp. 259–362, 2006. Available
online at http://www.stat.ucla.edu/∼sczhu/papers/Reprint Grammar.pdf. [555]53. David Lowe, a professor in the Computer Science Department of the University of
British Columbia, maintains a Web site of companies selling computer vision products:
http://www.cs.ubc.ca/spider/lowe/vision.html. [555]559
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30

NOTES

54. David Forsythe and Jean Ponce, Computer Vision – A Modern Approach, New York:
Prentice Hall, 2002. An online version of complete draft chapters is available at
http://decsai.ugr.es/mia/complementario/t1/book3chaps.html. [556]560
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31.0

Chapter 31Intelligent System
Architectures
Computer scientists have developed various ways to put together large
programs consisting of many specialist subprograms. The traditionalframework that controls the running of most programs involves having a main
program that runs through its instructions step by step, retrieving from andstoring data in memory, executing various operations on such data, and taking
other allowed actions. Some of the instructions in the main program might be
to “call” a subprogram, handing control over to it. The subprograms, in turn,can call other subprograms, and so on. After a subprogram finishes doing
what it has been called to do, overall control returns to the program thatcalled it, which might then call another subprogram, and so on until control
finally returns to the main program. Eventually, the main program can finallyquit running, having accomplished all that it was supposed to do, or it cancontinue running (in principle, forever) because, like a program that makes
airline reservations on demand for all who use it, its work is never done. This
scheme is the so-called von Neumann architecture.
There are many elaborations on this general idea. “Interrupts” can be
included in programs and subprograms. These are ever watchful for special
conditions within the computer system itself or in the environment –conditions, which if met, would call for control to be transferred immediately
to programs that are able to handle such conditions. Computer operating
systems, for example, depend on interrupts to be responsive to user inputs andto other things going on with the computer hardware.
The earliest AI programs ran on computers that used the von Neumannarchitecture, and thus it was natural for the architecture of the programs (thatis, the way they themselves were organized) to adhere to the von Neumannstyle of the computer’s operation. They did so even though, underneath and561
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31Intelligent System Architectures

over time, lower level programs that actually controlled the computer
gradually became more complex in ways that the programmers did not need to
notice. For example, one innovation important for running programs written
in LISP, involved making more efficient use of valuable computer storage
resources. So-called garbage collection routines scanned computer memory
from time to time to find list structures that would not ever be used again.
The memory used to store these structures could then be reclaimed to be used
to store new list structures. Program writers could ignore this aspect of lower
level computer software architecture and could go on writing their von
Neumann–style, sequentially running programs as if they had lots of available
memory.
In contrast with the von Neumann idea of executing instructions one after
another in sequence, one can conceive of an architecture in which many
instructions are executed simultaneously. One can accomplish such“parallelism,” either by actually having several hardware processors to which
programs are farmed out for execution or by the simulation of parallel
operation on the simpler von Neumann architecture in which the programs are
actually being executed in sequence but the programmers, for all they know,
think of them as running simultaneously. For example, in the nonsymbolic
world of neural networks, one could imagine groups of neural elements
operating simultaneously, even though simulations of these networks have toconsider each neural element in turn sequentially. In Pandemonium, thedemons (some implemented by neural elements perhaps and some
implemented by programs) could conceivably run in parallel, but Selfridge’s
programs had to simulate such parallelism. Simulation of parallelism can also
be accomplished by a “time-sharing” system, in which the user (or several
different users) can imagine that their programs are all running simultaneously.
A modern computer “operating system,” such as UNIX, Windows, or Mac
OS, is a very complex aggregation of programs whose organization (that is,whose architecture) must be very carefully designed. They exploit both actual
parallel hardware (as in so-called multicore systems) and time-sharing, so thatusers can run their e-mail programs, for example, simultaneously (for all theyknow) with their spreadsheet programs.
In this chapter, I’m going to describe some of the ways researchers haveorganized their programs to achieve intelligent behavior. Some of them were
inspired mainly by engineering and computational considerations and some bycognitive science in its attempt to model psychological data. Some were eveninfluenced by ideas about how various brain regions function. Parallel
operation is assumed in many of these architectures, even though it is often of
the simulated variety.

562
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31.1

Computational Architectures

31.1.1Three-Layer Architectures

I have already described how the components of one AI system, Shakey, wereorganized into high-, intermediate-, and low-level groups – a “three-layer”
architecture. In Fig. 31.1, I show how Shakey’s programs and data can begrouped into levels. Interaction among programs in these levels is illustrated
by connecting lines. All of Shakey’s perceptual and basic motor programs wereembedded in the low-level actions, whereas the intermediate-level actions
combined the low-level ones in various ways to perform certain common tasks.
The high level was in charge of planning and overall execution of plans.
Three-layered architectures, such as the one used by Shakey, were (andstill are) used in several other robot systems. As Erann Gat, a researcher who
has used these architectures at the Jet Propulsion Laboratory, points out inhis survey paper,1
The three-layer architecture arises from the empirical observationthat effective algorithms for controlling mobile robots tend to fall
into three distinct categories: 1) reactive control algorithms which
map sensors directly onto actuators with little or no internal state,2) algorithms for governing routine sequences of activity which rely
extensively on internal state but perform no search, and 3)
time-consuming (relative to the rate of change of the environment)
search-based algorithms such as planners.
Several of the three-layer architectures described by Gat are based on R.
James Firby’s three-layer scheme using “Reactive Action Packages” (RAPs).2
RAPs are quite similar to teleo-reactive programs in that they group together
and describe all known ways to carry out a task in different situations.3
A modern example of a three-layered architecture is the one used by theGerman driverless “seeing passenger car,” VaMoRs-P, described by Ernst D.
Dickmanns and colleagues.4 One of the architecture diagrams for their system
is shown in Fig. 31.2.

31.1.2Multilayered Architectures

As an alternative to the three-layered schemes, all of which involved aplanning level, Rodney Brooks and others proposed architectures that
controlled robot actions in a way that reacted directly to changes in theenvironment (as sensed) without the need for planning. Originally called“subsumption architectures,” these were later called “behavior-based”
because they were composed of specifically programmed robot behaviors.5
One type of behavior-based architecture is illustrated in Fig. 31.3.
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31Intelligent System Architectures

Figure 31.1: Shakey’s three-level architecture.
The different behaviors, for example “wander,” “avoid obstacles,” and“explore,” are arranged in levels, each responsive to its own set ofenvironmental stimuli and each able to control the robot depending on thesensed situation. This close coupling and interaction with what is going on in
the environment causes what some have called “emergent behavior.” As Maja
Matarić and François Michaud put it,6
For example, a robot that flocks with other robots may not have a
specific flocking behavior; instead, its interaction with the564
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31.1

Computational Architectures

Figure 31.2: A three-layered architecture for a driverless automobile. (Usedwith permission of Ernst D. Dickmanns.)

Figure 31.3: A behavior-based architecture.
environment and other robots may result in flocking, although its
only behaviors may be avoid-collisions, stay-close-to-the-group, andkeep-going.
James Albus (1935– ; Fig. 31.4), at the National Institute of Standards
and Technology (formerly the National Bureau of Standards), developed what
he called a “reference model architecture.” The architecture consists ofmultiple layers of “real-time control systems” (RCSs) developed earlier at
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31Intelligent System Architectures

NIST as components of a “theory of intelligence.”7 (Albus claims that his RCS
model was originally inspired by a model of the cerebellum that he and David
Marr devised.8 ) Each RCS “partitions the control problem into four basic
elements: behavior generation (or task decomposition), world modeling,
sensory processing, and (in more recent versions) value judgment. It clusters
these elements into computational nodes that have responsibility for specificsubsystems, and arranges these nodes in hierarchical layers such that eachlayer has characteristic functionality and timing.”9

Figure 31.4: James Albus. (Photograph courtesy of James Albus.)
A layered structure of RCSs, called NASREM (for NASA/NBS Standard
Reference Model), was proposed (but not implemented as far as I know) as thearchitecture for a flight tele-robotic servicer on the space station. It is
illustrated in Fig. 31.5. In each layer, the RCS units have sensory processing(SP) components, world modeling (WM) components, and task decomposition(TD) components. The lowest layer RCS is essentially a servo controller; as
one moves up the hierarchy, the RCSs handle increasingly strategic tasks.
Albus and his team at NIST developed a variety of architectures using layered
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Computational Architectures

RCSs.

Figure 31.5: The NASREM architecture. [From Figure 2 of James S. Albus, “A
Reference Model Architecture for Intelligent Systems Design,” in P. J. Antsaklis and K. M. Passino (eds.), An Introduction to Intelligent and Autonomous
Control, Chapter 2, pp. 27–56, Dordrecht: Kluwer Academic Publishers, 1993.]
Inspired by Albus’s architecture, I developed one I called the“triple-tower architecture” illustrated in Fig. 31.6.10 The lowest level of thecentral Model Tower receives inputs through sensors directly from theenvironment and stores them as primitive perceptual predicates. Programs(represented as rules) in the Perception Tower rerepresent these primitive
predicates as more abstract ones – adding them to the Model Tower. This
process of creating higher and higher level abstractions proceeds in stair-stepfashion up the Perception and Model Towers. In the Action Tower, the lowest
level action routines are simple reflexes, evoked by predicates in the Model
Tower corresponding to the primitive predicates. More complex actions are
evoked by more abstract predicates appropriate for those actions. High-level
actions call other actions until the process bottoms out at the primitive
actions that actually affect the environment. The actions in the Action Tower
were all to be programmed using my teleo-reactive language (see p. 419). The
perceived effects of these actions, in turn, change the values of predicates in
the Model Tower, evoking, perhaps, different actions. To model faithfully
ongoing environmental changes, a truth-maintenance system (TMS) is
included as part of the model tower. The TMS continuously deletes predicates
567
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31Intelligent System Architecturesand values from the Model Tower that are no longer derivable (through theperceptual rules) from the then-present components of the Model Tower. The
only implementation of this architecture that I know of was to control a
block-stacking simulated robot.11

Figure 31.6: Triple-tower architecture.
I have already described the Blackboard architecture, devised at Carnegie
Mellon University for its HEARSAY-II speech understanding system (see p.
279). It was also used in the HASP/SIAP system for ocean surveillance (see p.
321). As I quoted Russell and Norvig earlier, “Blackboard systems are thefoundation of modern user interface architectures.” They are also used inseveral computer applications, including, for example, an automatic genome
annotation system for predicting gene locations and structures.12
To review what I said earlier, a Blackboard is a layered memory structurein which programs, called “knowledge sources” (KSs) can read data from andwrite data into the various layers. (See Fig. 17.5.) Typically, a KS might look
for and then read some data from one or more layers, do some computations
using that data, and then write results of those computations into one or morelayers. A controller decides which KS, of those which see data upon which
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Computational Architectures

they can act, should be active. In some applications, several KSs can be activeat once. The result of all of this is a very dynamic process in which the dataon the Blackboard are constantly evolving, eventually producing desired
information, such as the prediction of a gene location, recognition of asentence, or interpretation of ocean sonar signals. Because information at one
layer of the Blackboard can cause, through the KSs, other information toappear at any other level, the Blackboard architecture foreshadowed the
upward and downward propagation of probabilities in the cortical models I
described earlier.

31.1.3

The BDI Architecture

Michael Georgeff (1946– ; Fig. 31.7) and others have proposed agent
architectures based on the philosophical concepts of beliefs, desires, andintentions.13 These are the so-called BDI architectures. An agent’s beliefs
represent its knowledge about its environment (including itself and otheragents), usually expressed in some kind of logical language, such as thefirst-order predicate calculus. (The word “belief” is used instead of“knowledge” because an agent’s beliefs are subject to change and might notaccurately model its environment.) An agent’s desires represent the agent’s
goals – situations that it wants to achieve. An agent’s intentions represent
those desires that the agent has actually chosen to begin to achieve. That is, it
has begun executing a plan to achieve them. BDI architectures, as distinct
from behavior-based, reactive ones for example, explicitly represent beliefs,
desires, and intentions as actual data structures.
Stated in such general terms, some of the architectural schemes I havealready mentioned can be thought of as BDI architectures. Shakey, forexample, had beliefs (its world model), at any time it was given a desire (itsgoal), and its executive system sometimes was in the process of executing aplan (its intention) to achieve that goal. Georgeff and colleagues, however,proposed a specific version of a BDI architecture, which they called a
Procedural Reasoning System (PRS).14 I illustrate it in Fig. 31.8.
Here, in brief, is how the architecture works. (For more detail, see theGeorgeff and Ingrand paper.)• The database consists of the agent’s current beliefs about its environment(including itself) and its subject area. Some beliefs are installed initiallyby the designer and some are obtained by the agent through its
perceptual apparatus and by its inference mechanisms. In PRS, beliefs
are represented by expressions in first-order predicate calculus.
• Goals (the agent’s desires) are conditions to be achieved and can referboth to the external world and to internal states of the agent.
• The KA library of plans contains what are called “Knowledge Areas”
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31Intelligent System Architectures

Figure 31.7: Michael Georgeff. (Photograph courtesy of Michael Georgeff.)(KAs). Each KA is a specific procedure specifying a plan foraccomplishing a task, such as picking up an object. A KA consists of abody, which describes the steps of the procedure, and an invocation
condition, which specifies under what situations the KA can be usefully
applied. “Primitive” KAs have no bodies but refer to actions directly
performable by the system. There are also “metalevel” KAs that canchoose among multiple applicable KAs, modify and manipulate
intentions, and compute the amount of reasoning to be devoted to a
problem, given real-time constraints.
• The intention structure contains tasks that the system has chosen to
execute. An intention is expressed as a main KA along with all the
sub-KAs that are used to execute the main KA.
• The interpreter runs the system. It maintains the other components ofthe system and chooses an intention from the intention structure (a KA)for execution. One feature of PRS is that the execution of a KA may beinterrupted by certain perceived situations (such as emergencies), giving
it the ability to react rapidly to unanticipated changes in the570
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Computational Architectures

Figure 31.8: PRS, a BDI architecture. (From Michael P. Georgeff and François
F. Ingrand, “Decision-Making in an Embedded Reasoning System,” Proceedingsof the Eleventh International Joint Conference on Artificial Intelligence, pp.
972–978, August, 1989.)
environment.
There have been several applications of the PRS-style architecture,including handling malfunctions of the space shuttle15 and control of anautonomous robot.16
Along with the specific architectural ideas I have just described there havebeen many other general suggestions for how to organize intelligent systems,
some of which have resulted in running programs (or at least programming
languages in which one could write running programs). There have been
several proposals for systems capable of what is called “meta-level reasoning,”that is, reasoning about how to reason. Of these, I’ll mention Brian Smith’s
3-LISP system17 and Richard Weyhrauch’s FOL system,18 both of which werecapable of “reflecting” on their own processes. Meta-level reasoning systems
have also been proposed by Pat Hayes,19 Michael Genesereth,20 and Stuart
Russell and Eric Wefald.21
Important considerations in the meta-level problem of deciding how best
to solve a base-level problem involve estimates of the expected costs andbenefits of different solution methods. Eric Horvitz (1958– ) pioneered theapplication of “decision theory to control the solution of difficult problems
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31Intelligent System Architectures

given limitations and uncertainty in reasoning resources.”22 The use ofprobabilistic methods and decision theory in meta-level reasoning has since
become an important part of AI research. The journal Artificial Intelligencedevoted a special issue to the topic in 2001.23
Marvin Minsky’s ideas about a “Society of Mind”24 are also suggestive
about potential designs for intelligent systems even though they were notsufficiently specific for immediate implementation. Such a “society” would becomposed of a large number of simple “agents,” none of which was powerful orcomplete enough to be an intelligent entity itself, but a “mind” would
presumably emerge from their joint behaviors and interactions. In a similarvein, William Kornfeld and Carl Hewitt suggested that an intelligent system
ought to be organized in a manner similar to a “scientific community,”
exploiting individual and parallel research, publication, and criticism.25

31.1.4

Architectures for Groups of Agents

It is to be expected that intelligent agents will exist in environments
containing other intelligent agents, both humans and machines. Many of theseagents will collaborate or compete in the performance of their tasks.
Agent-to-agent communication strategies and multiagent architectures havebecome important AI topics.
I have already paid some attention to interactions between AI systems
and humans. These interactions use restricted versions of natural language orsome other kind of user interface apparatus. Indeed, the world is full ofcomputers communicating with other computers over networks using speciallydesigned protocols. What I want to talk about here is how AI methods areused to enable more flexible and effective communication among AI agentsthan would be possible with fixed communication and organizational
protocols. AI agents should be able to plan their communications to otheragents along with planning their other actions. Moreover, they must be able tointerpret communications from other agents along with interpreting otherperceptual data. To do so they must take into account the expected actions,knowledge, and goals of other agents.
Some of the early work in what is now called “multiagent systems”(previously, “distributed AI”) was done by Victor Lesser (1944– ) at the
University of Massachusetts and Lee Erman (1944– ) at Carnegie Mellon
University. They had adapted Blackboard architecture ideas from HEARSAY-II
to develop a system they called DISTRIBUTED HEARSAY-II. It was a
combination of several distributed Blackboards, each with its own KSs,
communicating among themselves to process noisy signals arising from anumber of distributed sources.26 Lesser and Erman envisioned applications inseveral areas including “sensor networks (composed of low-power radar,acoustic, or optical detectors, seismometers, hydrophones, etc.), network(automotive) traffic control, inventory control (for example, car rentals), power
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Computational Architectures

network grids, and tasks using mobile robots.” As they pointed out, “anarchitecture that locates processing capability at the sensor sites and requires
only limited communication among the processors is especially advantageousand is, perhaps, the only way to meet the demands of real-time response,
limited communication bandwidth, and reliability.”
Lesser and colleagues continue work on multiagent systems at the MAS
Lab at the University of Massachusetts in Amherst. Building on the
DISTRIBUTED HEARSAY-II work, they developed the “Distributed Vehicle
Monitoring Testbed” (DVMT).27 Research with the testbed focused on
tracking vehicle motion using a distributed sensor network and was a resource
for testing methods of cooperative distributed problem solving. The DVMT
work was followed by a number of other multiagent systems projects.28
During the late 1970s and into the 1980s several ideas were developed for
coordinating the activities of multiple agents. One of these was the Contract
Net system developed by Reid Smith.29 It was based on a protocol and a“negotiation process” for “problem-solving communication and control fornodes in a distributed problem solver.” An early application involved a
distributed sensor network in which the locations and types of sensors were
not known until after sensor deployment.30
Another important system was the “Multi-Agent Computing
Environment” (MACE) developed by Les Gasser and colleagues at the
University of Southern California.31 The paper about MACE describes it asfollows:
MACE. . . is an instrumented testbed for building a wide range ofexperimental Distributed Artificial Intelligence systems. . . . MACE
computational units (called “agents”) run in parallel, andcommunicate via messages. They provide optional facilities forknowledge representation (world knowledge, models of otheragents, their goals and plans, their roles and capabilities, etc.) andreasoning capabilities.
An interesting application for multiagent systems research involves
cooperative (and competitive) robots. Professor Manuela Veloso (1957– ; Fig.
31.9) at Carnegie Mellon University is one of the major researchers working inthis area. She has, in addition to her work on “research on intelligent robots
that Cooperate, Observe, Reason, Act, and Learn,”32 been active in the
RoboCup matches of soccer-playing robots. Typically in these matches eachrobot has its own sensing and processing capabilities. Each needs to take into
account the actions of other players and what they might do.
RoboCup is “an international joint project. . . to foster AI and intelligent
robotics research by providing a standard problem where [a] wide range oftechnologies can be integrated and examined.”33 Its ultimate goal is to“develop a team of fully autonomous humanoid robots that [by 2050] can win
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31Intelligent System Architectures

Figure 31.9: Manuela Veloso (top) and soccer-playing Aibo robots (bottom).
(Photographs courtesy of Manuela Veloso.)
against the human world champion team in soccer.” Some of you will bearound then to see.
When the environment includes other agents with whom an agent mustcooperate or compete, it is important for that agent to have models of thoseother agents as part of its environmental model. These models should include
information about what other agents believe and how those beliefs might bemodified. To deal with matters like these, researchers began to consider
problems such as how an agent A should represent for itself that agent B
knows some fact P and under what circumstances agent A should tell somefact, P, to agent B. One major difficulty was how to distinguish between A
knowing that B knows (P ∨ Q) and A knowing either that B knows P or that
B knows Q. Another was how agent A can reason about telling agent B a fact
about some object, say OB, when A does not know the name that B uses for
OB. Yet another concerned what A could assume about the conclusions that B
might reach by B’s own reasoning processes. Various solutions were proposed.

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Computational Architectures

The most prominent among them involved what is called “epistemic logic”(logic of belief).34
An agent that has knowledge about what another agent knows and what
it might conclude is in a position to attempt to add to, correct, or learn fromthat other agent’s knowledge. Adjusting and learning from another agent’s
knowledge is key to cooperation among agents and requires communicationfrom the sender to the receiver and understanding and possible compliance by
the receiver. Researchers noted that there were several types ofcommunication actions. They are usually called “speech acts” even whencommunication is by means other than speech. Many of these types had beenclassified earlier by John Searle following the work of John L. Austin.35 Chiefamong these for use by multiple agents are “assertives” for transmitting facts
from one agent to another, “directives” for requesting or commanding thereceiver to take some action, and “commissives” for promising that the senderis committing to some action.
Once communication between agents is regarded in terms of actions, one
can think about generating plans using these actions. Philip R. Cohen(1950– ) at BBN and C. Raymond Perrault (1949– ) at the University ofToronto were among those who did just that.36 They dealt in particular withthe speech acts REQUEST and INFORM (based on the earlier “assertives” and“directives”) and proposed conditions under which those acts could beexecuted and what their effects would be. Conditions and effects were statedin terms of logical expressions occurring in (or derivable from) the knowledge
bases of the sender and receiver. (Both the sender and receiver were assumed
to have knowledge about the knowledge of each other and that they could
reason with that knowledge.) A planning system, somewhat like STRIPS, could
then generate plans consisting of instances of those speech acts that would
achieve desired effects.
Cohen’s and Perrault’s speech acts formed the basis of KQML, an acronym
for Knowledge Query Manipulation Language. KQML was developed under
DARPA support under its “knowledge sharing initiative.”37 It defines various
communicative actions that can take place between agents, such as ask-if,
inform, tell, and reply.
KQML uses KIF (Knowledge Interchange Format), a language based on
first-order predicate calculus, for expressing the content of a message.38 So,
when agent A wants to send a message to agent B, it encodes the content of
the message in KIF and then wraps it in the appropriate KQML communicative
action. For example, here is a typical KQML/KIF dialog:39

A to B: (ask-if (> (size chip1) (size chip2)))
B to A: (reply true)
B to A: (inform (= (size chip1) 20))
B to A: (inform (= (size chip2) 18))
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31Intelligent System Architectures

Motivated by projects such as KQML and KIF, the Foundation for Intelligent
Physical Agents (FIPA) was formed in Switzerland in 1996. FIPA is now oneof the standards committees of the IEEE Computer Society. Its standards are“intended to promote the interoperation of heterogeneous agents and theservices they can represent.” They deal with “Agent Communication
Language” (ACL) messages and provide for “message exchange interaction
protocols, speech act theory-based communicative acts, and content language
representations.”40
There are now several systems and languages for implementing multiagent
systems. For example, the open source Jason interpreter for the logic-based
language AgentSpeak provides a platform for users to build complex
multiagent systems.41
Although much work on multiagent systems has concentrated on
applications in which the several agents cooperate to solve some overall
problem, it is also the case that agents can be self-interested, which can leadto competition among them. Opposing teams of soccer-playing robots are one
example. Other examples are agents that engage in commerce such as buying
and selling (presumably acting for humans). These aspects of multiagent
research involve negotiations and auctions. There is a well-known framework,
namely, game theory, for dealing with situations in which an agent’s success inmaking choices depends on the choices of other agents. Game theory was
introduced into multiagent systems research by Jeff Rosenschein (1957– ) andMichael Genesereth (1948– ) in their paper “Deals among Rational Agents.”42
Now, multiagent systems comprise a major subtopic of AI, and speech-act
theory and game theory are among its important theoretical underpinnings.43

31.2

Cognitive Architectures

31.2.1

Production Systems

Allen Newell and Herb Simon were among the first to be interested incomputational models of human problem solving. I have already described
GPS, the General Problem Solver (see p. 121), which can be considered one of
the first architectures for cognitive processes. I’ll describe a few other so-called
cognitive architectures in this section. As the developers of one family of thesearchitectures later put it,44[A cognitive architecture is] the fixed base of tightly-coupledmechanisms underlying intelligent behavior. [Such an] architecturethen forms the basis for wide-ranging investigations into basic
intelligent capabilities – such as problem solving, planning,learning, knowledge representation, natural language, perception,and robotics – as well as applications in areas such as expert
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31.2

Cognitive Architecturessystems and psychological modeling.

Following their work on GPS, Newell and Simon focused on “production
systems,” models that used IF–THEN rules, called productions. I first talked
about rules of this kind when I described expert systems in Section 18.2.
There, the rules were used mainly for inference, either forward to produce
inferred statements or backward to produce subgoals. Newell and Simon wereinterested in the use of these rules for producing actions. The IF part was, as
usual, a condition, and the THEN part was an action, which was executed if
the condition was satisfied in a model. (My teleo-teactive programs describedin Section 25.4.2 also used these action-producing rules.) Newell and Simon
conceived of an architecture consisting of two kinds of memory structures.
One, a “long-term memory,” consists of the production rules. The other, ashort-term or “working memory,” holds the dynamic information about thetask being worked on. The long-term memory persists over time and might
contain thousands of rules. The working memory contains the data to betested by the condition parts of the rules. When the condition part of a rulematches data in the working memory, that rule “fires”; that is, its action partis executed. Execution may result in writing or erasing (or both) some data in
the working memory or taking some action in the external environment. Whendata in the working memory are changed, different rules are fired, whichchange the data again, and so on.
Figure 31.10 shows a simple version of this production-rule architecture.
In case more than one rule’s condition part matches data in the working
memory (which would be usual), the “Conflict Resolver” chooses which one(or ones, in case of parallel operation) should fire. In this version of thearchitecture, which produces actions in the external environment, a“Perception” system is able to write data into the Short-term Memory torepresent any salient features of the environment’s current state.
Newell and Simon did extensive experimental work with human subjects
performing problem-solving tasks – showing that their performance could bewell modeled by the operation of versions of this architectural scheme. Theirbook, Human Problem Solving, is an account of much of this work.45 Mostly,
they considered their production-system architecture a contribution to thescientific study of human cognition, not a proposal for how to structure AI
systems. In commenting on their book, Newell later wrote “The aim was tomake the case that psychology was being done, not something that could be
pigeon-holed as associated with computers.”46 However, others did associate
production systems with computers, most notably through the use of the OPS5
computer language.
Later proposals for cognitive architectures, namely, ACT-R and SOAR,
were influenced by the production system model and were used both as models
of problem solving and as architectures for AI systems. I’ll describe thosesystems next.
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31Intelligent System Architectures

Figure 31.10: A production rule architecture.

31.2.2

ACT-R

John R. Anderson (Fig. 31.11) and others have been developing a series ofcognitive architectures called ACT (Adaptive Control of Thought) at Carnegie
Mellon University.47 (As I mentioned in Section 11.4, when Anderson was astudent at Stanford, he and Gordon Bower developed a theory of humanassociative memory, HAM, which can be regarded as a precursor to his ACT
work.) The latest in this series of models is ACT-R (“R” standing for rational).
According to its Web site,48
ACT-R is a cognitive architecture: a theory for simulating andunderstanding human cognition. Researchers working on ACT-R

strive to understand how people organize knowledge and produce
intelligent behavior. As the research continues, ACT-R evolves evercloser into a system which can perform the full range of humancognitive tasks: capturing in great detail the way we perceive,think about, and act on the world.

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31.2

Cognitive Architectures

Figure 31.11: John Anderson. (Photograph courtesy of John Anderson.)
The basic idea of the ACT-R architecture is illustrated in Fig. 31.12.
There are three main components: modules, buffers, and a pattern matcher.
The Motor Module can act on the Environment through motor routines or on
the ACT-R Buffers. Besides the Visual Module, which is illustrated, there may
be other perceptual modules for audition, touch, and so on.
There are two types of memory modules in ACT-R. Declarative Memory
consists of facts, such as “Washington, DC is the capital of the United States,
France is a country in Europe, or 2 + 3 = 5.” Declarative knowledge isrepresented in ACT-R by units called chunks. Procedural Memory consists of579
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31Intelligent System Architectures

production rules representing “knowledge about how we do things: for
instance, knowledge about how to type the letter ‘Q’ on a keyboard, about
how to drive, or about how to perform addition.”49 Often we are unable toverbalize our knowledge about how we do certain things: We just do them;thus knowledge about them is considered procedural, not declarative.
Buffers in ACT-R serve as interfaces between modules. “The contents ofthe buffers at a given moment in time represent the state of ACT-R at thatmoment. . . The pattern matcher searches for a production that matches thecurrent state of the buffers. Only one such production can be executed at agiven moment. That production, when executed, can modify the buffers andthus change the state of the system. Thus, in ACT-R cognition unfolds as a
succession of production firings.”
According to one of the ACT-R Web sites,50
ACT-R is a hybrid cognitive architecture. Its symbolic structure is a

production system; the subsymbolic structure is represented by aset of massively parallel processes that can be summarized by anumber of mathematical equations. The subsymbolic equations
control many of the symbolic processes. For instance, if several
productions match the state of the buffers, a subsymbolic utility
equation estimates the relative cost and benefit associated witheach production and decides to select for execution the production
with the highest utility. Similarly, whether (or how fast) a fact canbe retrieved from declarative memory depends on subsymbolic
retrieval equations, which take into account the context and thehistory of usage of that fact. Subsymbolic mechanisms are also
responsible for most learning processes in ACT-R.
ACT-R models have been used to explain and simulate a wide variety ofcognitive behaviors in humans, including learning, language processing,perception, problem solving, and decision making. There are hundreds (if not
thousands) of papers describing this work.51 Applications of ACT-R cover awide range of topics from, for example, “predicting the effects of cellular-phonedialing on driver performance” to intelligent tutoring systems. In applications
more directly related to AI, Greg Trafton and Alan C. Schultz at the Navy
Center for Applied Research in Artificial Intelligence (NCARAI) have beenbuilding an “embedded cognitive robot” using a version of ACT-R they call
ACT-R/E. It includes “visual and auditory modules” and “motor and spatial
modules” for perception and action.52

In addition to its role in explaining psychological processes, functional
magnetic resonance imaging (fMRI) studies have been used to associatecomponents of the ACT-R architecture with brain regions that are active incomplex tasks.53

580
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31.2

Cognitive Architectures

Figure 31.12: The basic ACT-R architecture. (Used with permission of John
Anderson.)
ACT-R software, together with reference materials and tutorials, isavailable from http://act-r.psy.cmu.edu/actr6/.

31.2.3SOAR

In the early 1980s, John Laird (1954– ; Fig. 31.13), Allen Newell, and Paul
Rosenbloom (1954– ; Fig. 31.13) began development of a series of cognitive
architectures called SOAR (which originally, it is said, was an acronym forState, Operator And Result). SOAR’s developers said that their “ultimate goal
for the SOAR architecture is that it serve as a basis for both human andartificial cognition.”54 Like ACT, SOAR evolved from Newell and Simon’s work
on GPS and production systems and included ideas involving problem spaces,
heuristic search, cognitive skill acquisition, and learning. Laird andRosenbloom were Ph.D. students of Newell’s and completed dissertations on
aspects of SOAR.55 The SOAR architecture and its applications are described inbooks and in several hundred articles56 and on the SOAR Web sites accessible
from http://sitemaker.umich.edu/soar/home.
There have been a series of SOAR architectures, SOAR1 (in 1982) throughSOAR9 (2008), each with improvements on its predecessor.57 Some of thelatest versions of the SOAR software can be downloaded from581
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31Intelligent System Architectures

Figure 31.13: John Laird (left) and Paul Rosenbloom (right). (Photographs
courtesy of John Laird and of Paul Rosenbloom.)
http://sitemaker.umich.edu/soar/soar software downloads. Understanding
exactly how these various versions of SOAR work can best be gained by tracing
through some of the examples in articles and papers about SOAR. I’ll limit mybrief account here to mentioning some of the main ideas.
SOAR is something like a programming language having a fixed set ofroutines. Different kinds of tasks can be “programmed” in SOAR using theseroutines. Examples range from AI’s favorite “toy” problems (such as theeight-puzzle) to “real-world” applications (such as configuring computer
systems and robot control). SOAR solves each task given to it by creating andsolving a hierarchy of subtasks. Each task (including the main one and each of
the subtasks) is posed as the goal of finding a desired state in a “problem
space” consisting of a set of operators that apply to a current state to produce
a new state.

To set up a problem space, SOAR needs to know its current state andwhat operators can be applied to that state. If it does not know these things
directly, say from prior experience, it sets up a subsidiary problem space
whose goal is to discover them (and so on). Once SOAR has defined a problem
space, it must select an operator to apply to the current state in that space. If
it does not already know which operator to apply, it sets up a subsidiary
problem space whose goal is to find out. As stated in the volume of SOAR
papers, “SOAR’s mechanisms form a tightly coupled hierarchy of layers –memory, decision, and goal – in which each layer forms the inner loop of thelayer above it. These layers increase progressively in both complexity and timescale from the bottom to the top of the hierarchy.”582
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31.2

Cognitive Architectures

Setting up subsidiary problem spaces is called “universal subgoaling,”
which can result in a deep tree of subgoals and problem spaces. When these
involve control decisions (such as which operator to apply), SOAR can be said
to be “reflecting” on its own problem-solving behavior. The process of
universal subgoaling can invoke a variety of so-called weak methods, such as
hill climbing, means–ends analysis, and heuristic search, depending on theknowledge SOAR has previously learned about the kind of task it is working on.
A production system, with the usual long-term and short-term memory
structures, is used to set up problem spaces. The long-term memory (LTM)
stores information that is independent of the current situation. The
short-term or working memory (WM) holds information that is most relevantto the current situation. SOAR learns by caching results of its problem-solving
experiences, both as productions in its LTM and as general facts in WM about
previous situations that might be useful in future situations. Assemblinglearning sequences of previously experienced problem-solving traces is called“chunking,” a term sometimes used to describe analogous learning in humans.
Relationships between these memory structures (for a recent version of
SOAR) are shown in Fig. 31.14.

Figure 31.14: Memory structures in SOAR. (Illustration used with permission ofJohn Laird and Paul Rosenbloom.)
There are three kinds of LTM structures, namely, procedural, semantic,
583
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31Intelligent System Architecturesand episodic. Here is how one of the SOAR papers describes them:58
Procedural knowledge is about how and when to do things – how
to ride a bike, how to solve an algebra problem, or how to read arecipe and use it to bake a cake. Semantic knowledge consists offacts about the world – things you believe to be true in general –things you “know,” such as bicycles have two wheels, a baseballgame has nine innings, and an inning has three outs. Episodic
knowledge consists of things you “remember” – specific situations
you’ve experienced, such as the time you fell off your bicycle andscraped your elbow. LTM is not directly available, but must be“searched” to find what is relevant to the current situation.
To say this a little more intuitively, it is useful to think about LTM
as containing what can be relevant to many different situations but
must be explicitly retrieved, and WM as containing what themodel thinks is relevant to the particular situation it is currently
in. One of the key distinctions between WM and LTM is thatknowledge in working memory can be used to retrieve otherknowledge from LTM, whereas LTM must first be retrieved into
WM. Knowledge moves from LTM to WM by both automatic anddeliberate retrieval of relevant LTM structures.
The SOAR architecture (in its various versions) has been used byresearchers all over the world for a variety of tasks. The intention of thepeople working on SOAR is to enable it to59
• work on the full range of tasks expected of an intelligent agent, fromhighly routine to extremely difficult, open-ended problems,• represent and use appropriate forms of knowledge, such as procedural,declarative, episodic, and possibly iconic,• employ the full range of problem-solving methods,• interact with the outside world, and• learn about all aspects of the tasks and its performance on them.
This is a tall order so stay tuned. One interesting SOAR application area has
been in programming automated agents as stand-ins for humans in simulated
training exercises. For example, TacAir-SOAR simulates the intelligent behaviorof a tactical fighter pilot.60 In 1998, Laird founded SOAR Technology, an Ann
Arbor (Michigan) company specializing in creating autonomous AI entities
using the SOAR architecture.
ACT-R and SOAR are probably the most prominent cognitive architectures,
but there are others also. The field is well surveyed in a paper by Pat Langley,
John Laird, and Seth Rogers.61

584
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31.2

NOTES

Notes
1. Erann Gat, “On Three-Layer Architectures,” in David Kortenkamp, R. Peter Bonnasso,
and Robin Murphy (eds.), pp. 195–210, Cambridge, MA: MIT Press, 1998. Available online
at http://www.flownet.com/ron/papers/tla.pdf. [563]
2. R. James Firby, “Adaptive Execution in Dynamic Domains,” Ph.D. dissertation, Yale
University, Computer Science Department Technical Report No. 672, 1989. For a summary,see R. James Firby, “An Investigation into Reactive Planning in Complex Domains,”
Proceedings of AAAI-87, pp. 202–206, 1987. [563]
3. Several other architectures for controlling robots, including three-layered ones, aredescribed in Bruno Siciliano and Oussama Khatib (eds.), Springer Handbook of Robotics,
Berlin and Heidelberg: Springer-Verlag, 2008. [563]
4. Ernst D. Dickmanns et al., “The Seeing Passenger Car ‘VaMoRs-P,’” Proceedings of theIEEE 1994 Symposium on Intelligent Vehicles, pp. 68–73, 24–26 October 1994. [563]5. For a review, see Chapter 38 of Bruno Siciliano and Oussama Khatib (eds.), op. cit..
[563]
6. Ibid, p. 895. [564]7. See James S. Albus, “Outline for a Theory of Intelligence,” IEEE Transactions on
Systems, Man, and Cybernetics, Vol. 21, No. 3, May/June 1991. [566]8. See James S. Albus, “A New Approach to Manipulator Control: The Cerebellar Model
Articulation Controller (CMAC),” Journal of Dynamic Systems, Measurement and Control,Vol. 97, American Society of Mechanical Engineers, pp. 220–227, September 1975; a version
is available online at http://www4.cs.umanitoba.ca/∼jacky/Robotics/Papers/
Albus-ANewApproachForManipulatorControlCMACS.pdf. [566]9. James S. Albus, “A Reference Model Architecture for Intelligent Systems Design,” in P.
J. Antsaklis and K. M. Passino (eds.), An Introduction to Intelligent and Autonomous
Control, Chapter 2, pp. 27–56, Dordrecht: Kluwer Academic Publishers, 1993. [566]
10. Nils J. Nilsson, “Teleo-Reactive Programs and the Triple-Tower Architecture,”
Electronic Transactions on Artificial Intelligence, Vol. 5, Section B, pp. 99–110, 2001.
PostScript version available online at http://www.ep.liu.se/ej/etai/2001/006/. [567]11. Ibid. [568]12. See Stéphane Descorps-Declère et al., “Genepi: A Blackboard Framework for Genome
Annotation,” BMC Bioinformatics, Vol. 7, pp. 450ff, October 12, 2006. Available online at
http://www.biomedcentral.com/1471-2105/7/450. [568]13. For a philosophical treatment of these ideas see Michael E. Bratman, Intention, Plans,and Practical Reason, Stanford CA: CSLI Publications, 1999. [569]
14. There are several papers and reports about PRS. One is Michael P. Georgeff andFrançois F. Ingrand, “Decision-Making in an Embedded Reasoning System,” Proceedings ofthe Eleventh International Joint Conference on Artificial Intelligence, pp. 972–978, August
1989. Available online at http://www.agent.ai/download.php?ctag=download&docID=147
and at http://www.ai.sri.com/pubs/files/493.pdf. [569]
15. Michael P. Georgeff and Amy L. Lansky, “A System for Reasoning in Dynamic
Domains: Fault Diagnosis on the Space Shuttle,” SRI AI Center Technical Note 375, 1986.
Available online at http://www.ai.sri.com/pubs/files/584.pdf. [571]
16. Michael P. Georgeff and Amy L. Lansky, “Reactive Reasoning and Planning: An
Experiment with a Mobile Robot,” Proceedings of the Sixth National Conference on
Artificial Intelligence, 1987. A longer version is Michael P. Georgeff, Amy L. Lansky, andMarcel J. Schoppers, “Reasoning and Planning In Dynamic Domains: An Experiment with

585
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31

NOTES

A Mobile Robot,” Technical Note 380, AI Center, SRI International, April 1987. Available
online at http://www.ai.sri.com/pubs/files/579.pdf. [571]
17. Brian Cantwell Smith, “Reflection and Semantics in a Procedural Language,” MIT
Ph.D. dissertation and MIT Laboratory of Computer Science Technical Report 272, 1982.
Available online at http://publications.csail.mit.edu/lcs/pubs/pdf/MIT-LCS-TR-272.pdf.
[571]
18. Richard Weyhrauch, “Prolegomena to a Theory of Formal Reasoning,” Artificial
Intelligence, Vol. 13, Nos. 1 and 2, pp. 133–176, April 1980. Available also as Stanford
Computer Science Department Technical Report CS-TR-78-687, 1978, at
http://www-db.stanford.edu/TR/CS-TR-78-687.html. [571]
19. Patrick J. Hayes, “Computation and Deduction,” Proceedings of the Second
Mathematical Foundations of Computer Science Symposium, Czechoslavak Academy of
Sciences, pp. 105–118, 1973. [571]
20. Michael R. Genesereth, “An Overview of Meta-Level Architecture,” Proceedings of theThird National Conference on Artificial Intelligence, pp. 119–124, Los Altos, CA: Morgan
Kaufmann Publishers, 1983. [571]
21. Stuart Russell and Eric H. Wefald, Do the Right Thing: Studies in Limited Rationality,
Cambridge, MA: MIT Press, 1991. [571]
22. Eric J. Horvitz, “Reasoning about Beliefs and Actions under Computational Resource
Constraints,” Proceedings of the Third Workshop on Uncertainty in Artificial Intelligence,
pp. 429–444, Seattle WA, July 1987. An online version is available at
ftp://ftp.research.microsoft.com/pub/ejh/u87.ps. [572]
23. Artificial Intelligence, Vol. 126, Nos. 1–2 (Special Issue on Computational Tradeoffs
under Bounded Resources), February 2001. [572]
24. Marvin Minsky, The Society of Mind, New York: Simon and Schuster, 1988. [572]25. William Kornfeld and Carl Hewitt, “The Scientific Community Metaphor,” IEEE
Transactions on Systems, Man, and Cybernetics, Vol. SMC-11, No. 1, pp. 24–33, 1981.
Available online as MIT AI Laboratory Memo No. 641, January 1981, at
http://dspace.mit.edu/bitstream/1721.1/5693/2/AIM-641.pdf. [572]
26. Victor R. Lesser and Lee D. Erman, “Distributed Interpretation: A Model andExperiment,” IEEE Transactions on Computers, Vol. C-29, No. 12, pp. 1144–1163,
December 1980. Available online at
ftp://mas.cs.umass.edu/pub/lesser/LesserIEEE1980.pdf. [572]
27. Victor R. Lesser and Daniel Corkill, “The Distributed Vehicle Monitoring Testbed: A
Tool for Investigating Distributed Problem Solving Networks,” AI Magazine, Vol. 4, No. 3,
pp. 15–33, 1983. [573]
28. See the Lab’s Web site at http://mas.cs.umass.edu/ and a history of the Lab’s work at
ftp://mas.cs.umass.edu/pub/LabHistory Web-Article.pdf. [573]
29. Reid G. Smith, “The Contract Net Protocol: High Level Communication and Control ina Distributed Problem Solver,” IEEE Transactions on Computers, Vol. C-29, No. 12, pp.
1104–1113, December 1980. [573]
30. See also Randy Davis and Reid G. Smith, “Negotiation as a Metaphor for Distributed
Problem Solving,” Artificial Intelligence, Vol. 20, No. 1, pp. 63–109, 1983. [573]
31. Les Gasser, Carl Braganza, and Nava Herman, “MACE: A Flexible Testbed forDistributed AI Research,” in Michael N. Huhns (ed.), Distributed Artificial Intelligence, pp.
119–152, London: Pitman Publishers, 1987. Available online at
http://www.isrl.uiuc.edu/%7Egasser/papers/
gasser-braganza-herman-mace-a-flexible-testbed-for-dai-research-1987.pdf. [573]
32. See the CORAL Web site at http://www.cs.cmu.edu/∼coral/main/. [573]586
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31.2

NOTES

33. See http://www.robocup.org/. [573]
34. Mathematically brave readers might consult, for example, Robert C. Moore, “Reasoning
about Knowledge and Action,” Proceedings of IJCAI-77, Vol. 1, pp. 223–227, 1977. An
expanded version is Robert C. Moore, “A Formal Theory of Knowledge and Action,” SRI AI
Center Technical Note 320, February 1984, available online at
http://www.ai.sri.com/pubs/files/632.pdf. Also see Kurt Konolige, “A Deduction Model ofBelief and Its Logics,” SRI AI Center Technical Note 326, August 1984, available online at
http://www.ai.sri.com/pubs/files/626.pdf. [575]35. John L. Austin, How to Do Things with Words, New York: Oxford University Press,
1962; John Searle, Speech Acts, New York: Cambridge University Press. 1969. [575]36. Philip R. Cohen and C. Raymond Perrault, “Elements of a Plan Based Theory of
Speech Acts,” Cognitive Science, Vol. 3, No. 3, pp. 177–212, 1979. Available online at
http://www.cs.huji.ac.il/∼imas/readings/cohen79.pdf. [575]37. KQML is described in Tim Finin et al., “KQML as an Agent Communication
Language,” in Jeff Bradshaw (ed.), Software Agents, Cambridge, MA: MIT Press, 1997,
available online at http://www.cs.umbc.edu/kqml/papers/kqmlacl.pdf. The DARPA
knowledge sharing initiative is described in Robert Neches et al., “Enabling Technology forKnowledge Sharing,” AI Magazine, Vol. 12, No. 3, pp. 16–36, 1991. [575]38. See http://www-ksl.stanford.edu/knowledge-sharing/kif/. [575]39. This example is taken from one of Michael Wooldridge’s lecture slides on multiagent
systems, accessible from http://www.csc.liv.ac.uk/∼mjw/pubs/imas/. [575]40. See the FIPA Web page at http://www.fipa.org/. [576]
41. Rafael H. Bordini, Jomi Fred Hübner, and Michael Wooldridge, Programming
Multi-Agent Systems in AgentSpeak Using Jason, New York: John Wiley and Sons, Inc.,
2007. A Jason Web site is at
http://jason.sourceforge.net/JasonWebSite/Jason%20Home.php. [576]42. Jeffrey S. Rosenschein and Michael R. Genesereth, “Deals among Rational Agents,”
Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pp.
91–99, 1985. Available online at http://dli.iiit.ac.in/ijcai/IJCAI-85-VOL1/PDF/017.pdf.
[576]
43. See, for example, Michael Wooldridge, An Introduction to MultiAgent Systems,
Chichester, England: John Wiley and Sons, Inc., 2002; Yoav Shoham and Kevin
Leyton-Brown, Multiagent Systems: Algorithmic, Game Theoretic, and LogicalFoundations, New York: Cambridge University Press, 2008: and Tim Finin’s “Agents 101,”a Web page to learn about agents at http://agents.umbc.edu/. [576]44. Paul S. Rosenbloom, John E. Laird, and Allen Newell (eds.), The SOAR Papers:
Research on Integrated Intelligence, Chapter 1, Cambridge, MA: MIT Press, 1993. [576]45. Allen Newell and Herbert A. Simon, Human Problem Solving, Englewood Cliffs, NJ:
Prentice-Hall, 1972. [577]
46. Allen Newell, “This Week’s Citation Classic,” Current Contents, No. 34, p. 167, August25, 1980. Available online at
http://www.garfield.library.upenn.edu/classics1980/A1980KD04600001.pdf. [577]47. A recent description can be found in John R. Anderson et al., “An Integrated Theory of
the Mind,” Psychological Review, Vol. 111, No. 4, pp. 1036–1060, 2004; available onlinefrom http://act-r.psy.cmu.edu/publications/pubinfo.php?id=526. See also John R.
Anderson, How Can the Human Mind Occur in the Physical Universe, New York: Oxford
University Press, 2007. [578]48. http://act-r.psy.cmu.edu/. [578]49. The quotation is from http://act-r.psy.cmu.edu/about/. [580]587
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31

NOTES

50. http://act-r.psy.cmu.edu/about/. [580]51. For a list, see http://act-r.psy.cmu.edu/publications/index.php. [580]52. See J. Greg Trafton et al., “Integrating Vision and Audition within a Cognitive
Architecture to Track Conversations,” Proceedings of the 3rd ACM/IEEE International
Conference on Human Robot Interaction, pp. 201–208, 2008. Available online at
http://www.nrl.navy.mil/aic/iss/aas/documents/trafton.hri08.pdf. [580]
53. See John R. Anderson, et al., “A Central Circuit of the Mind,” Trends in Cognitive
Science, Vol. 12, No. 4, pp. 136–143, 2008 (available online fromhttp://act-r.psy.cmu.edu/publications/pubinfo.php?id=800), and John R. Anderson et al.,“An Integrated Theory of the Mind,” Psychological Review, Vol. 111, No. 4, pp. 1036–1060,
2004 (available online from http://act-r.psy.cmu.edu/publications/pubinfo.php?id=526).
[580]
54. Paul S. Rosenbloom, John E. Laird, and Allen Newell (eds.), op. cit. [581]55. John E. Laird, “Universal Subgoaling,” Ph.D. thesis, Computer Science Department,
Carnegie Mellon University, Pittsburgh, PA, 1983; Paul S. Rosenbloom, “The Chunking ofGoal Hierarchles: A Model of Practice and Stimulus–Response Compatibility,” Ph.D. thesis(also Technical Report No. 83-148), Computer Science Department, Carnegie Mellon
University, Pittsburgh, PA, 1983. [581]56. Sixty-eight articles (along with an extensive bibliography) are collected in a two-volume
book: Paul S. Rosenbloom, John E. Laird, and Allen Newell (eds.), op. cit. (Anintroductory chapter of the book is available online at
http://www.isi.edu/soar/papers/soar-papers-book/intromosaic.ps.) The primary paper on
SOAR is John E. Laird, Allen Newell, and Paul S. Rosenbloom, “SOAR: An Architecturefor General Intelligence,” Artificial Intelligence, Vol. 33, pp. 1–64, 1987. [581]57. SOAR9 is described in John E. Laird, “Extending The SOAR Cognitive Architecture,”
Proceedings of the Artificial General Intelligence Conference, Memphis, TN, March 2008,
Amsterdam: IOS Press; available online at
http://ai.eecs.umich.edu/people/laird/papers/Laird-GAIC.pdf. [581]58. Jill Fain Lehman, John Laird, and Paul Rosenbloom, “A Gentle Introduction to SOAR:
An Architecture for Human Cognition: 2006 Update”; available online at
http://ai.eecs.umich.edu/soar/sitemaker/docs/misc/GentleIntroduction-2006.pdf. [584]59. From the SOAR Web site, http://sitemaker.umich.edu/soar/home. [584]60. R. M. Jones et al., “Automated Intelligent Pilots for Combat Flight Simulation,” AI
Magazine, Vol. 20, No. 1, pp. 27–41, 1999. [584]61. Pat Langley, John E. Laird, and Seth Rogers, “Cognitive Architectures: Research Issues
and Challenges,” 2006; available online at
http://cll.stanford.edu/∼langley/papers/final.arch.pdf. [584]588
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31.2

Part VIII

Modern AI: Today andTomorrow

AI technology has matured markedly in the past couple of decades andnow includes an impressive array of powerful computational tools. These canbe deployed with great effectiveness because of the increasing power ofrelatively inexpensive computers, the availability of large databases, and thegrowth of the World Wide Web. Today’s AI programs are capable ofapproximating many human cognitive abilities, automating some of themcompletely, and even bettering what humans can do in others. Because AI isnow capable of contributing to the solution of many real-world problems,many graduates who have specialized in AI studies go to work for companies
and start-ups instead of pursuing academic AI research. Google and Microsoft,
just to name two examples, have hired many of these graduates.
Just as other branches of engineering gradually develop a number ofsubspecialties, so has AI. For example, the July 2009 International Joint
Conference on Artificial Intelligence (IJCAI) had papers in the following“theme” areas: Agent-based and Multi-agent Systems; Constraint,
Satisfiability, and Search; Knowledge Representation, Reasoning, and Logic;
Machine Learning; Multidisciplinary Topics and Applications;
Natural-Language Processing; Planning and Scheduling; Robotics and Vision;
Uncertainty in AI; and Web and Knowledge-based Information Systems.
(Note that many of the theme areas combine two or more broad topics, andthe topics themselves are further articulated in the call for papers.1 ) Of
course, many of these subspecialties draw on each other so the field as a wholestays connected.
589
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31
Even though more accomplished historians than I have wisely avoided
writing accounts that get too close to the present (and predicting the future iseven more hazardous), in this final part of the book I’ll attempt a look at how
the quest for AI is faring and speculate about the future. Because the past
several years have seen an explosion of new AI technology and applications, I’ll
have space for just a limited number of what I take to be representative
examples of what AI programs are doing now and might yet do.

590
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32.1

Chapter 32

Extraordinary
Achievements
Some of AI’s recent achievements stand as extraordinary milestones of its
progress, and others have insinuated themselves almost invisibly into our daily
routines. In between these extremes, AI programs have become important
tools in science and commerce. These three categories provide a useful way oforganizing the state of AI today. First, I’ll look at some of theheadline-making systems appearing just before and just after the beginning of
the twenty-first century, beginning with AI game-playing programs.

32.1

Games

Although getting computers to excel at games, such as chess and checkers, isthought by some to be a somewhat frivolous diversion from more serious work,
computer game-playing has served as a laboratory for exploring new AI
techniques – especially in heuristic search and in learning. In a previous
chapter, for example, I explained how reinforcement learning methods wereused to develop a championship-level backgammon program. From the earliest
days of AI, people worked on programs to play chess and checkers, and now,
mainly by using massive amounts of heuristically guided computation,
computers are able to play these and other games better than humans can.

32.1.1Chess

The big news in 1997 was the defeat of the world chess champion, Garry
Kasparov, by IBM’s “Deep Blue” chess-playing computer. (See Fig. 32.1.)
The first time Kasparov played Deep Blue, in February 1996, Deep Blue won
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Extraordinary Achievements

the first game but lost the match. But on May 11, 1997, a hardware-enhanced
1997 version (unofficially nicknamed “Deeper Blue”) won a six-game match(under regular chess tournament time controls) by two wins to one with threedraws. (For a Computer History Museum movie, Endgame: Challenging the
Chess Masters, visit http://www.youtube.com/watch?v=5hRNlfAUeEE. For
records of the play of the games in the 1997 match, see
http://www.research.ibm.com/deepblue/watch/html/c.shtml.)
The 1997 Deep Blue was a combination of special-purpose hardware andsoftware running on an IBM RS/6000 SP2 supercomputer. Some of its
features included improvements “in response to specific problems observed in
the 1996 Kasparov games. . . ”2

Figure 32.1: Garry Kasparov playing chess against Deep Blue in game two ofa six-game rematch. (Photograph used with permission of AP/Wide World
Photos. c )After his defeat, Kasparov was quoted in the New York Times as saying“I was not in the mood of playing at all.”3 The Times article goes on to say“that after Game 5 on Saturday, he had become so dispirited that he felt thematch was already over. Asked why, he said: ‘I’m a human being. When I see
something that is well beyond my understanding, I’m afraid.’” Several Web
sites mention that, after his loss, Kasparov said that he sometimes saw deep
intelligence and creativity in the machine’s moves. But his statement wasmeant to imply that human chess players must have intervened during thesecond game of the match. Kasparov wanted a rematch, but IBM dismantled
the machine, and there was none.
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Games

Kasparov did have his defenders however. The Times article justmentioned quotes Lev Albert, a former U.S. champion, as saying “This was ashow. If they [IBM] want to prove it was more than a show, let them playanyone but Garry. If it would play against, say, Grandmaster Boris Gulko,
who is not even among the top 50, I am willing to bet $10,000 the computer
would lose.”4
Deep Blue’s history began with the chess program “Deep Thought”
developed by Ph.D. student Feng-Hsiung Hsu at Carnegie Mellon University.
According to IBM’s Web site about Deep Blue
(http://www.research.ibm.com/deepblue/), the “IBM Deep Blue project
began when Hsu and Murray Campbell [Hsu’s classmate at Carnegie Mellon]
joined IBM Research in 1989” and began work on parallel processing systems.
The version of Deep Blue that won the match against Kasparov was acomputer containing 256 “chess processors,” which, in combination, could
examine about 200 million chess positions per second. (For comparison, notethat back in the 1970s, Northwestern University’s CHESS 3.0 could evaluate
only about 100 positions per second.)
Deep Blue evaluated a chess position using both a “fast” evaluation
function and a “slow” one. The values of some 8,000 features used in thesefunctions were computed by special hardware. According to a Deep Blue
paper,5This [use of both fast and slow functions] is a standard techniqueto skip computing an expensive full evaluation when an
approximation is good enough. The fast evaluation, which
computes a score for a chess position in a single clock cycle,
contains all the easily computed major evaluation terms with highvalues. The most significant part of the fast evaluation is the“piece placement” value, i.e., the sum of the basic piece values withsquare-based location adjustments. Positional features that can becomputed quickly, such as “pawn can run,” are also part of the fastevaluation. The slow evaluation scans the chess board one column
at a time, computing values for chess concepts such as squarecontrol, pins, x-rays, king safety, pawn structure, passed pawns, raycontrol, outposts, pawn majority, rook on the 7th, blockade,restraint, color complex, trapped pieces, development, and so on.
The features recognized in both the slow and fast evaluation
functions have programmable weights, allowing their relative
importance to be easily adjusted.
Heuristic search (guided by Deep Blue’s evaluation functions) permitted
search to a depth of between six and sixteen ply to a maximum of forty ply insome situations. In addition to search, Deep Blue could draw on standard“book moves” containing over 4,000 positions. Its play could also be influenced
by a database of 700,000 grandmaster games. It also used endgame databases
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Extraordinary Achievementsthat included “all chess positions with five or fewer pieces on the board, as well
as selected positions with six pieces that included a pair of blocked pawns.”
Because Deep Blue won by using what some computer science people call“brute-force” methods, can it be said that its victory was an “AI
achievement?” Here is IBM’s opinion of the matter:6
Does Deep Blue use artificial intelligence? The short answer is“no.” Earlier computer designs that tried to mimic human thinking
weren’t very good at it. No formula exists for intuition. . . . DeepBlue relies more on computational power and a simpler search andevaluation function.
The long answer is [also] “no.” “Artificial Intelligence” is more
successful in science fiction than it is here on earth, and you don’t
have to be Isaac Asimov to know why it’s hard to design a machine
to mimic a process we don’t understand very well to begin with.
How we think is a question without an answer. Deep Blue could
never be a HAL-9000 if it tried. Nor would it occur to Deep Blueto “try.”
Among the differences that IBM lists between how Kasparov and DeepBlue each approached the problem of playing chess are the following:7• Deep Blue can examine and evaluate up to 200,000,000 chess positions
per second; Garry Kasparov can examine and evaluate up to three chesspositions per second.
• Deep Blue has a small amount of chess knowledge and an enormous
amount of calculation ability; Garry Kasparov has a large amount ofchess knowledge and a somewhat smaller amount of calculation ability.
• Garry Kasparov uses his tremendous sense of feeling and intuition toplay world champion–calibre chess; Deep Blue is a machine that isincapable of feeling or intuition.
• Garry Kasparov is able to learn and adapt very quickly from his own
successes and mistakes; Deep Blue, as it stands today, is not a “learning
system.” It is therefore not capable of utilizing artificial intelligence toeither learn from its opponent or “think” about the current position of
the chessboard.
• Any changes in the way Deep Blue plays chess must be performed by themembers of the development team between games; Garry Kasparov canalter the way he plays at any time before, during, and/or after each
game.

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Games

But I have a broader view of AI. Although Deep Blue relied mainly onbrute-force methods rather than on rule-based reasoning (for example), it did
use heuristic search, one of AI’s foundational techniques. The differences
between Kasparov and Deep Blue simply indicate how much better chessprograms would fare if they employed human-chess-playing knowledge andskills (once these become known well enough to program) and machinelearning methods in addition to brute force.
John McCarthy has expressed similar views. In a recent article,8 he wrote
However, it is a measure of our limited understanding of theprinciples of artificial intelligence (AI) that this [championship]level of play requires many millions of times as much computing as
a human chess player does.
. . . Champion-level play is possible with enormously lesscomputation than Deep Blue and its recent competitors use.
McCarthy goes on to recommend that computer chess tournaments “should
admit programs only with severe limits on computation. This would
concentrate attention on scientific advances.”
Matches between computers and humans, as well as between computers,
continue to be played. In a match staged between November 25 and December
5, 2006, in Bonn, Germany, World Champion Vladimir Kramnik played amatch with Deep Fritz, a chess program developed by Frans Morsch andMathias Feist in Germany. Of the six games in the match, Deep Fritz won twogames, and four ended in draws. Kramnik is quoted as saying “Deep Fritz 8[an inexpensive version] is stronger than Deep Blue.” The latest version is
Deep Fritz 11. (Deep Fritz 8 can be purchased fromhttp://www.chesscentral.com/software/deep-fritz-8.htm.)
Several Web sites are devoted to computer chess programs and theirmatches.

32.1.2Checkers

In September 2007, Professor Jonathan Schaeffer (1957– ; Fig. 32.2) and his
team at the University of Alberta in Edmonton, Canada, published an article
with the title “Checkers is Solved” – announcing that “Perfect play by bothsides leads to a draw.”9 Schaeffer and colleagues have been working to solve
checkers since 1989. They claim that computations to do so have been runningalmost continuously since then. The finally completed proof that checkers
leads to a draw “consists of an explicit strategy that never loses – the program
can achieve at least a draw against any opponent, playing either the black orwhite pieces.” The checkers team credits their result to “advanced AI
algorithms and improved hardware (faster processors, larger memories, and595
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Extraordinary Achievements

larger disks). . . ” It’s not surprising that the effort required eighteen years ofskilled effort and massive amounts of computation: There are500,995,484,682,338,672,639 different positions in the game of checkers!

Figure 32.2: Jonathan Schaeffer. (Photograph courtesy of Jonathan Schaeffer.)
Along the way to the proof, the team developed a constantly improving,excellent checkers program named “CHINOOK.” (Chinook is the name of awarm winter wind from the west coming downslope from the Canadian Rocky
Mountains onto the Great Plains.) In 1992’s first man–machine world chesschampionship, checkers champion Marion Tinsley beat CHINOOK four wins totwo, with thirty-three draws.10
A rematch with a much-improved CHINOOK was held at the Computer
Museum in Boston in 1994. According to the CHINOOK team,11 “CHINOOK
1994 searched better and deeper [than CHINOOK 1992], evaluated positions
better, had access to more and better quality endgame databases, and hadaccess to 12 times as much (and better quality) opening knowledge.” The firstsix games of the rematch were drawn. Before game seven could be played,
Tinsley resigned the match, citing health reasons. According to the rules,
CHINOOK was declared the Man–Machine World Champion. (Soon after,
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32.1

Games

Tinsley was diagnosed with pancreatic cancer.) The match organizers
arranged for play to continue in Boston by having Grand Master Don Lafferty
play a challenge match against CHINOOK. CHINOOK retained its title because
the match ended with one game for each and eighteen draws. Tinsley’s healthimproved sufficiently for him to ask for a rematch to reclaim his title, but hedied in 1995 before it could take place.
The proof that one can always be guaranteed at least a draw in checkers
involves a prodigious amount of computation and very large databases. The
proof team wrote that “checkers represents the most computationallychallenging game solved to date.” I’ll give a general idea of the structure of theproof. Figure 32.3 is a schematic diagram showing the relationship between
the number of pieces still on the board and the number of ways these pieces
can be configured in checkers positions. (The vertical axis is the number ofpieces; the horizontal axis is the logarithm of the number of positions.) The
shaded area at the bottom of the diagram represents all of the ways that ten
or fewer pieces can be configured. There are 39,271,258,813,439 such positions,and Schaeffer and his team have calculated and stored in the endgame
database whether these positions result in a win, a loss, or a draw for eachplayer. The small open circles in the diagram represent positions (with morethan ten pieces) for which a value (win, loss, or draw) has been established.
Optimum play involves using heuristic search to find a line of play guaranteed
to get from the starting position to a position in the shaded area from which
at least a draw can be guaranteed. An example path is shown by the solid line
marked “seeded” in the diagram. (Other features of this diagram are
explained in the paper announcing the proof.) You can inspect how the proof
evaluates various positions by visiting the CHINOOK Web pages at
http://www.cs.ualberta.ca/∼chinook/. You can also play against CHINOOK
from these Web pages.
I believe the checkers result is a superb AI achievement. As the authors
put it in the conclusion of their paper,
The checkers computation pushes the boundary of what can beachieved by search-intensive algorithms. It provides compelling
evidence of the power of limited-knowledge approaches to artificial
intelligence. Deep search implicitly uncovers knowledge.
Furthermore, search algorithms are well poised to take advantageof the increase in on-chip parallelism that multicore computing willsoon offer. Search-intensive approaches to AI will play anincreasingly important role in the evolution of the field.
So, it appears that intensive search methods have resurfaced to challenge
the “in-the-knowledge-is-the-power” doctrine, which I mentioned on page 258.
It should be noted though how much personal effort also was required for thisachievement. Jonathan Schaeffer’s wife, Stephanie, has been quoted as saying“Its been 18 years! . . . obsessive-compulsive behavior. . . not normal. . . Get a597
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Extraordinary Achievements

Figure 32.3: Schematic for the checkers proof. (Illustration used with permissionof Jonathan Schaeffer.)life, Jonathan.”12
But what about chess with its much, much larger search space – at least1040 positions. Checkers has only 500 ×1018 positions, about the square rootof that of chess. The authors think that solving chess anytime soon is unlikely.
They wrote, “Given the effort required to solve checkers, chess will remain
unsolved for a long time, barring the invention of new technology.”13

32.1.3

Other Games

There are several other games that computers are now very good at. One
example that has attracted a lot of attention is the game of poker. The
University of Alberta maintains a Web page at
http://www.cs.ualberta.ca/∼pokert/index.html devoted to the AAAI’s
Computer Poker Competition. For example, the 2008 competition had nine
entrants for both “Heads-Up Texas Hold’em Limit” and “Heads-Up Texas
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32.2

Games

Hold’em No Limit” and six entrants for “6-Player Texas Hold’em Limit.”14
You can play against some of the Unversity of Alberta’s poker-playing
programs at http://poker.cs.ualberta.ca/.
Matt Ginsberg (1955– ) developed a program called GIB, an acronym for
Goren in a Box (or Ginsberg’s Intelligent Bridgeplayer), for playing bridge.15
At any stage of play, GIB knows the cards in its own hand, in the dummy’s
hand, and the cards played so far. It then assigns the remaining cards
randomly to the opponents and calculates the best card to play based on that
random assignment. It goes through this process thousands of times before
actually playing a card. Based on the statistics gathered by this “Monte
Carlo” approach, it selects what it considers to be the best card to play.
In a New York Times article of March 14, 2008, the bridge columnist
Phillip Alder analyzed a game in which two humans (each with a GIB partner)
played against each other. In summarizing GIB’s play, Alder said “These
robots, as such programs are sometimes called, are quirky. Occasionally theybid and play well, but often they make strange decisions.”16 You can play
bridge (with or against GIB) at http://www.bridgebase.com/, and there is a
GIB Web site, from which you can purchase a copy of GIB, at
http://www.gibware.com/. Ginsberg has ceased work on GIB, and there areprobably stronger programs around. Commenting on bridge, Jonathan
Schaeffer wrote me that “The bottleneck in achieving strong play continues tobe bidding. Bidding conventions are human-designed conventions, and it isdifficult to capture all the rules that human players have developed to playusing this ‘bidding language.’”17
Probably one of the hardest games for computers is the game of Go. In
2008, a program named MoGo Titan,18 developed by INRIA France andMaastricht University in the Netherlands, beat a professional Go player in agame in which the computer, the Dutch supercomputer Huygens, was given a
handicap of nine stones. Computer Go programs continue to improve. The
best of them now use Monte Carlo methods to make move selections. The
Web page at http://www.computer-go.info/h-c/index.html lists results ofhuman–computer Go games.
Finally of course, there is Scrabble R , a game especially suited forcomputers with their abilities to access large dictionaries and conduct massive
searches. Scrabble programs now routinely beat expert humans.19
Much more could be written about computer game-playing. Many
programs are available for purchase or download. You can select your favorite
games and match your own abilities against what AI has achieved! The
International Computer Games Association (ICGA) maintains a Web site thatprovides information about all kinds of computer game-playing tournaments.20
But let us turn now from what some consider to be a frivolous (albeit
challenging) pursuit to talk about the possibly more exciting subject of robots.

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32.2

Robot Systems

Robots are everywhere! They have gone to Mars, to the ocean depths, andeven into volcanoes. There are agricultural robots, factory robots, surgicalrobots, and warehouse robots. The business of building robots is thriving,
especially in Pittsburgh, Pennsylvania, the home of Carnegie Mellon
University and its Robotics Institute. According to a July 16, 2008, article in
The Christian Science Monitor, “Today there are more than 30 robotics
companies in Pittsburgh.”21 Some of today’s “robots” are actually justremote-controlled mechanical devices operated by humans, but more and moreof them have become autonomous and capable of acting intelligently on theirown. I’ll describe some examples.

32.2.1

Remote Agent in Deep Space 1

On October 24, 1998, NASA launched Deep Space 1 (DS1), a spacecraft whose
mission was to evaluate the space-worthiness of twelve advanced technologies.
One of these technologies was “Remote Agent” (RA), a robotic system forplanning and executing spacecraft actions.22 An artist’s rendering of DS1
approaching Comet 19P/Borrelly is shown in Fig. 32.4.
Although not quite up to all of the capabilities of HAL 9000 (theintelligent system controlling the spacecraft in the novel and film 2001: A
Space Odyssey), the Remote Agent was a major AI achievement. It was so
named because it served as an intelligent agent on DS1, intermediate between
the operators back on earth and the sensors and effectors on board. One of theNASA reports about the project states that23. . . one of the most unique characteristics of RA, and a maindifference with traditional spacecraft commanding, is that ground
operators can communicate with RA using goals (e.g., “During thenext week take pictures of the following asteroids and thrust 90% of
the time”) rather than with detailed sequences of timed commands.
RA determines a plan of action that achieves those goals andcarries out that plan by issuing commands to the spacecraft.
RA was developed by engineers at the Jet Propulsion Laboratory inPasadena and at the Ames Research Center in Mountain View, California. It
consists of three major tightly integrated subsystems. A Planner and
Scheduler uses heuristic search to produce plans for accomplishing mission
goals; an Executive system carries out the activities specified by these plans;
and a Mode Identification and Recovery System monitors the status of thespacecraft and attempts to discover, diagnose, and correct faults. The systemarchitecture is shown in Fig. 32.5.

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32.2

Robot Systems

Figure 32.4: Artist’s rendering of DS1 approaching a comet.

Figure 32.5: Remote agent architecture. (Used with permission of P. Pandurang
Nayak.)
RA’s Smart Executive (EXEC) issues commands to the Real-Time
Execution system, which controls the spacecraft and its components. These

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Extraordinary Achievementscommands involve turning on and off the ion propulsion system, orienting thespacecraft, positioning the camera, and so on. RA’s Mode ID system receives
information about the status of the spacecraft through a set of sensory
Monitors. Based on mission goals and anticipated spacecraft state provided by
EXEC, the Mission Manager (MM) formulates a planning problem for the
Planner/Scheduler (PS). PS, in turn, constructs a plan consisting of a timeschedule of actions for execution by the EXEC. Planning Experts participate in
the planning process for dealing with specialized tasks, such as navigation.
EXEC decomposes the schedule of high-level activities composed by the PS into
commands to motors, valves, and other effectors and monitors the execution ofthese commands. If some task cannot be achieved, EXEC may either attempt
an alternative command or may ask the Mode ID and Recovery system for ananalysis and repair of the problem. If neither of these courses of action workout, EXEC aborts its current plan, puts the spacecraft into a “safe state,” andrequests a new plan from MM. All the while, the Mode Identification systemobserves EXEC issuing commands, receives data about events taking place fromthe sensory Monitors, and uses its models of the spacecraft’s components todeduce their states, which are communicated to EXEC.
RA was subjected to thorough testing and evaluation before NASA
allowed it to fly on DS1. The space-tested version was called RAX (the added“X” signifying experiments). RAX was given control of the spacecraft forperiods between May 17 and May 21, 1999. (It first ran from May 17, 1999, 5
am PST to May 19, 1999, 7 pm PST. It ran again from May 21, 1999, 7:15 am
PST to 1:30 pm PST.) In these experiments, a “RAX Manager” (part of theDS1 flight software) supervised RAX’s control of the spacecraft. As a final
report about RAX notes “The ability to tightly control RAX activity throughthe RAX manager was an important factor in convincing the DS1 project thatground controllers could easily recover control of the spacecraft from RAX.”24
While RAX was in control, it could issue commands to the Ion Propulsion
System (IPS), which was also being tested during the DS1 mission, to a
Miniature Integrated Camera and Spectrometer, to an Autonomous
Navigation system, to the spacecraft Attitude Control System, and to a seriesof power switches. As described in the final report, “The main scenario goals
were to execute an IPS thrust arc, acquire optical navigation images asrequested by the autonomous navigator, and respond to several simulated
faults. The faults included minor ones that could be responded to without
disrupting the current plan and more serious ones that required generating a
new plan to achieve the remaining goals.” The report concludes, “In spite of acouple of bugs that occurred during the flight experiment, RA successfully
demonstrated 100% of its flight validation objectives.”25

Figure 32.6 is a diagram of telemetry data showing what RAX was up toduring several hours of its final test run. It was captured from an interactive
Web applet, which you can access and run fromhttp://ti.arc.nasa.gov/project/remote-agent/. (The Web site also has details
about RA and RAX, with pointers to papers, press releases, and other material.)
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Robot Systems

As an interesting aside, the RA software was programmed in LISP Works, a
LISP product originally marketed by The Harlequin Group, Ltd. As far as I
know, RAX was the first instance of LISP programs running in space. MIT
professor Brian C. Williams, one of the RA developers while he worked at
NASA, continues related work on planning, execution, and model-based
reasoning in his “Model-based Embedded and Robotic Systems” group at
MIT.26 He wrote me that “the remote agent architecture has been extensively
emulated and deployed on a wide range of robotic systems, including earth
orbiters, air vehicles, boats, submersibles, and ground vehicles.”27

Figure 32.6: Illustration of RAX activities. (Illustration courtesy of Mark Shirleyat NASA.)

32.2.2

Driverless Automobiles

Perhaps driving an automobile under a wide range of conditions is even morechallenging than controlling a spacecraft. The pace of dealing with the many
controls on a spacecraft is leisurely compared with the rapid planning andreaction required to negotiate traffic skillfully. Thus, we might expect that theperception and action computations needed for driving to be quite different
from those of the Remote Agent.
Humans drive cars, more or less well, on sunny and stormy days, at night,
on city streets, on high-speed motorways, and on and off desert roads. Yes,
there are crashes. In the United States there were 28,933 people who died and603
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Extraordinary Achievements

2,221,000 people who were injured in passenger-car accidents in 2007.28 But
that represents only slightly over one person killed per 100 million vehicle
miles traveled.
There is a long history of attempting to get AI systems to drive vehicles
as well as humans do. DARPA began work on this problem in the mid-1980s
with its ALV project of the Strategic Computing Program (see Chapter 23).
Carnegie Mellon University had continuing programs on driverless vehicles,
notably the ALVINN, RALPH, and Navlab systems (see Section 29.4.3). The
most impressive early work was probably that of Ernst Dickmanns and his
team at the Universität der Bundeswehr in Munich. Supported in part by theEuropean EUREKA Prometheus driverless-automobile project, Dickmanns’s
VaMP vehicle (a Mercedes-Benz 500 SEL) drove from Munich to Odense,
Denmark, and back in 1995 (95% of the way autonomously), using computer
vision and radar.29 Other driverless-vehicle projects have been undertaken in
Japan (Tsukuba Mechanical Engineering Lab), in the Netherlands (2getthere),and in Italy (the ARGO Project).
DARPA returned to tackle the driverless car problem by announcing a“Grand Challenge” on July 30, 2002. The challenge (a competition for
U.S.-based teams only) was to demonstrate a vehicle that could driveautonomously on and off roads in the desert from Barstow, California, toPrimm, Nevada, on March 13, 2004. The team that most quickly completed
the 142-mile course in less than the ten-hour time limit would receive a cash
prize of $1,000,000. As the DARPA commemorative Web site for the challenge
notes, “Competitors’ entries must be unmanned, autonomous ground vehicles,
and cannot be remotely driven. Boundaries define the course, and vehicles
that go outside of them will be disqualified. Each vehicle will be trailed on thecourse by a manned control vehicle equipped with an emergency stop system
to prevent collisions and other unsafe situations.”30
The motivation for the challenge was a mandate by the FY2001 Defense
Authorization Act (H.R. 4205/P.L. 106-398 of October 30, 2001), which stated“It shall be a goal of the Armed Forces to achieve the fielding of unmanned,
remotely controlled technology such that – (1) by 2010, one-third of theaircraft in the operational deep strike force aircraft fleet are unmanned; and (2)by 2015, one-third of the operational ground combat vehicles are unmanned.”
Out of 106 teams that originally applied to compete in the challenge,
fifteen driverless vehicles left the starting line in Barstow, California. There
were teams from large and small companies, from universities, and from
specially assembled groups of individual innovators.31 Each team was given a
CD with the coordinates of some 2000 “waypoints” to help them chart their
way along the course using their GPS systems. Navigating around obstacles,
staying on roads, and avoiding drop-offs was up to the sensory and planningmechanisms on board.
None of the vehicles completed the course.32 Eight failed before traveling604
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32.2

Robot Systems

a mile and others crashed soon after starting. (For a short film, see
http://www.youtube.com/watch?v=wTDG5gjwPGo.) The farthest any
vehicle traveled was just under seven and a half miles. According to DARPA’s
final data, as reported by CNN, “Vehicle 22 Red Team (Carnegie Mellon): At
mile 7.4, on switchbacks in a mountainous section, vehicle [which was named“Sandstorm”] went off course, got caught on a berm and rubber on the front
wheels caught fire, which was quickly extinguished. Vehicle wascommand-disabled.”33 Commenting on the results, Tom Strat, the deputy
program manager of the Grand Challenge, said34
Some of the vehicles were able to follow the GPS waypoints very
accurately; but were not able to sense obstacles ahead, and we hadsome collisions at the qualification rounds. . . Other vehicles werevery good at sensing obstacles, but had difficulty following
waypoints or were scared of their own shadow, hallucinating
obstacles when they weren’t there.
Although the 2004 Grand Challenge hardly displayed any “extraordinary
achievements,” it was, perhaps, a necessary precursor to sequels that did.
Three months after the 2004 event, DARPA announced “a second Grand
Challenge for Autonomous Robotic Ground Vehicles” to be held on October 8,
2005, with a prize this time of $2 million.35 Twenty-three finalists competed in
the 2005 event, and all but one of them went farther than Sandstorm did in
the 2004 event. Five of the vehicles completed the course – a 132-mile desert
route starting and ending in Primm, Nevada:
1. Stanley (a modified 2004 Volkswagen Touareg R5 TDI; Fig. 32.7)
entered by “The Stanford Racing Team” from Stanford University.
Stanley came in first in six hours and fifty-four minutes.
2. Sandstorm (a modified 1986 AM General M998 HMMWV; Fig. 32.8)
entered by “The Red Team” from Carnegie Mellon University.
Sandstorm (which was upgraded from the 2004 version) came in secondin seven hours and five minutes.
3. H1ghlander (a modified 1999 AM General H1 Hummer Sport Utility
Truck) entered by “The Red Team Too” also from Carnegie Mellon
University. H1ghlander came in third in seven hours and fourteen
minutes. (As a “cultural note,” a Web page for the film Déjà Vu states“The Humvee time machine is a real-life robot H1ghlander built byCarnegie Mellon’s Red Team for the 2005 DARPA Grand Challenge.”36 )4. Kat-5 (a modified 2005 Ford Escape Hybrid) entered by “Team Gray”
from the Gray Insurance Company of Metairie, Louisiana (a suburb of
New Orleans). Kat-5 came in fourth in seven hours and thirty minutes.
(Development of Kat-5 was hampered by two hurricanes, notably
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32

Extraordinary Achievements
Katrina – which hit New Orleans in late August of 2005. Paul
Trepagnier, one of the team members, wrote me that “Most of us had
lost our homes right before the Grand Challenge. [The name, Kat-5,] was
not made in honor of Katrina, as some people have incorrectly stated,but rather it was more of an act of defiance. You gave us your best, andwe are still standing. You can knock us down, but we’ll persevere andcome back stronger than before. It was also a pun on Cat-5 cabling.”37 )5. TerraMax (a modified tactical cargo hauler) entered by “TeamTerraMax” from the Oshkosh Truck Corporation. TerraMax came in
fifth (after being paused overnight) in a total running time of twelve
hours and fifty-one minutes, which was over the ten-hour limit.

Figure 32.7: Stanley on Beer Bottle Pass followed by a DARPA chase vehicle.
(Photograph courtesy of DARPA.)
In some respects the 2005 course was easier than the one in 2004. The
roads were somewhat wider and had fewer curves. The most harrowing
stretch, with steep drop-offs, was over Beer Bottle Pass. A false move there
would send a vehicle tumbling down the mountainside. Figure 32.7 shows
Stanley navigating a stretch of that section. H1ghlander had the best time fora good part of the race but a problem developed just past the 100-mile mark,and Stanley passed it to gain the lead, the victory, and the $2 million. (Videosof the CMU and Stanford entrants can be found from sites at
http://www.redteamracing.org/ andhttp://robots.stanford.edu/talks/stanley/RaceDay.wmv. A PBS NOVA
606
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Robot Systems

Figure 32.8: Sandstorm on Beer Bottle Pass. (Photograph courtesy of DARPA.)
program about the race can be found at
http://www.pbs.org/wgbh/nova/darpa/.)
The entrants in the 2005 Grand Challenge used ranging and opticalsensors that, together with computer vision technology, enabled them to avoid
obstacles and to distinguish drivable from undrivable terrain. They could also
construct plans to control speed and driving direction even though theperceptual information on which these plans were based was uncertain. Space
does not permit a description of all of the vehicles, but I’ll describe some of
the technology used by Stanley, the winner.38
Stanley was outfitted with a six-processor computing platform provided
by Intel, a suite of sensors, and a drive-by-wire control system connected tothe computers. The sensors included five laser range-finding units, a video
camera, a GPS system, and gyroscopes and accelerometers.39 Stanford
professor Sebastian Thrun (1967– ; Fig. 32.9) was the overall supervisor of the
project, and Stanford senior research engineer Michael Montemerlo (1975– ;Fig. 32.9) headed up the software design group. They decided early on that
autonomous navigation was primarily a software problem, and designing thesoftware and its architecture was critical to success. Methods were developed,
and existing methods extended, to deal with the problems of long-range
terrain perception, real-time collision avoidance, and stable vehicle control onslippery and rugged terrain. In particular, three important softwareinnovations played a major role in Stanley’s performance.
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Extraordinary Achievements

Figure 32.9: Sebastian Thrun (left) and Michael Montemerlo (right). (Photograph courtesy of John Markoff.)
One of these innovations was the development of a probabilistic terrain
analysis (PTA) algorithm. It used probabilistic techniques to integrate range
measurements over time from a single-scan laser. It then employed efficient
statistical tests to distinguish drivable from nondrivable terrain. During theGrand Challenge, the PTA algorithm was able to accommodate severe errors
in sensing and to identify obstacles with nearly 100% accuracy at speeds of up
to 35 mph.40
Another innovation was in computer vision. First, a nearby patch of
surface identified to be drivable by the PTA algorithm was located in thevisual field. This patch was then used as a sample of what drivable surfaces
look like and used as training data for the computer vision algorithm. As apaper describing the technique states, “The vision algorithm then classifies theentire field of view of the camera [extending beyond that of the laser scanner],and extracts a drivability map with a range of up to 70 m. The combined
sensors [laser plus computer vision] are integrated into a driving strategy,
which allows for very robust, long range sensing.”41 It is worth noting that
Stanley’s vision system was not the kind (as advocated by David Marr, forexample) that thoroughly analyzed a scene to determine the kinds andlocations of objects in it. Instead it used special-purpose routines whose only

608
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32.2

Robot Systems

job was to help Stanley decide where it was safe to drive.
Finally, a supervised learning system was developed for online speed
control. Speed control was critical in Stanley’s achievement of the fastest
finishing time. The vehicle needed to drive slowly on risky terrain but thenspeed up on safe terrain. Using supervised learning matched to human driving
over both risky and safe terrain, the system was able to learn to choose driving
speeds that traded off risk and speed.42
In January 2006 Stanley was judged to be the “Best Robot of All Time”
by Wired.43 (Shakey was judged to be the fifth best robot, behind the Martianrobots Spirit and Opportunity and two fictional ones.) Stanley currently
resides at the Smithsonian National Museum of American History.
DARPA sponsored another autonomous driving event on November 3,
2007. Called an “Urban Challenge,” it was held near Victorville, California, atwhat was once the site of George Air Force Base. Entrants had to be capableof visiting a set of “check points” within six hours along a sixty-mile course
through a mock city environment. Successful completion required theperformance of complex maneuvers, such as merging, passing, parking, andnegotiating intersections, in the presence of other traffic and obeying all
California driving regulations. In all, over fifty vehicles, both manned andautonomous, were navigating the city streets simultaneously during the final
event. (Pictures and videos of the event are available at
http://www.darpa.mil/grandchallenge/gallery.asp.)44
Through a series of qualifying procedures and actual vehicle testing,
DARPA narrowed the field down first from eighty-nine original applicants tothirty-five teams from twenty-two states and finally to eleven teams. Six ofthese successfully finished the course on race day. DARPA announced thefollowing finishers and completion times:
1. Boss (a 2007 Chevy Tahoe) entered by Tartan Racing from Carnegie
Mellon University. (See Fig. 32.10.) Boss took first place, and a prize of$2 million, with a time of 4:10:20 (for an average speed of approximately
14 mph).
2. Junior (a 2006 Volkswagen Passat Wagon) entered by the Stanford
Racing Team from Stanford University. Junior took second place, and a
prize of $1 million, with a time of 4:29:28.
3. Odin (a 2005 Ford Hybrid Escape) entered by Team Victor Tango from
Virginia Tech. Odin took third place, and a prize of $500,000, with atime of 4:36:38.
4. Talos (a Land Rover LR3) entered by Team MIT from the Massachusetts
Institute of Technology. Talos took fourth place with a time of
approximately six hours.

609
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32

Extraordinary Achievements(Note that the times mentioned here were adjusted to account for periods
during the event when the vehicles were paused due to no fault of their own.)
Two other teams also finished the course, one from a combination of the
University of Pennsylvania and Lehigh University and one from Cornell
University.
The Urban Challenge made new and strong demands on perception andplanning technology. Consider, for example, some of the things the vehicles
had to do: follow rules of the road, detect and track other vehicles at longranges, find a spot and park in a parking lot, obey intersection precedence
rules, follow vehicles at a safe distance, and react to dynamic conditions suchas blocked roads or broken-down vehicles. All of the finishers accomplishedthese feats, and interested readers can refer to papers and Web sites of theentrants to learn details about how they did so. Here is a summary description
from Boss’s Web site:45
Boss uses perception, planning and behavioral software to reason
about traffic and take appropriate actions while proceeding safely
to a destination.
Boss is equipped with more than a dozen lasers, cameras andradars to view the world. High-level route planning determines thebest path through a road network. Motion planning requiresconsideration of the static and dynamic obstacles detected byperception, as well as lane and road boundary information, parking
lot boundaries, stop lines, speed limits, and similar requirements.
Boss handles surprises such as other vehicles running a stop sign or
making sudden stops or turns. Defensive driving skills allow Bossto avoid crashes.
Based on the success of vehicles in the various DARPA Challenges, theautomobile industry is expanding its interest in driverless vehicles. (At least it
was before the 2008–2009 financial problems.) In November 2007, Volkswagenof America announced that it was donating $2 million to Stanford University
to construct a building to house the Volkswagen Automotive Innovation Lab(VAIL) and $750,000 a year for five years for research on automotive
technology. Dr. Burkhard Huhnke, executive director of the Electronics
Research Laboratory, Volkswagen of America, said “The VAIL will be a solid
foundation on which Volkswagen researchers and Stanford scientists will beable to find new ways to explore automotive technology. The work done at
VAIL will help to further develop the future of mobility and autonomous
driving that we started with our partnership on the DARPA Grand Challenge
vehicles, Stanley and Junior.”46
In June 2008, General Motors announced a program to supply Carnegie
Mellon University $1 million annually for five years (and possibly beyond) for
research on autonomous driving technology. Alan Taub, GM’s executive
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Robot Systems

Figure 32.10: Tartan Racing team leader William (Red) Whittaker and Bosspose with first place trophy. (Photograph courtesy of Carnegie Mellon University.)
director of research and development, told a news conference, “We at General
Motors believe that autonomous driving is feasible to begin to enter themarketplace in the next decade and clearly by 2020.”47 You might wonder
what effect GM’s recent bankruptcy has had on this pledge. I am told by
William H. Swisher, Director of Corporate Relations at CMU, that $1 million
has in fact been granted but that “it is unknown if they will complete the 5year agreement.”48
Sebastian Thrun tells me that he expects his research vehicles “will be
driving [autonomously] from San Francisco to Los Angeles routinely. . . [by]around June 2010.”49 He also predicts that “By 2030, half of our highway
miles will be driven autonomously without human input.”50 He doubts,
however, that driverless cars will appear in showrooms anytime soon. He cites
various societal and legal problems such as updating vehicle codes, determining
accident liability, and the human desire to be in control. Instead, he thinksthat for the next few years the main benefits stemming from driverlessautomobile research will be automated aids to human drivers. These will allow
more efficient use of highways and will markedly reduce traffic injuries andfatalities. Examples of some of these aids are all-around collision warning
systems, radar-based cruise control, lane-change warning devices, electronicstability control, satellite global positioning systems, and digital maps.

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32

NOTES

Just as AI technology will soon be helping us drive automobiles, bits andpieces of AI are already in everyday use all around us, which leads me to mynext topic.

Notes
1. See http://ijcai-09.org/fcfp.html. [589]2. Murray Campbell et al., “Deep Blue,” available online at
http://sjeng.org/ftp/deepblue.pdf. [592]3. Bruce Webber, “Swift and Slashing, Computer Topples Kasparov,” New York Times,
May 12, 1997. [592]4. http://www.nytimes.com/library/cyber/week/051297weber.html. [593]
5. Murray Campbell et al., “Deep Blue,” available online at
http://sjeng.org/ftp/deepblue.pdf. [593]6. http://www.research.ibm.com/deepblue/meet/html/d.3.3a.shtml#ai. [594]7. http://www.research.ibm.com/deepblue/meet/html/d.2.shtml. [594]8. John McCarthy, “AI as Sport,” book review, Science, Vol. 276, No. 5318, pp.
1518–1519, June 6, 1997. [595]9. Jonathan Schaeffer et al., “Checkers is Solved,” Science, pp. 1518–1522, Vol. 317,
September 14, 2007. [595]10. Jonathan Schaeffer. “Man versus Machine: The Silicon Graphics World Checkers
Championship,” TR 92-19, Department of Computing Science, University of Alberta, 1992.
Available online at http://www.cs.ualberta.ca/∼jonathan/Papers/Papers/TR92-19.ps. Also
see Jonathan Schaeffer et al., “Man versus Machine for the World Checkers Championship,”
AI Magazine, Vol. 14, No. 2, pp. 28–35, 1993. [596]11. Jonathan Schaeffer et al., “CHINOOK: The Man–Machine World Checkers Champion,”
AI Magazine, Vol. 17, No. 1, pp. 21–29, 1996. [596]12. From one of Jonathan Schaeffer’s slides about CHINOOK at
http://www.cs.ualberta.ca/∼chinook/news/ChinookTalk.pdf. [598]13. For more on Schaeffer and checkers, see Jonathan Schaeffer, One Jump Ahead:Computer Perfection at Checkers, second edition, New York: Springer-Verlag, 2008. [598]14. See http://www.cs.ualberta.ca/∼pokert/2008/results/ for the results. [599]
15. Matthew L. Ginsberg, “GIB: Imperfect Information in a Computationally Challenging
Game,” Journal of Artificial Intelligence Research, Vol. 14, pp. 303–358, 2001. Available
online at http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume14/ginsberg01a.pdf. [599]
16. Phillip Alder, “BRIDGE; A Case Study of the Mysteries of the Robot Thought
Process,” New York Times, April 14, 2008. [599]
17. E-mail of February 14, 2009. [599]18. See http://www.nwo.nl/nwohome.nsf/pages/NWOA 7HHBNS. [599]19. See Brian Sheppard, “World-Championship-Caliber Scrabble,” Artificial Intelligence,Vol. 134, Nos. 1–2, pp. 241–275, January 2002. [599]
20. http://www.icga.org/. [599]
21. Tom A. Peter, “Pittsburgh Is Robot Country,” The Christian Science Monitor, July 16,

612
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32.2

NOTES

2008. [600]
22. Nicola Muscettola et al., “Remote Agent: To Boldly Go Where No AI System Has Gone
Before,” Artificial Intelligence, Vol. 103, pp. 5–47, 1998. Available online at
http://groups.csail.mit.edu/mers/papers/aij98.pdf. [600]23. Available online at
http://nmp-techval-reports.jpl.nasa.gov/DS1/Remote Integrated Report.pdf. [600]24. Douglas E. Bernard et al., “Remote Agent Experiment DS1 Technology Validation
Report,” Jet Propulsion Laboratory, California Institute of Technology and NASA Ames
Research Center, Moffett Field. Available online at
http://nmp-techval-reports.jpl.nasa.gov/DS1/Remote Integrated Report.pdf. [602]25. For a full description of the “bugs,” see P. Pandurang Nayak et al., “Validating the DS1
Remote Agent Experiment,” Artificial Intelligence, Robotics and Automation in Space,
Proceedings of the Fifth International Symposium, ISAIRAS ’99, held June 1–3, 1999, in
ESTEC, Noordwijk, the Netherlands, M. Perry (ed.), ESA SP-440, Paris: European Space
Agency, pp. 349–356, 1999. Available online at
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.30.2688&rep=rep1&type=pdf.
[602]
26. mers.csail.mit.edu. [603]27. E-mail of January 25, 2009. [603]28. These data are from “Traffic Safety Facts,” available online at
http://www-nrd.nhtsa.dot.gov/Pubs/811017.PDF. [604]29. See http://ijcai.org/Past%20Proceedings/IJCAI-97-VOL2/PDF/117.pdf. [604]30. http://www.darpa.mil/grandchallenge04/program.pdf. [604]31. See http://www.darpa.mil/grandchallenge04/program.pdf for descriptions. [604]32. To see how they fared, visit http://www.msnbc.msn.com/id/4517001/. [604]33. See http://www.cnn.com/2004/TECH/ptech/03/14/darpa.race/index.html. [605]34. This quotation is taken from a CNN report written by Marsha Walton at
http://www.cnn.com/2004/TECH/ptech/03/14/darpa.race/index.html. [605]35. See http://www.darpa.mil/grandchallenge05/InitialPressRelease.pdf. [605]36. See http://www.imdb.com/title/tt0453467/trivia. [605]37. E-mail communication of December 1, 2008. [606]38. Readers who are interested in digging deeper might consult various papers available on
the Web, for example one describing Sandstorm at
http://www.darpa.mil/grandchallenge05/TechPapers/RedTeam.pdf and one describing
H1ghlander at http://www.darpa.mil/grandchallenge05/TechPapers/RedTeamToo.pdf.
[607]
39. For an overview paper describing Stanley, see Sebastian Thrun et al., “Stanley: The
Robot That Won The DARPA Grand Challenge,” Journal of Field Robotics, Vol. 23, No. 9,
pp. 661–692, June 2006. Available online at
http://robots.stanford.edu/papers/thrun.stanley05.pdf. [607]40. For more information, see Sebastian Thrun, Michael Montemerlo, and Andrei Aron,“Probabilistic Terrain Analysis for High-Speed Desert Driving, in G. Sukhatme et al. (eds.),
Proceedings of the Robotics Science and Systems Conference, II, Philadelphia, PA, 2006.
Available online at http://robots.stanford.edu/papers/thrun.mapping-Stanley.pdf. [608]41. Hendrik Dahlkamp et al., “Self-supervised Monocular Road Detection in Desert
Terrain,” in G. Sukhatme et al. (eds.), Proceedings of the Robotics Science and Systems
Conference, II, Philadelphia, PA, 2006. Available online at

613
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32

NOTES

http://robots.stanford.edu/papers/dahlkamp.adaptvision06.pdf. [608]42. For more information, see David Stavens, Gabriel Hoffmann, and Sebastian Thrun,“Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road
Autonomous Driving,” Proceedings of the International Joint Conference on Artificial
Intelligence. pp. 2218–2224, 2007. Available online at
http://robots.stanford.edu/papers/stavens hoffmann thrun ijcai07.pdf. [609]43. See
http://www.wired.com/wired/archive/14.01/robots.html?pg=1&topic=robots&topic set=.
[609]
44. The official DARPA Web site for the event is at
http://www.darpa.mil/GRANDCHALLENGE/index.asp. [609]45. http://www.tartanracing.org/tech.html. [610]46. See http://www.vw.com/vwbuzz/browse/en/us/detail/Volkswagen to contribute 5 75 million to Stanford University/180. [610]47. Reported by the Associated Press. See
http://www.mercurynews.com/nationworld/ci 9664682. [611]48. E-mail of June 21, 2009. [611]49. E-mail of October 17, 2008. [611]50. E-mail of January 14, 2009. [611]614
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33.0

Chapter 33

Ubiquitous Artificial
Intelligence
In today’s world, the magic of AI is everywhere – maybe it’s not “full AI” butthere are significant parts. Allen Newell foresaw these bits and pieces as partof an “enchanted land.” In an address given in 1976, he called computers the“technology of enchantment.” He noted two ingredients that made it so:1
First, it is the technology of how to apply knowledge to action toachieve goals. . . That is what algorithms and programs are allabout – frozen action to be thawed when needed.
The second ingredient is the miniaturization of the physicalsystems that have this ability for intelligent action.
Thus, computer technology differs from all other technologies
precisely in providing the capability for an enchanted world:
For brakes that know how to stop on wet pavement
For instruments that can converse with their users
For bridges that watch out for the safety of those whocross them
For streetlights that care about those who stand underthem – who know the way, so no one need get lost
For little boxes that make out your income tax for you
In short, computer technology offers the possibility ofincorporating intelligent behavior in all the nooks and crannies of
our world. With it, we could build an enchanted land.

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33

Ubiquitous Artificial IntelligenceLet’s see some examples of how AI is already inhabiting the “nooks andcrannies of our world.” I’ll start with our houses and some of the things in andaround them.

33.1

AI at Home

Homes and their contents are becoming more intelligent. Here is a partial listof what you might find today (or sometime soon) on a tour of a modern house:• thermostats for heat and air-conditioning systems that anticipate
temperature changes and the needs of occupants, communicate withother home devices, and take appropriate actions in advance;• microwave ovens that read barcodes on packages to determine how longto cook an item;• smart running shoes with a computer chip that senses the runner’s sizeand stride length and directs on-going changes in the heal cushioning via
a miniature screw and cable system;• washing machines that automatically adjust to different conditions towash clothes better;• refrigerators that automatically inventory their contents and informowners of needed items;• cameras with computer vision systems to identify faces and to control
focusing, exposure, and framing;• hearing aids that adapt to ambient sound levels and block out “cocktail
party” chatter;• robotic pet “animals” and toys that interact with people;• floor-washing and vacuum-cleaning robots; and• caretaker robots for the elderly or infirm.
This list will continue to grow. Some AI researchers talk about a field
called “ambient intelligence,” where the “broad idea is to enrich a space (suchas a room, house, building, bus station, or a critical area in a hospital) withsensors tied to intelligent software, so that the people using the space canbenefit from a responsive, even wise environment.”2 The components in anenvironment permeated by ambient intelligence are also being networked sothat they can communicate with each other and so that people cancommunicate with them using ordinary speech. Vlingo, Nuance, and Yap are
three companies that sell products for mobile phones (such as the iPhone) that616
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33.3

Advanced Driver Assistance Systems

translate voice into text. Vlingo, for example, claims on its Web site that if
you say “pizza places in Pittsburgh” to your phone, it then “figures out what
you want, finds it and shows you how to get there. No tapping, no thumbs,
just good old speaking.”3 Presumably similar technology could enable one tocommand and query one’s smart appliances by talking to them.

33.2

Advanced Driver Assistance Systems

Perhaps the landscape with the most places into which computers andartificial intelligence have crept is the modern passenger automobile. Today’s
cars can have as many as fifty microprocessors controlling such things as
automatic transmissions, fuel injection systems, antilock brakes, airbags,
security systems, and cruise control systems to name just a few. And,
although not yet completely autonomous, more and more automobiles are
beginning to be equipped with safety features called “advanced driverassistance systems” (ADAS).
Here is a list of just a few of the ADAS features that are either available
now or are being planned by several automobile manufacturers:• adaptive cruise control (ACC) for providing more intelligent control ofspeed, enabling the vehicle to slow down or speed up depending on
traffic conditions as perceived by radar or laser sensors;• intelligent speed adaptation (ISA) for monitoring local speed limits,slowing the vehicle down (or warning the driver) when it enters zones
with speed limits;• lane control systems for monitoring the presence of vehicles orobstructions in adjacent lanes and for monitoring when a driver drifts
into an adjacent lane or off the roadway;• automatic parking systems for assisting a driver when executing aparallel parking maneuver;• traffic sign recognition systems;• driver drowsiness detection systems; and• intelligent tire pressure control systems.
Although not all automobile computers employ AI technology, the mostambitious of the ADAS features use computer vision, planning methods,
probabilistic inference, and machine learning. The motivation for using ADAS
is the desire to eliminate automobile injuries and fatalities. In 1997, forexample, the Swedish parliament passed a “Road Traffic Safety Bill” whose
goal, “Vision Zero,” is that “no one will be killed or seriously injured whenmoving within the road transport system.”4617
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33

Ubiquitous Artificial Intelligence33.3

Route Finding in Maps

While we are on the subject of automobiles, one of the things drivers often
need to know is how to get from one place to another. Many automobiles havedevices that “talk” you to your destination using on-board GPS systems, map
databases, and speech synthesis. Map databases can be thought of as graphs
consisting of place “nodes” and connecting road “links.” So whether thenavigation advice is provided ahead of time by your home computer (using
Google Maps, for example) or by an on-board navigation system, it isgenerated by a process of searching a graph to find a path from some node, A,
to some other node, B.
You will recall that the most commonly used graph-searching procedure isA∗ , a heuristic search method that takes into account both the distancetraveled so far and an estimate of the distance to the goal (see p. 220.) Is A∗
used in route-finding programs? Well, Google, for example, will only say thatit uses “state-of-the-art hierarchical graph algorithms to compute shortest
paths in routing networks in a matter of milliseconds.”5 Most likely thesealgorithms, and similar ones used by other route-finders, use heuristic
techniques similar to those used by A∗ but specialized to the case of searchingtwo-dimensional maps. For example, the searches are hierarchically organized.
That is, for trips to a distant goal, large-scale maps with just the major roads
and highways are searched. Then, to get from a starting position to a majorroad on the way to the goal, a more detailed map with less-traveled roads isused. Hierarchical search may result in slightly suboptimal, but neverthelessquite acceptable, paths. In addition, when responding to billions of queries,
some of the computations that would have to be repeated for each query canbe shared among them instead.
Most route-finding programs can (and do) take into account criteria otherthan distance, such as estimated travel times. For example, Microsoft’s
ClearFlow program, which uses Bayesian networks informed bytraffic-monitoring sensors to estimate traffic densities, can base route
recommendations either on shortest time, shortest distance, or current traffic
conditions. (Try it out at http://maps.live.com/.)

33.4

You Might Also Like. . .

When I log on to Amazon.com’s Web site, it responds “Hello, Nils J. Nilsson.
We have recommendations for you.” It then lists some items that it guesses I
might like including the book Portuguese Irregular Verbs by Alexander McCall
Smith (recommended because I had previously purchased another book by thesame author) and the video Lipstick Jungle with Brooke Shields because I had
previously downloaded the video BBC Shakespeare: Othello(?!). It then lets
me make some changes. When I say that I am not interested in Lipstick618
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33.5

Computer Games

Jungle it wonders whether or not I might be interested in the video How I Met
Your Mother. Perhaps I have not ordered enough videos to give it a betteridea of what I might be interested in.
Amazon’s recommendations are based on what is called social or
collaborative filtering. A database of preferences (for books, movies, orwhatever) is maintained for every user. If user B’s preferences correlatesufficiently with those of user A, a collaborative filtering system would
recommend some of B’s purchases (not already bought by user A) to user A.
Rather straightforward machine learning techniques are used to find preference
correlations among users. Several other sites, including iTunes, TiVo, andNetflix, base their recommendations on collaborative filtering.
Another type of recommending system uses what is called content-based
filtering, in which a user’s preferences for books, movies, documents, orwhatever are analyzed to find similarities with other items of the same kind
(instead of with other users having the same preferences). The most similar
items are then recommended. For documents, for example, comparisons might
be made using the vector representations I discussed in Section 27.3.
Content-based filtering is widely used for blocking unwanted e-mail (such asspam) and Web sites (such as pornography). It’s also used for personalized
Web searches – for example in customizing news feeds to gather news about
particular topics. Recommendation systems that combine collaborative andcontent-based methods have also been developed. Interested readers mightwant to see a special issue of the journal AI Communications on“Recommender Systems.”6

33.5

Computer GamesIn addition to the use of AI techniques for playing games such as chess andcheckers, AI is beginning to be used in the kinds of computer games in whichhuman users interact with artificial characters in a simulated world. Although
the emphasis in these games has been on rich and realistic graphics, the use of
AI techniques can make them even more appealing and challenging.
Developers and aficionados of these games use the term “Game AI” todistinguish the kinds of AI in computer games from what they call academicor “R&D AI.” They point out that in many games all that is required is theillusion or appearance of intelligence – much like the ELIZA program appears
to be able to carry on a conversation but does not really know what it istalking about.
Nevertheless, computer games are rich with possibilities for the use of AI.
In the usual setting, a human player interacts with and competes with artificial
agents in the game; these agents are called nonplayer characters (NPCs).
Among other tasks, the NPCs have to be able to navigate from place A intheir world to place B without bumping into obstacles or other NPCs, and619
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33

Ubiquitous Artificial Intelligence

thus many games use A∗ . But also, the NPCs should be able to perceive theirsimulated environment, make and execute plans, and learn – just like robots inreal environments do. The more intelligently they can do those things, themore realistic they will appear to the human player (and purchaser) of thegame. I’ll mention two representative examples of games with these abilities.7
The game Black and White 2, developed by Lionhead Studios andmarketed by Electronic Arts, uses a combination of neural nets and decision
trees. According to a Web site for the game, the NPCs (evil and benevolent
deities) “can learn strategies, master new abilities and skills, [and] lead armies
into battle. . . Every choice you make will have an impact. Each action andinaction prompts obvious changes to buildings, flora and fauna, all morphing
to reflect your personality.”8The game F.E.A.R. (First Encounter Assault Recon) by Jeff Orkin and
Monolith Productions uses A∗ to plan sequences of NPC actions in addition toits usual role in path finding.9
Black and White and F.E.A.R. were rated numbers 1 and 2, respectively,of the “Top 10 Most Influential AI Games,” by http://AIGameDev.com, a
Web site about the use of AI in games. There are several Web sites andconferences devoted to AI in games.10
Some AI researchers have advocated using computer games as aconvenient arena for developing new ideas for intelligent agents. For example,
University of Michigan professor John Laird has written, “[because] research inrobotics requires solving many difficult problems related to low-level sensing
and acting in the real world that are far removed from the cognitive aspects ofintelligence, . . . computer games provide us with a source of cheap, reliable,
and flexible technology for developing our own virtual environments forresearch.”11
The bits and pieces of AI just discussed, whether in the home, in theautomobile, or in computer games, are usually of the “reactive” or“behavior-based” variety. Conditions are sensed, and actions are taken
depending on what is sensed. Building systems that are able to react
appropriately to the situation at hand has been an important strand of AI
research. AI agents that inhabit dynamic environments, whether simulated orreal, must also be able to decide when to react and when to deliberate. Albert
Lewis, when he was a cornerback of the Oakland Raiders Football team, had
this to say about when to react and when to think:12
When you think on the field, you’ve automatically lost that down.
The time you should be thinking is during the course of the week
in practice. That’s when the light should go on. When you get in
the game, it’s all about reacting to what you see.
One can imagine that the simulated football players (the NPCs) in games like
Madden NFL 09 will use increasingly complex reactive strategies aided, when620
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33.5

NOTES

appropriate, by higher level reasoning.

Notes
1. For a published version of his address, see Allen Newell, “Fairy Tales,” AI Magazine,
Vol. 13 No. 4, 1992. Available online at
http://www.aaai.org/ojs/index.php/aimagazine/article/download/1020/938. [615]2. See the article by Juan Carlos Augusto and Daniel Shapiro, “The First Workshop on
Artificial Intelligence Techniques for Ambient Intelligence (AITAmI ’06),” AI Magazine, Vol.
28, No. 1, pp. 86–87, Spring 2007. Also see
http://www.infj.ulst.ac.uk/∼jcaug/aitami07.htm. [616]3. See http://www.vlingo.com/. [617]
4. Swedish Ministry of Transport and Communications, 1997. [617]5. E-mail from Peter Norvig, Director of Research at Google, Inc., September 9, 2008.
[618]
6. AI Communications, Special Issue on Recommender Systems, Dietmar Jannach, Markus
Zanker, and Joseph Konstan (guest eds.), Vol. 21, Nos. 2–3, 2008. [619]7. I thank John Laird for mentioning these games to me (e-mail of September 10, 2008).
[620]
8. http://www.lionhead.com/bw2/Default.aspx. [620]9. See Jeff Orkin, “Three States and a Plan: The A.I. of F.E.A.R.”; available online at
http://web.media.mit.edu/∼jorkin/gdc2006 orkin jeff fear.doc. [620]
10. See, for example, http://www.gameai.com/, http://www.igda.org/ai/,
http://www.aiwisdom.com/bygame.html, and http://aigamedev.com/. [620]11. John E. Laird, “Research in Human-Level AI Using Computer Games,”
Communications of the ACM, Vol. 45, No. 1, pp. 32–35, 2002. [620]
12. As quoted by Sam Farmer in the San Jose Mercury News, page 1D, August 30, 1996.
[620]

621
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33

NOTES

622
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34.1

Chapter 34

Smart Tools
In between the headline-making, milestone AI achievements and the smaller
bits of computational intelligence that we find everywhere are impressive AI
programs that are used by physicians, scientists, engineers, and businesspeople to help them in (and sometimes automate) their workaday tasks. I callthese the “smart tools” of AI. Sometimes these are stand-alone systems, butmore often they are integrated into a larger computational framework or into
hardware devices. Some work only when called upon to help solve someparticular problem, such as disease diagnosis. Some are constantly active, suchas online stock-trading systems. I’ll not be able to mention all of them since
there are far too many, and some are known only to their corporate andgovernment users. But a few examples will serve to illustrate their utility andvariety.

34.1

In Medicine

Let’s start with how AI is being used in medical clinical practice. Beginning as
early as the 1980s, AI technology has been an important part of medical
systems and devices. In March 2000, a monthly magazine titled Medical
Device & Diagnostic Industry published an article claiming that “the medical
device industry is seeing an emergence of computer-based intelligent decision
support systems (DSSs) and expert systems, the current success of whichreflects a maturation of artificial intelligence (AI) technology.”1 It mentioned
several AI-infused devices, including the “Agilent Acute Cardiac Ischemia
Time-Insensitive Predictive Instrument. . . , an intelligent electro-cardiagram(ECG) device that predicts the probability of acute cardiac ischemia (ACI), a
common form of heart attack,” and the General Electric “MAC 5000 Resting
Test System, [incorporating] the Marquette 12SL ECG analysis program, anintegrated DSS that uses newly developed digital processing methods and623
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34

Smart Tools

diagnostic program algorithms to interpret and classify ECG waveforms.”
A September 2005 online review2 in Clinical Window (which is sponsored
by GE Healthcare) of ECG devices by Dr. Paul Kligfield, Division of
Cardiology at Cornell University, stated that “digital electrocardiographs of all
major manufacturers now are capable of providing automated diagnostic
statements that can help the physician.” However, he also mentioned cases
where these statements could “mislead the physician.” In evaluating aparticular device, Dr. Kligfield stated that in 3,954 patients without
pacemakers, 7.8% of the cardiac rhythm interpretations required revision by
the combined opinion of two expert cardiologists.
OpenClinical3 maintains a family of Web sites listing a number of decision
support systems in current use. Among these are Athena DSS (for hypertension
management), Gideon (for infectious diseases), Iliad (for internal medicine),
TherapyEdge HIV (for HIV patient management), and several others. Some ofthe systems listed trace their ancestry back to MYCIN, INTERNIST-1, PUFF,
and diagnostic systems based on Bayesian networks. More details about theseand other systems can be gleaned from the OpenClinical Web pages. Anothersource of information is the Elsevier journal Artificial Intelligence in Medicine.
I’ll describe a couple of representative examples. ATHENA DSS is a systemfor providing advice to physicians about managing hypertension in a mannerconsistent with guidelines defined by the U.S. Institute of Medicine. It wasdeveloped jointly by Stanford Medical Informatics, the Veterans
Administration Palo Alto Health Care System, and the Stanford Center forPrimary Care and Outcomes Research. ATHENA processes a patient’s clinicaldata against hypertension management knowledge in its knowledge base andgenerates patient-specific recommendations for management during a clinicalvisit. It is in use and undergoing continuing evaluation and upgrading atseveral Veterans Administration medical centers. A new version, called
ATHENA-HTN, is being evaluated. ATHENA’s technology stems from previous
medical rule-based systems developed at Stanford. According to Mark Musen,
Head of the Stanford Biomedical Informatics Division, “ATHENA uses the EON
task-specific architecture for assisting protocol-based medical care that grewout of my dissertation work in the late 1980s, which grew out of ONCOCIN [aprogram for helping to manage oncology protocols], which grew out of
MYCIN. . . .”4Another system, Gideon, is a program to help physicians diagnose andtreat country-specific diseases. Gideon makes its diagnoses based on a large
database of diseases, symptoms, signs and laboratory findings, and countries.
Bayesian analysis is used in the computation of the probability of a disease
given data about a patient. The original version of Gideon was developed by
Stephen A. Berger, M.D., at the Tel Aviv Sourasky Medical Center and Uri
Blackman at the University of Tel Aviv. Blackman is now CEO of Gideon
Informatics, Inc., in Los Angeles.5 There is a version of the program that canbe accessed on mobile phones or PDAs.
624
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34.2

For SchedulingAccording to the Gideon Web site,6Gideon is made up of four modules: Diagnosis, Epidemiology,

Therapy and Microbiology. The constantly updated databaseincludes 337 diseases, 224 countries, 1,147 microbial taxa and 306
antibacterial (-fungal, -parasitic, -viral) agents and vaccines.
Gideon’s world wide data sources essentially include the entire
world’s literature and adhere to the standards of Evidence Based
Medicine. Over 10,000 notes outline the status of specific infections
within each country. Also featured are over 20,000 images, graphs,
interactive maps and references.
...
In a blinded multicenter field trial of 495 patients, the correct
diagnosis was displayed in over 94% of cases, and was listed first inover 75%.
A reviewer in the Journal of the American Medical Association wrote“Gideon: The Global Infectious Disease and Epidemiology Network is asuperbly designed expert system created to help physicians diagnose any
infectious disease (337 recognized) in any country of the world (224 included).
. . . This diagnostic system is remarkable for its ease of use, breadth of scope,and depth of information. It is as practical a program as one could hope for.”7
Notwithstanding the success of diagnostic systems, such as Gideon, most of
the applications of AI in medicine involve DSSs, which can be used byphysicians for reference. As Thomas Rindfleisch, an expert in medical
informatics says, “The name DSS is significant in that doctors always need bein charge of final patient-related decisions to avoid FDA regulation of thesoftware.” Rindfleisch also mentions that for DSSs to be useful for mostphysicians, they have to be integrated with electronic medical and healthrecord systems (so that doctors don’t have to type in all the neededbackground information about a patient).8 However, a survey of 2,758
American physicians (taken in late 2007 and early 2008) found that only 17%of them had access to electronic record systems.9

34.2

For SchedulingIntelligent scheduling software is another area where AI techniques are being
used. One example is the AURORATM system marketed by Stottler Henke
Associates, Inc., a company specializing in applying “artificial intelligence andother advanced software technologies to solve problems that defy solution
using traditional approaches.” AURORA is being used by the Boeing Company
to help schedule and manage the building of the Boeing DreamlinerTM .
Stottler Henke says that “once AURORA has created a schedule, it displays it in625
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34

Smart Toolsa series of graphical images that allow the user to see the scheduled activities,
resource allocations and the temporal relationships among the activities.”10
TEMPORISTM , developed by United Space Alliance, LLC, is an intelligent

spaceflight mission management and planning tool for use by the crew onboard future space missions. TEMPORIS will help crews schedule all aspects of
their in-flight lives, including routine daily activities, spacecraft housekeeping,and conducting on-board experiments. But producing acceptable schedules
requires “volumes of spaceflight constraints, flight rules, dependencies,
sequences, medical guidelines and safety requirements.” According to acompany press release, these can now be “efficiently embedded into
TEMPORIS’s intelligence. To illustrate: It currently takes 50 mission planners
working 24/7 for two weeks to schedule one day’s worth of activities on theInternational Space Station. TEMPORIS reduces that 2-week job to a few
moments with the click of a computer mouse.”11 Stottler Henke’s AURORA
software is an integral part of TEMPORIS.

34.3

For Automated Trading

AI data mining, text processing, and decision methods are used in the analysisof real-time trading data and news feeds to make automatic buy-and-selldecisions on stocks, commodities, and currencies. Up-to-the-minute news
sources in digital form are readily available. The Reuters “NewsScope
Archive”12 and the Dow Jones “Elementized News Feed”13 are among news
feeds that are used for automated trading and analysis. Reuters, for example,
claims to provide14
customers seeking to develop news-based programmatic trading
strategies with a comprehensive, machine-readable archive ofReuters global news. Events are presented exactly as they broke tothe markets, with each release of information timestamped to themillisecond and tagged with an array of metadata fields for easymachine consumption.
According to an article in the New York Times about automatedtrading,15 Professor Andrew Lo, the Director of the MIT Laboratory for
Financial Engineering, and colleagues discovered that there was a correlation
between how often certain words, such as “anxiety,” “depression,” and“bankrupt,” appeared in news stories and future values of the S&P stockindex. These correlations, among other things, can be used by stock trading
algorithms to initiate stock trades.
Vhayu Technologies Corporation, one of the firms offering algorithmic
trading services, claims that “8 of the top 10 global financial institutions use
one of its products,” namely, Vhayu VelocityTM , “to identify opportunities in626
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34.5

In Business Practices

milliseconds.”16 Another firm, Streambase Systems, says that with one of its
products “leading trading organizations track critical market conditions acrossmultiple markets and instantaneously execute sophisticated strategies tocapture short-lived trading opportunities.”17 Of course, with such instantfeedback there is the potential that news of a sell order might trigger other sellorders, and so on, leading to swift downdrafts in the market (and vice versa).

34.4

In Business Practices

Business Rule Management Systems (BRMSs) are descendants of therule-based expert systems of the 1980s. Examples are Fair Isaac’s “BLAZE
ADVISOR 6.1,” ILOG’s “JRules 6.0,” and Information Builders’s “WebFOCUS.”
Business rules express information about how a business operates – its policies
and constraints. All companies have such rules. As Fair Isaac puts it, these
are usually expressed “in conversation, written text and software – as ‘If, then’statements [such as] ‘If the loan applicant does not have a sufficient credit
history, then pull a report from a debit bureau.’” In BRMSs, these rules areusually encoded in English-like, computer-readable syntax. Unlike rules usedin some expert systems, they are not annotated with probabilities or certainty
factors but are definite statements of business practice. Because theinformation expressed by business rules changes from time to time, it isimportant that the rules be maintained to reflect current policies.
Rule engines are used to perform both forward and backward inference
over a network of rules. A descendant of the Rete algorithm, which wasmentioned on page 301, is used by the inference engine in BLAZE ADVISOR, forexample. Conclusions are used to communicate policy, late-breaking business
opportunities, and needs for action among staff and other parties. In somecases conclusions evoke automatic actions such as ordering, sending e-mails,
and so on. For example, Information Builders advertises that with their suiteof event management solutions, “the automated process itself is able to makepredetermined decisions and take specific courses of action based on thresholds
contained within the business intelligence content that is fed to it.”18 An
article about WebFocus gives an example: “[An order arriving] can trigger aseries of responses and decisions – e.g. based on WebFOCUS analytics
embedded in the process, the physical size of the order can be determined. If
it is too big for the warehouse space available, WebFOCUS analytics can triggera change in the process that ships the order to a different warehouse that has
the required space, and then alerts warehouse employees that a larger than
expected order will be arriving.”19627
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34

Smart Tools

34.5

In Translating Languages

Several commercial natural language translation systems now exist. IBM’s
speech-to-speech translator (MASTOR) can (according to its Web site)translate free-form English speech into Mandarin speech (and vice versa).20
BBN Technologies has developed a number of speech processing systems. One
is their “Broadcast Monitoring System,” which (according to its Web site)“creates a continuous searchable archive of international television
broadcasts.” As explained on the site, “The system automatically transcribes
the real-time audio stream and translates it into English. Both the transcript
and translation are searchable and synchronized to the video, providing
powerful capabilities for effective retrieval and precise playback of the video
based on its speech content. With this revolutionary system, users can siftthrough vast collections of news content in other languages quickly andefficiently.”21 SRI International’s IraqComm translation system can transformspoken English into translated spoken colloquial Iraqi Arabic (and vice versa).
Currently (according to its Web site) it is “tailored to translate spoken
interactions on topics on force protection, security, and basic medical services,and can be customized to include other topics as needed.”22

34.6

For Automating Invention

John Koza, the inventor of Genetic Programming (GP), a search method
based on simulating the processes of evolution, claims that GP is itself an“invention machine.” (He also claims that GP more-or-less subsumes AI
because AI’s goal is to produce intelligent programs and GP does just that.)
For example, Koza and colleagues used GP to evolve (after thirty-one
generations) an optimal antenna system.23 They have also evolved (sometimes
after hundreds of generations) designs for optical lenses. As one of their papers
states, “One of the genetically evolved lens systems infringed a previously
issued patent, whereas the others were noninfringing novel designs that
duplicated (or improved upon) the performance specifications contained in thepatents.”24 Other evolved designs include those for electrical circuits,
controllers, mechanical systems, and other devices.25 The goal of the group is
to produce what they call “human-competitive designs,” that is, ones whose
specifications meet or exceed those of the best human designers. With
expected increases in computer power, I believe that the use of GP and
GP-like search methods will likely produce several interesting new inventions.

34.7

For Recognizing Faces

People are quite good at recognizing familiar faces whether “live” or inphotographs. They can often do this regardless of pose, scale, facial
628
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34.7

For Recognizing Faces

expression, or lighting conditions. (Interestingly, they don’t do very well if thephotograph of a face is presented upside down.) Computers are getting better,so much so in fact that I might have included computer face recognition in thechapter on Ubiquitous Artificial Intelligence. Although not quite ubiquitous
yet, face-recognizing systems are becoming more common at airports, banks,and places where personal identity must be verified or established. According
to some people who worry about privacy, the practice is too common,
Work on face recognition by computer has continued from its early days.
How far has it progressed? A 2007 National Institute of Standards andTechnology report on face-recognition tests claimed (among other things),“The results show that, at low false alarm rates for humans, seven automatic
face recognition algorithms were comparable to or better than humans at
recognizing faces taken under different lighting conditions. Furthermore, threeof the seven algorithms were comparable to or better than humans for the fullrange of false alarm rates measured.”26 The best methods use machinelearning algorithms working on very large data sets.27
A variety of different algorithms have been developed. Some are based onwell-known pattern-recognition techniques that sample features from a face
image and then compare these features against those of a large library ofidentified faces to find the closest match. Some algorithms use Bayesian
techniques and HMMs. Many of the methods use mathematical techniques toproject a high-dimensional vector representation of a face image into a vector
in a lower dimensional subspace. One method uses lower dimensional spaces
whose coordinates consist of a set of reduced images, called eigenfaces, whichhave the property that they can be combined to give good approximations to
any of the faces in the database (much like a set of individual audio tones ofdifferent frequencies can be combined to approximate arbitrary sounds). For a
Web page with information about face recognition with links to research
papers, books, algorithms, and vendors, see http://www.face-rec.org/.
New approaches continue to be developed. One method purports to showthat “image averaging” (that is, merging different images of the same face toform a single image) “greatly improves performance of [the commerciallyavailable FaceVACS] automatic face-recognition system.”28 An article in Wiredreports on a method developed by researchers at the University of California,
Berkeley, and the University of Illinois at Urbana-Champaign. According tothat article, Shankar Sastry, the Dean of UC Berkeley’s College of Engineering,
noted that this new method “renders years of research in the field obsolete.”29
There are already several commercial companies selling face-recognition
and face-locating software and equipment. For example, Oki Electric Industry
Co., Ltd., sells a product called FSE (Face Sensing Engine). It boasts many
applications including controlling access to information in camera-equipped
cell phones and other devices, sorting photographs based on recognizing faces,
and locating faces in a camera’s field of view. The German company Cognitec
Systems GmbH markets the FaceVACS system previously mentioned.30
629
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34

NOTES

Before closing this section on smart tools, I should mention that there areseveral other areas in which AI tools are enhancing human productivity. For
example, I could have mentioned tools for aiding (and automating) theprocesses of movie animation, for computer program writing and debugging,for industrial process control, for circuit and program verification, and forenhancing and searching the semantic Web. Tools powered by AI techniques
will be increasingly used to aid and amplify (and sometimes to substitute for)human cognitive, motor, and perceptual abilities. Just wait!
As I hope the past few chapters have demonstrated, some parts of thequest for artificial intelligence have been quite successful. AI has become more
and more a part of all of our lives as well as of those of specialists. But themain goal of the quest (for some of us at least) still remains, namely, endowing
artifacts with full human (as well as superhuman) capabilities for language,
perception, reasoning, and learning. So, let’s look next at where the quest
might lead us.

Notes
1. Ralph J. Begley et al., “Adding Artificial Intelligence to Medical Devices,” Medical
Device & Diagnostic Industry, pp. 150ff, March 2000. Available online at
http://www.devicelink.com/mddi/archive/00/03/014.html. [623]2. See http://www.clinicalwindow.net/cw issue 20 article1.htm. [624]3. See http://www.openclinical.org/aisinpracticeDSS.html. OpenClinical is a nonprofit
organization created and maintained as a public service with support from Cancer Research
UK under the overall supervision of an international technical advisory board. [624]4. E-mail communication on September 25, 2008. [624]5. See Stephen A. Berger and Uri Blackman, “A Computer Program for Diagnosing andTeaching Geographic Medicine,” Journal of Travel Medicine, Vol. 2, No. 3, pp. 199–203.
Available online at http://www.gideononline.com/reviews/JTM1995.pdf. [624]6. http://www.gideononline.com/index.htm. [625]7. Vincent J. Felitti, MD, Reviewer, Journal of the American Medical Association, Vol.
293, pp. 1674–1675, 2005. [625]8. Thomas Rindfleisch, e-mail of November 20, 2008. [625]9. Catherine M. DesRoches et al., “Electronic Health Records in Ambulatory Care – A
National Survey of Physicians,” The New England Journal of Medicine, Vol. 359, No. 1, pp.
50–60, July 3, 2008. [625]10. See http://www.stottlerhenke.com/news/pr aurora boeing.htm. [626]11. See http://www.unitedspacealliance.com/news/press/2006/060404.pdf. [626]12. See http://about.reuters.com/productinfo/newsscopearchive/. [626]13. See http://www.djnewswires.com/us/djenf.htm. [626]14. http://about.reuters.com/productinfo/newsscopearchive/. [626]15. Tim Arango, “I Got the News Instantaneously, Oh Boy,” New York Times, p. WK3,
September 14, 2008. Available online at http:630
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34.7

NOTES

//www.nytimes.com/2008/09/14/weekinreview/14arango.html?partner=rssnyt&emc=rss.
[626]
16. See http://www.vhayu.com/. [627]17. See http://www.streambase.com/algorithmic-trading.htm. [627]18. From
http://www.informationbuilders.com/products/webfocus/intelligent processes.html. [627]19. From Expert Systems, Vol. 23, No. 4, p. 245, September 2006. [627]20. See http://domino.research.ibm.com/comm/research.nsf/pages/r.uit.innovation.html.
[628]
21. See http://www.bbn.com/products and services/bbn broadcast monitoring system/.
[628]
22. See http://www.iraqcomm.com/. [628]23. John R. Koza et al., “Automated Synthesis of a Fixed-Length Loaded Symmetric Dipole
Antenna Whose Gain Exceeds That of a Commercial Antenna and Matches the Theoretical
Maximum,” Proceedings of the 9th Annual Conference on Genetic and Evolutionary
Computation, pp. 2074–2081, New York: Association for Computing Machinery, 2007. [628]24. John R. Koza, Sameer H. Al-Sakrana, and Lee W. Jones, “Automated ab Initio
Synthesis of Complete Designs of Four Patented Optical Lens Systems by Means of Genetic
Programming,” AIEDAM: Artificial Intelligence for Engineering, Design, andManufacturing, Vol. 22, pp. 249–273, Cambridge: Cambridge University Press, 2008. [628]25. See John R. Koza, Sameer H. Al-Sakran, and Lee W. Jones, “Multi-Domain
Observations Concerning the Use of Genetic Programming to Automatically Synthesize
Human-Competitive Designs for Analog Circuits, Optical Lens Systems, Controllers,
Antennas, Mechanical Systems, and Quantum Computing Circuits,” in Rick Riolo, Terence
Soule and Bill Worzel (eds.), Genetic Programming Theory and Practice IV, pp. 131–147,
New York: Springer-Verlag, 2007. [628]26. P. Jonathon Phillips et al., “FRVT 2006 and ICE 2006 Large-Scale Results,” NISTIR
7408, March 2007. Available online at
http://www.frvt.org/FRVT2006/docs/FRVT2006andICE2006LargeScaleReport.pdf. [629]27. Some of the data sets used by researchers are itemized at
http://www.face-rec.org/databases/. See also Ralph Gross, “Face Databases,” in Stan Z. Li
and Anil K. Jain (eds.), Handbook of Face Recognition, New York: Springer-Verlag, 2005.
Available at
http://www.ri.cmu.edu/pub files/pub4/gross ralph 2005 1/gross ralph 2005 1.pdf. [629]28. R. Jenkins and A. M. Burton, “100% Accuracy in Automatic Face Recognition,”
Science, Vol. 319, p. 435, January 25, 2008. [629]29. Bryan Gardiner, “Engineers Test Highly Accurate Face Recognition,” Wired, March 24,
2008. Available at
http://www.wired.com/science/discoveries/news/2008/03/new face recognition. [629]30. http://www.cognitec-systems.de/index.html. [629]631
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NOTES

632
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35.0

Chapter 35

The Quest Continues
Where will the AI adventure lead next? We can get some idea of theimmediate future simply by extrapolating present trends. Probably there will
be some new milestone achievements. Undoubtedly, pieces of AI technology
will become ever more common in our homes, automobiles, and activities, andthe specialists’ smart tools will become ever smarter and more numerous.
But predicting beyond where AI’s present momentum will take us isproblematic. Let’s look at how some previous predictions have fared. Simon’s
1957 prediction of a computer chess champion within ten years was markedly
overoptimistic. In 1973, SRI engineers led by Oscar Firschein iteratively
queried several AI “experts” about when certain “products” would be realized.
The medians and ranges of predicted dates were reported back to them, they
were given a chance to modify their predictions, and so on until the results
settled down. (This process of making predictions is called a Delphi method.1 )The following table shows a few of the final predictions:2
Product
Automatic medical diagnostician
Robot servant capable ofperforming all household tasks
Voice-operated typewriter
Automatic high-quality languagetranslator of text
Robot chauffeur for driving oncity streets and country highways

Medianprototype date
1976

Mediancommercial date
1980

2000
1985

2010
1992

1987

1995

1992

2000

The “robot servant” and the “robot chauffeur” still seem quite a ways off, but
the others were perhaps only somewhat too optimistic. (Well, the year 2000
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The Quest Continues

seemed a long way off back in 1973.)
Against this background of prediction successes and failures, I hesitate to
make any that do not seem rather obvious. Except, I will predict that
someday we’ll have human-made artifacts with levels of intelligence (in all of
its manifestations) equalling and exceeding that of humans. I make thatprediction because I believe that we humans are machines (for what else could
we be?) and that eventually we’ll be able to build machines that can dowhatever we can do because there will be economic as well as scientific reasonsfor doing so.
I’ll have more to say about “human-level artificial intelligence” later, but
let’s first look at some of the research projects underway in AI laboratories
during the early part of this century to see whether they give us any insights
about the future.

35.1

In the Labs

There are now probably hundreds of laboratories – industrial, government, andacademic – that carry on research in artificial intelligence. I could not possibly
describe even a small part of what is going on in them, and, in any case,projects come and go. Just as a historian cedes accounts of current events tonewspapers and other media, I recommend that readers wanting to stay
current on AI research visit the Web sites maintained by the individual AI
laboratories, AI societies, government agencies that support AI research, andspecialized conferences and workshops.3 To give some of the flavor of thebreadth of current research, I’ll mention a few projects ongoing during the firstfew years of this century. Of course, these are research projects so it’s possible,
but not certain, that some of them will leave their marks on the future.

35.1.1

Specialized Systems

Building smart tools for work in specialized areas is still a big part of AI
research. However, work on these tools is increasingly less AI-centric and is
merging with the disciplines upon whose technologies these efforts depend –such as statistics, control engineering, image processing, and linguistics, among
others. Of course the new techniques invented and used in building even themost specialized niche systems might, in fact, be broadly applicable in otherareas of interest to AI.
A. Content-Based Image Retrieval
Present-day image and video search engines that respond to queries composedof words, such as “motorcycles,” do so by looking for Web sites that contain634
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In the Labs

the text “motorcycle” along with some image or video file (say in jpeg,
quicktime, or some other appropriate format). Unfortunately, the image orvideo file in some of the sites retrieved by these methods might not evencontain a motorcycle. Research is underway to base image and video searchmore on the content of images rather than on the content of associated text
alone.
One such project has been undertaken by researchers at Oxford University
and at Microsoft Corporation.4 Using images from a public Web site (Flickr),
a “query” consists of outlining (with a rectangle) that part of an image that
contains a sought-for object. That part of the image is then converted into avector representation that preserves key features of the image of the object.
Vector representations of images in a large image database are analyzed toform “clusters” of similar vectors. The idea is that each cluster is associatedwith images of similar objects. The vector representation of the query is thenmatched against the image database vectors to find clusters of the mostsimilar images. These can then be ranked, and the high-ranking images, with
object regions outlined, are returned as answers to the query. Some examplesof query images and response images are shown in Fig. 35.1. The query image,with the query region outlined, is on the left, and seven returned images (withtarget regions outlined) are on the right. Of course, as with any search
process, there will be false positives returned also, but in these examples thefalse positives appeared later in the list than the response images shown.

Figure 35.1: Searching for objects in images. (From James Philbin et al.,“Object Retrieval with Large Vocabularies and Fast Spatial Matching,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2007. Available online at http://www.robots.ox.ac.uk/∼vgg/
publications/papers/philbin07.pdf.)
The authors evaluate their work as follows:
The system returns photos from the corpus that contain a query
object, despite substantial differences in lighting, perspective,
image quality and occluders between the query and retrieved
images.
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We view this as a step towards the ultimate goal of building a
Web-scale retrieval system that will scale to billions of images. We
have therefore concentrated on algorithms which are scalable, andwhich we believe are straightforward to distribute over a cluster ofcomputers.

They acknowledge that further work is needed to find “efficient ways toinclude spatial information in the index [which is computed before images are
queried], and move some of the burden of spatial matching from the ranking
stage to the filtering stage.”
Other projects involve content-based video retrieval. When users submit avideo to YouTube, for example, they also submit “tag” words to help describe
it. Example tag words that might be used are beach, hiking, soccer, cats,
concerts, and so on. Tagging takes effort, so some researchers are attempting
to automate that process using statistical machine learning methods. From a
database of already tagged videos, a group of German researchers hasdeveloped a prototype system that extracts image information for use insuggesting tag words for other videos.5 Systems such as theirs mightultimately be used for tagging large corpora of videos. Once tagged, these
corpora could be more easily searched.
Along these lines, two different companies, VideoSurf and Digitalsmiths,
have announced products that allow versions of content-based searching.
VideoSurf’s Web site (http://www.videosurf.com/about) claims that “using aunique combination of new computer vision and fast computation methods,
VideoSurf has taught computers to ‘see’ inside videos to find content in a fast,
efficient, and scalable way.”
B. Meaning-Based Web Search
Next, I’ll mention an AI project at a commercial company, Powerset. Powerset
began as a San Francisco start-up developing an Internet search engine thatuses natural language understanding techniques. (The company was acquiredby Microsoft in 2008.) Powerset claims to be able to find “articles related tothe meaning of your query. And sometimes direct answers.”
The query can be a statement or a question posed in ordinary natural
language. A grammar and semantic processor are then used to parse the query– converting it to a representation that expresses the meaning of the original
sentence. Their prototype version is limited to searching Wikipedia articles,
which have also been processed to extract meanings. A matching procedure isthen used to return those articles whose meanings are most related to thequery.
The developers think that their technology will scale beyond Wikipedia tobe able to deal with more of the Web. The natural language processing
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In the Labs

technology used by Powerset is based on the Lexical Functional Grammar
originally developed by Joan Bresnan and Ronald Kaplan. Kaplan, formerly aresearcher at PARC (Palo Alto Research Center) is now an employee ofPowerset. Here is an example of a Powerset search using their Web site.6 I
typed the query “What technology has powerset licensed?” It answered “Thecompany has licensed natural language technology from PARC, the former
Xerox Palo Alto Research Center. . . ,” and referred me to the Wikipedia pagefrom which it lifted its answer, namely, “Powerset (company),” which is at
http://en.wikipedia.org/wiki/PowerSet. If techniques for meaning-based
search scale up, as the Powerset people hope, the quality of Internet searchwould be dramatically improved.
C. Legged Robots
Marc Raibert did research at Caltech on walking, running, and hopping
robots. He continued related research as a professor at CMU and later at
MIT. In 1992, he started a company called Boston Dynamics, which according
to its Web site “specializes in robotics and human simulation.” One of their
prototype products is called BigDog (Fig. 35.2), a four-legged walking robot,
about the size of a Great Dane, claimed to be the “most advanced quadruped
robot on earth.” BigDog is extremely stable. It can walk, run, and climb onrough terrain. It is said to be able to carry a 340-pound load. It is powered bya gasoline engine driving a hydraulic actuation system. BigDog’s suite ofsensors includes a laser gyroscope, a stereo vision system, devices for sensing
joint positions and forces, as well as internal things such as engine temperatureand so on. Overall control is provided by an on-board computer. A movie of
its operation shows it walking through a forest, climbing a hill, recovering froma strong kick to its side and negotiating an icy parking lot – all without
falling. (Well, at least it didn’t fall in the movie I saw. The movie is available
at http://www.bostondynamics.com/dist/BigDog.wmv.) The BigDog projectis being supported by DARPA.
According to a paper about BigDog,7 the team is continuing to work onsuch problems as getting BigDog to right itself if it does happen to fall overand giving it more ability to navigate by itself. (It now relies mostly on ahuman operator to guide it.)
There are hundreds of more projects going on in the labs where “smart
tools” are being developed for hundreds of professions. The variety is amazing.
People are working on automating the process of movie animation,8
computational genomics,9 robotic surgery, business intelligence,10 and muchmore. Most of these are what I would call “niche” systems, focused on
performing specific, rather than generic, tasks. In the next section, I’ll mention
some laboratory work aimed either at building general-purpose systems or atdeveloping technology that might be applicable in a wide range of settings.

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The Quest Continues

Figure 35.2: Marc Raibert (left) and BigDog (right). (Photographs courtesy of
Boston Dynamics c 2008.)

35.1.2

Broadly Applicable Systems

A. Robotics
Ever since the days of Shakey the robot, AI researchers have used robots as
platforms for developing AI systems that integrate many aspects of intelligent
behavior and, therefore, aim for a kind of general utility. Work on
general-purpose robot systems has gone in and out of style, and robotics
researchers sometimes had to focus instead on special tasks, as might arise inindustrial automation, for example. Reacting to some of my proposals in the
1970s and 1980s for work on general-purpose robots, potential sponsors would
sometimes ask “Just exactly what is your robot going to do?” My answer thatthey were supposed to be “general purpose” seldom satisfied sponsors withspecific problems to solve. Now, however, there does seem to be a return toworking on robots that are able to do a lot of things – decathlon robots
instead of high-hurdle robots or pole-vaulting robots.
One example is the work headed by Stanford professor Andrew Ng with arobot named STAIR, an acronym for STanford AI Robot. STAIR is designed
to be a kind of “general factotum,” that is, a robot that can do a lot of things
including navigating home and office environments, picking up and interactingwith objects and tools, and intelligently conversing with and helping people inthese environments. (See Fig. 35.3.) According to its Web page,11 STAIR
integrates “methods drawn from all areas of AI, including machine learning,
vision, navigation, manipulation, planning, reasoning, and speech/natural
language processing. This is in distinct contrast to the 30-year trend of638
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35.1

In the Labs

working on fragmented AI subfields, and will be a vehicle for driving research
towards true integrated AI.”

Figure 35.3: STAIR unloading a dishwasher at the Stanford AI Lab. (Photograph courtesy of Andrew Ng.)
STAIR even learns how to pick up objects it has never seen before. Usingmachine learning methods, STAIR’s perceptual system was trained on a
database of a thousand or more pictures of each of a number of common
objects as they might be seen in a home or office. Each image was labeledwith the appropriate grasping position for that particular object. The objects
639
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35

The Quest Continues

included a stapler (2,001 example pictures), a coffee mug (2,001 example
pictures), a cereal bowl (1,001 example pictures), and several other items. The
training pictures showed the objects under different lighting conditions, fromdifferent camera positions, and in different orientations. (To ease the task ofcollecting and labeling training data, synthetic computer-graphics images wereused.) After training, STAIR could predict the best grasping point for several
novel objects and develop a plan to guide the arm and grasper. The system
was tested on a large set of novel objects, examples of which it had not seen
before. These included “rolls of duct tape, markers, a translucent box, jugs,
knifecutters, cellphones, pens, keys, screwdrivers, staplers, toothbrushes, a
thick coil of wire, [and] a strangely shaped power horn.” On average, STAIR
successfully picked up these objects 87.8% of the time. (To be counted as
successful, “the robot had to grasp the object, lift it up by about 1 ft, andhold it for 30 seconds.”12 )Videos of STAIR performing various tasks, such as opening a door,
fetching a stapler from inside an office, and unloading a dishwasher can beseen from the team’s multimedia Web page at
http://stair.stanford.edu/multimedia.php.
Ng and his colleagues and students envision robots that would be able toperform tasks such as the following:• fetch or deliver items around the home or office,• tidy up a room, including picking up and throwing away trash and using
the dishwasher,• prepare meals using a normal kitchen, and• use tools to assemble a bookshelf.
Ultimately, they say, robots will “revolutionize home and office automationand [will] have important applications ranging from home assistants to elderlycare.”
Ng is not alone in pursuing this kind of robotics research. A teamconsisting of researchers at Intel Research in Pittsburgh and at the Robotics
Institute at Carnegie Mellon University are developing a robot called HERB,
an acronym for Home Exploring Robotic Butler. (See Fig. 35.4.) It consists ofa laser range finder, Segway RMP mobile base, WAM arm, Barrett Hand, andtwo video cameras. According to a paper describing HERB, it can search for
objects, learn to navigate in cluttered dynamic indoor environments, recognize
objects using vision, and manipulate doors and other constrained objects.13 A
video showing HERB performing tasks is available at
http://pittsburgh.intel-research.net/∼ssrin10/HERB09/HERB.wmv.
Another example is DOMO (Fig. 35.4.), a behavior-based,
multiarticulator robot developed by MIT Ph.D. student Aaron Edsinger foraccomplishing “useful manipulation tasks in human environments.”14
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35.1

In the Labs

Yaskawa Electric Corporation in Japan has developed a service robot theycall SMARTPAL V for work around the home and office.15 (See Fig. 35.4.)
Other Japanese companies are developing home robots also. Helping thistrend toward general-purpose robots are competitions sponsored by AAAI,IJCAI, and other groups.16

Figure 35.4: HERB (top left), DOMO (top right), and SMARTPAL V (bottom).
(HERB photograph used with permission of Siddhartha Srinivasa; DOMO photograph used with permission of Aaron Edsinger; SmartPal photograph usedwith permission of Yaskawa Electric.)
In previous chapters I described more specialized robot systems designed
for specific tasks, such as soccer-playing and autonomous automobile-driving.
Even these, however, are integrated systems that advance AI perception,
641
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35

The Quest Continues

learning, planning, and plan-execution techniques that will be broadly useful.
B. Intelligent Assistants
Now let’s move away from robots to consider disembodied “agents” that help
people in ways that do not require mobility. Instead they help with databases,
communication, Internet access, and task performance. I’ll mention a couple ofprojects that are representative of those seeking to develop such agents.17
DARPA’s “PAL Program” has been a source of support for some of thiswork. PAL is an acronym for Personalized Assistant that Learns. According
to the program’s Web site,18
The mission of the PAL program is to radically improve the way
computers support humans by enabling systems that are cognitive,i.e., computer systems that can reason, learn from experience, betold what to do, explain what they are doing, reflect on theirexperience, and respond robustly to surprise.
...
This is the first broad-based research program in cognitive systems
since the Strategic Computing Initiative funded by DARPA in the
1980s. Since then, there have been significant developments in thetechnologies needed to enable cognitive systems, such as machinelearning, reasoning, perception, and, multimodal interaction.
Improvements in processors, memory, sensors and networking havealso dramatically changed the context of cognitive systems
research. It is now time to encourage the various areas to cometogether again by focusing on by [sic] a common application
problem: a Personalized Assistant that Learns.
One of the systems being developed under the general umbrella of the
PAL Program is CALO, an acronym for Cognitive Assistant that Learns andOrganizes.19 CALO is being managed by SRI International and includes overthirty participants from American universities and companies. The project
brings together experts in machine learning, natural language processing,knowledge representation, human–computer interaction, flexible planning, andbehavioral studies. The CALO software learns by interacting with and being
advised by its users and is meant to help users with military decision-making
tasks. (The name, CALO, was inspired by the Latin word “calonis,” whichmeans “soldier’s servant.”)
The ways in which CALO is supposed to learn and help are depicted inFig. 35.5.
Two of the components worked on under the CALO project are thefollowing:
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In the Labs

Figure 35.5: CALO’s functions. (Used with permission of Karen Myers andThomas Garvey.)• Organize and Prepare Information (OPI) organizes user’s information to
improve decision making and situational awareness. (A video about OPI
is available at
http://caloproject.sri.com/videos/OrganizeInformation.mov.)• Project Execution Assistant (PEXA) performs delegated tasks,
anticipates needs and opportunities, and manages user’s time andcommitments.20 (A video is available at
http://caloproject.sri.com/videos/TaskMgtMov2.mov.)Many of the individual components of the overall CALO system represent
important contributions to intelligent systems research generally. One such is
the Marginal Probability Architecture developed by Professor Thomas
Dietterich and Xinlong Bao at Oregon State University.21 It uses “Markov
Logic,”22 a representation combining first-order logic and probabilistic
graphical models, to integrate multiple learning components into the CALO
system.
Another component of CALO is its user interface and knowledge
repository, called IRIS (an acronym for Integrate. Relate. Infer. Share). A
version of IRIS has been released as an open source application. According to
its Web site,23 “IRIS is a semantic desktop application framework that enables
users to create a ‘personal map’ across their office-related information objects.
IRIS includes a machine-learning platform to help automate this process. It
provides ‘dashboard’ views, contextual navigation, and relationship-based
structure across an extensible suite of office applications, including a calendar,
Web and file browser, e-mail client, and instant messaging client.” You candownload this version of IRIS from the Web site.
643
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35

The Quest Continues

Now let us move across the Atlantic where another intelligent assistantproject is ongoing. Yorick Wilks (1939– ), a professor in the Computer Science
Department at the University of Sheffield in the United Kingdom, is thecoordinator of a project called “COMPANIONS.” It is being sponsored by theEuropean Commission and includes participants from Europe and the UnitedStates. According to its Web site,24 the project
aims to change the way we think about the relationships of people
to computers and the Internet by developing a virtual
conversational “Companion.”
This will be an agent or “presence” that stays with the user forlong periods of time, developing a relationship and “knowing” itsowner’s preferences and wishes. It will communicate with the user
primarily by using and understanding speech, but also using othertechnologies such as touch screens and sensors.
Video demonstrations of two prototype systems can be seen on YouTube.
One COMPANION engages in a conversation with a user about “Health andFitness.” The other one shows photographs to a senior from her digital album
and discusses them with her. (See
http://www.youtube.com/watch?v=KQSiigSEYhU andhttp://www.youtube.com/watch?v=s33 UcGyFSE.)
Versatile conversational agents may well serve a useful role for somepeople as occasional surrogates for human interaction. But one should bear inmind warnings, such as those voiced by Theodore Roszak and others, about
their misuse and overuse. Aaron Sloman of the University of Birmingham’s
School of Computer Science in the United Kingdom has written a thoughtful
position paper about some of the difficulties of building digital companions.25
C. Learning by Reading
All AI researchers agree that both common-sense knowledge and specialized
knowledge is the key to intelligence. Various approaches have been pursued togather and organize that knowledge in a form exploitable by computer
programs. Projects like Cyc attempt to do so by hand-coding millions of small
pieces of knowledge as logical sentences. Machine learning research has shownthat statistical methods can be used to “mine” large databases for knowledge.
A third approach is to build programs that can read (and understand)natural language text. After all, as the proponents of that approach claim, theworld is full of knowledge – in books, in news feeds, and on the Web. (Ofcourse, it is full of a lot of nonsense also, but smart programs may ultimately
be able to highlight the trustworthy parts.) As we have already seen,
computer understanding of natural language text requires both generalcommon-sense knowledge and background knowledge about the subject matter
644
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35.2

In the Labsof the text. Thus, “Learning by Reading” (LbR), as this third approach iscalled, draws on the technologies of knowledge representation and reasoning aswell as on natural language processing.
One of the earliest attempts to extract knowledge from the Web was by agroup led by Professor Tom M. Mitchell (1951– ) at Carnegie Mellon
University. In a 1999 paper they proposed “to automatically create a computerunderstandable knowledge base whose content mirrors that of the World Wide
Web.”26 Subsequently, DARPA funded a two-year study called “ProjectMöbius” to determine the feasibility of learning by reading. The project final
report, describing the construction and evaluation of a prototype LbR system,
concluded that “Learning by Reading produced statistically significant
improvements in the problem solving abilities [i.e., question answering] of thetarget knowledge, and that, with a major research effort, substantial progress
could be made in the general application of LbR.”27 At the time of thiswriting, and based on the conclusions of the Project Möbius report, it appears
that DARPA will soon be supporting more LbR research.28
Several researchers are now involved in working on learning by reading
and related problems. (A sampling of work being done in this field can beobtained by looking at the schedule of talks given at a March 2007 AAAI
Spring Symposium available online at
http://www.cs.washington.edu/homes/pjallen/aaaiss07/schedule.htm.) One
group is led by Professor Oren Etzioni (1964– ), Director of the University ofWashington’s Turing Center.29 The Center’s “KnowItAll Project”30 has
developed a search program called “TextRunner.” Among other things, it
attempts to extract logical relations from text so that they can be used topopulate (or add to) a computer-accessible knowledge base. In one of Etzioni’s
examples, TextRunner extracts the list “(Ebay, Founded-by,
Pierre-Omidyar)” from the sentence “EBay was originally founded by Pierre
Omidyar.” This list is a way of writing a logical relation having the predicate
Founded-by and arguments Ebay and Pierre-Omidyar. As I have alreadymentioned, sets of logical relations are used in most of the schemes forrepresenting declarative knowledge. According to its Web page,31 “TextRunnersearches hundreds of millions of assertions extracted from 500 million
high-quality Web pages.” The TextRunner Web page includes a facility that lets
you query its database of assertions to produce answers along with their Web
page sources.32

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The Quest Continues

35.2

Toward Human-Level Artificial
Intelligence

35.2.1

Eye on the Prize

In a 1995 article titled “Eye on the Prize” I argued that AI researchers ought
to be putting more effort into developing generally intelligent systems in
addition to their work on the kinds of smart tools I mentioned in the previous
chapter. I suggested that AI ought to focus on building what I called “habile”
systems – ones that could learn to use smart tools, just as humans are able tolearn to use them. More generally, it has always seemed to me that AI’s grandgoal – the “prize” we are questing for – should be to develop artifacts that can
do most of the things that humans can do – specifically those things that arethought to require “intelligence.” These systems would have what some AI
researchers have called “Human Level Artificial Intelligence” (HLAI).
HLAI was the goal of the founders and of many other early AI
researchers. John McCarthy claims that the “first scientific discussion ofhuman level machine intelligence was apparently by Alan Turing” in his
lecture to the London Mathematical Society on Febuary 20, 1947.33 Turing
made the mechanization of human-level intelligence an explicit goal in his 1950
“Computing Machinery and Intelligence” paper. Later, in the proposal for the1956 Dartmouth Summer Study, John McCarthy wrote “The study is toproceed on the basis of the conjecture that every aspect of learning or any
other feature of intelligence can in principle be so precisely described that amachine can be made to simulate it.”Newell and Simon’s “General Problem Solver” (GPS) was aimed
specifically at HLAI, and they continued to work on the problems both ofunderstanding and mechanizing intelligent behavior. In a 1957 talk, Simon
said that “that there are now in the world machines that think, that learn andthat create. Moreover, their ability to do these things is going to increase
rapidly until – in a visible future – the range of problems they can handle will
be coextensive with the range to which the human mind has been applied.” In
his 1961 paper “Steps Toward Artificial Intelligence” Marvin Minsky wrote“We are on the threshold of an era that will be strongly influenced, and quitepossibly dominated, by intelligent problem-solving machines.” In a 2003 paper,
Edward Feigenbaum concluded “Computational Intelligence is the manifest
destiny of computer science, the goal, the destination, the final frontier. Morethan any other field of science, our computer science concepts and methods arecentral to the quest to unravel and understand one of the grandest mysteriesof our existence, the nature of intelligence. Generations of computer scientists
to come must be inspired by the challenges and grand challenges of this greatquest.”34
Some people have pointed out that HLAI necessarily implies

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35.2

Toward Human-Level Artificial Intelligencesuperhuman-level intelligence. Back in 1965 the English statistician (andco-worker of Alan Turing) I. J. Good wrote35
Let an ultraintelligent machine be defined as a machine that can
far surpass all the intellectual activities of any man however clever.
Since the design of machines is one of these intellectual activities,
an ultraintelligent machine could design even better machines;
there would then unquestionably be an “intelligence explosion,”and the intelligence of man would be left far behind. Thus the firstultraintelligent machine is the last invention that man need ever
make, provided that the machine is docile enough to tell us how tokeep it under control.
In 1987 Jack Schwartz, the sometime critic of AI, wrote36
If artificial intelligences can be created at all, there is little reason
to believe that initial successes could not lead swiftly to theconstruction of artificial superintelligences able to explore
significant mathematical, scientific, or engineering alternatives at a
rate far exceeding human ability, or to generate plans and takeaction on them with equally overwhelming speed. Since man’s
near-monopoly of all higher forms of intelligence has been one of
the most basic facts of human existence throughout the pasthistory of this planet, such developments would clearly create a
new economics, a new sociology, and a new history.
The idea of machines becoming more and more intelligent inspired Vernor
Vinge (1944– ), a mathematician, computer scientist, and science fiction
writer, to predict that a computer superintelligence would emerge by 2030. He
called this event a “singularity,” that is, a point in time when the rate oftechnological progress becomes unimaginably rapid. In an essay about the
singularity, he wrote “When greater-than-human intelligence drives progress,
that progress will be much more rapid. In fact, there seems no reason why
progress itself would not involve the creation of still more intelligent entities –on a still-shorter time scale.”37 He foresaw the day when “Large computer
networks (and their associated users) may ‘wake up’ as a superhumanly
intelligent entity.”
The inventor and AI researcher Ray Kurzweil (1948– ) has popularized
the idea of the singularity in his book The Singularity is Near.38 Based largelyon the idea that the rate of technological progress increases exponentially (andwill continue to do so), Kurzweil makes a number of predictions about what
technology will produce in various decades starting with 2010 and leading up
to “The Singularity” in 2045 and beyond. Some of his predictions seem rather
outlandish, and the whole idea of a singularity has provoked much derision,
but I’ll leave it to you to judge for yourself. (Here is one sample prediction:
647
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The Quest Continues

By 2045, “machines enter into a ‘runaway reaction’ of self-improvement cycles,with each new generation of A.I.s appearing faster and faster. From this point
onwards, technological advancement is explosive, under the control of themachines, and thus cannot be accurately predicted.”) The Web page
http://singularity.com/themovie/ says that a movie version of “TheSingularity is Near” (featuring Kurzweil as well as other futurists andcomputer scientists) will appear in late 2009. Several commentators about the
singularity have written articles for the June 2008 special issue of IEEE
Spectrum. Its Web site has pointers to the articles and to auxiliary material.39
In 2004, The Singularity Institute for Artificial Intelligence (SIAI)40 wasformed “to confront this urgent challenge, both the opportunity and the risk.”
Its Director of Research, Ben Goertzel, is also chair of an organization called
the “Artificial General Intelligence Research Institute” (AGIRI) whose“mission is to foster the creation of powerful and ethically positive Artificial
General Intelligence.”41 AGIRI sponsors conferences and workshops andmanages some open source projects. AGIRI uses the term “artificial general
intelligence” (AGI) somewhat in the same sense that I have been using HLAI.
According to one of its Web sites, “the term is used to stress the ‘general’nature of the desired capabilities of the systems being researched – ascompared to the bulk of mainstream Artificial Intelligence (AI) work, which
focuses on systems with very specialized ‘intelligent’ capabilities.”
I’ll conclude this chapter and the book by talking about HLAI, what it
might be, some arguments for and against it, the possible consequences of it,
and proposed methods for achieving it.

35.2.2Controversies

HLAI (and beyond) is still the goal of many AI researchers even though we
may still be a long way from achieving it – whatever it is. In fact, there iscontroversy about just what HLAI might be. Will we have achieved it when we
have programs that can pass various tests, such as the Turing test? Pat Hayes
and Ken Ford of the Institute for Human and Machine Cognition in Pensacola,
Florida, are among those who argue, on various practical and methodologicalgrounds, against using the Turing test as a measure of AI’s progress.42
Other tests have been proposed that could be taken as helping to define
HLAI. For example, in a 2005 paper, I suggested one, which I called the“employment test.” In that paper, I wrote that to pass the test “programsmust be able to perform the jobs ordinarily performed by humans. Progress
toward human-level AI could then be measured by the fraction of these jobs
that can be acceptably performed by machines.” I had in mind all kinds ofjobs that humans get paid to perform – from skilled and unskilled labor to
managerial and office work.43 For me, achieving HLAI implies (at least)
knowing how to build artifacts that can do what we now pay humans to do.

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35.2

Toward Human-Level Artificial Intelligence

Hayes and Ford reject the very idea of HLAI as a goal for AI research.44
For one thing, they point to the fact that AI programs can already outperform
humans in many areas. So, achieving HLAI may in some ways be too modest
a goal. Furthermore, just as airplanes fly without mimicking birds, AI need
not attempt to mimic humans. Observing that “there is no shortage ofhumans, and we already have well-proven ways of making more of them,” they
conclude that “human cognition is not in short supply” – implying, I suppose,that there is no need to automate the full range of human cognitive abilities.
Yes, even though we already have AI systems that can do some things
better than humans can do them, there are still many, many human cognitive
skills that we don’t yet know how to automate. I believe there are two reasons
why AI researchers will continue to strive to automate these skills. One is
economic: The prospect of someday being able to employ AI systems rather
than more expensive humans (no matter how many humans there might be)
will sustain a strong and irresistible urge to build artifacts that can do what
we now pay humans to do. The other reason is scientific: In their attempts tounderstand how the human brain works, people will continue to build
computational models of its many functions. Thus, I think achieving someversion of HLAI will continue to be AI’s long-term goal.
Even though HLAI may be hard to define, there is money riding on aprediction that we’ll achieve something like it. In 2002 Mitchell Kapor bet$20,000 that “By 2029 no computer – or ‘machine intelligence’ – will havepassed the Turing Test.” Kapor is the designer of Lotus 1-2-3, the founder of
Lotus Development Corporation, and the co-founder of the Electronic Frontier
Foundation. The Long Now Foundation posted this bet on its “Long Bets”
Web page at http://www.longbets.org/. Ray Kurzweil accepted it. Both
Kapor and Kurzweil gave arguments for their positions, and these along withdetailed terms of the bet can be found at http://www.longbets.org/1#terms.

35.2.3

How Do We Get It?

Assuming that HLAI remains one of AI’s goals, how do we achieve it? Can thetechnical tools that have been developed so far be utilized and combined in theright way to produce human-level intelligence? Will continuing research onmachine learning, neural networks, graphical models, simulated evolution,knowledge representation, reasoning, heuristic search, natural language
processing, behavioral mechanisms, and perception (especially vision) leadinexorably toward the goal? Or is something completely different needed as
well?
John McCarthy mentions two approaches toward achieving HLAI. One is
to attempt to simulate how the human intellect works, but, as he wrote,“Understanding the human brain well enough to imitate its
function. . . requires theoretical and experimental success in psychology andneurophysiology.” The other is to write programs that mimic human649
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35

The Quest Continues

intelligent behavior, which is what AI researchers have largely been trying to
do. McCarthy says that “It isn’t a question of deciding between them, because
each should eventually succeed; it is more a race.”45
But should the racers aim immediately for the goal or pursue it in stages?
I think the latter. In his 1961 paper “Steps Toward Artificial Intelligence,”
Marvin Minsky presciently wrote “It is my conviction that no scheme forlearning, or for pattern-recognition, can have very general utility unless there
are provisions for recursive, or at least hierarchical use of previous results.” Hemight well have included, besides learning and pattern recognition, otheraspects of intelligence as well. He went on to say46
We cannot expect a learning system to come to handle very hard
problems without preparing it with a reasonably graded sequenceof problems of growing difficulty. The first problem must be onethat can be solved in reasonable time with the initial resources.
The next must be capable of solution in reasonable time by using
reasonably simple and accessible combinations of methods
developed in the first, and so on. The only alternatives to this useof an adequate “training sequence” are 1) advanced resources,
given initially, or 2) the fantastic exploratory processes foundperhaps only in the history of organic evolution.
I think Minsky was exactly right. We’ve been trying the alternatives of“advanced resources, given initially,” and simulating “organic evolution.”
These approaches have produced smart tools and other useful programs but
not HLAI yet. What about working on a “graded sequence of problems of
growing difficulty”? This strategy has been suggested and deserves serious
consideration.
In his 1950 paper, Alan Turing suggested that “Instead of trying to
produce a programme to simulate the adult mind, why not rather try to
produce one which simulates the child’s? If this were then subjected to an
appropriate course of education one would obtain the adult brain. . . .” The“appropriate course of education” would then correspond to Minsky’s “gradedsequence of problems.”
The staged approach is also reflected in a list of AI capabilities thatRodney Brooks would like to see implemented. He suggested they might havebeen “the foundation for the emergence, through an evolutionary process, ofhigher levels of intelligence in human beings.” Here is his list:47• the object-recognition capabilities of a 2-year-old child,• the language capabilities of a 4-year-old child,• the manual dexterity of a 6-year-old child, and650
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35.2

Toward Human-Level Artificial Intelligence• the social understanding of an 8-year-old child.
Brooks points out that computer systems don’t yet have thesecapabilities. I believe that working toward them would constitute important,and perhaps necessary, steps toward HLAI. With them, AI could implement
Turing’s strategy of educating machines in the same way as we educate people.
But achieving these abilities will be very hard.
I think that Brooks had in mind achieving them by attempting to mimic
human behavior – in this case the behavior of human children – the second of
the approaches that McCarthy suggested.
As regards the other approach, namely, “understanding the human brain
well enough to imitate its function,” there has been work on that problem too.
Several computer scientists are attempting to use concepts familiar to AI
people to explain the brain. I have already mentioned the hierarchical modelsof the cortex proposed by Mumford, Hinton, Hawkins, Dean, and their various
colleagues. Building on features of primitive sensory inputs from an array of
pixels, for example, and ascending through ever-more-abstract percepts, thesemodels are able to learn to classify images independently of size, translation,and orientation. Yet, to my knowledge, no work has yet been done to use thesemodels for more than perception. Can they learn to understand, to reason, toplan, and to select actions? Looked at from the point of view of my“triple-tower architecture” (see p. 567), they have tackled the problem of how
the brain might implement the perception and model towers but they have not
yet tried to do anything with the action tower. Could one of these corticalmodels control a robot, for example?
Dharmendra Modha (1969– ), manager of cognitive computing at IBM’s
Almaden Research Center in San Jose, California,48 is among those pursuing amore “bottom-up” approach. In 2007, Modha’s team carried out an historicexperiment in which they constructed a computer simulation of a rat-scalemodel of the cortex (with 55 million spiking neurons and 448 billion synapses
with spike-timing-dependent plasticity) that could function in near real-timeusing a BlueGene supercomputer with 32,768 processors and 8 TB of main
memory. Modha is the principal investigator of a DARPA project to develop“Systems of Neuromorphic Adaptive Plastic Scalable Electronics”(SyNAPSE), or, in plain English, a project whose goal is to build a machinethat mimics the actions of about 100 million neurons. That’s twice thenumber of neurons in a rat brain but only about 0.25% of the number in ahuman brain. One can hope that the top-down and bottom-up approaches willmeet in the middle somewhere.
Many of the laboratory efforts I mentioned earlier in this chapter are what
I would call “HLAI-friendly”; that is, they are likely to develop the technologythat will be needed by HLAI systems. One of them, the STAIR project, isworking directly toward a challenge problem I posed back in 1996, namely,49

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35

The Quest Continues. . . to produce a robot factotum and errand-runner for a typical
office building – an office building that is not specially equipped toaccommodate robots. . . . The robot must be able to perform (orlearn to perform with instruction and training – but without
explicit post-factory computer programming) any task that ahuman might “reasonably” expect to be able to perform given its[the robot’s] effector/sensor suite.
...
The second part of the challenge is that the robot must stayon-the-job and functioning for a year without being sent back to
the factory for reprogramming.
...
I do not think that it will be feasible for the robot’s builders tosend it to its office building with a suite of programs that
anticipate all of the tasks that could be given. I think the robot
will need to be able to plan and to learn how to perform sometasks that the building occupants (who know only about its sensors
and effectors) might expect it to be able to perform but that its
programmers did not happen to anticipate.

Independently of the various concerns about the appropriateness of (andeven the definition of) HLAI as a goal, I think we’ll indeed achieve it. I won’t
predict when except that it will probably be sometime in this century. But
what if we do? That’s a topic I turn to next.

35.2.4

Some Possible Consequences of HLAI

Suppose that someday superintelligent machines become part of our society, tohelp us, to entertain us, and to do much of our work for us. They are likely totake various forms – humanoid and other varieties of robots, “presences” on
the World Wide Web, software on our home and laptop computers, and,possibly, special implants to aid our own intellectual functioning. Will we by
then have constructed a social order that will preclude these machines fighting
us and each other? Can we even define what it would mean for them to serve
only socially acceptable goals?
These are concerns that have engaged both computer scientists andhumanists. In 1987 Jack Schwartz wrote that many humanist thinkers. . . express the amorphous unease of a much broader public. The
fear is that the whole fabric of human society, which at times
seems terrifyingly fragile, may be torn apart by enormously rapid
technological changes set in motion by AI research as it begins toyield its major fruits. For example, it is possible to imagine that
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35.2

Toward Human-Level Artificial Intelligencewould-be dictators, small centrally placed oligarchies, or predatory
nations could exploit this technology to establish a power oversociety resting on robot armies and police forces independent ofextensive human participation and entirely indifferent to all
traditional human or humane considerations. Even setting thisnightmare aside, one can fear a variety of more subtle deleterious
impacts, for example, rapid collapse of human society into aself-destructive pure hedonism once all pressures, and perhaps evenreasons or opportunities, for work and striving are undermined by
the presence of unchallengeably omnicompetent mechanisms.
Certainly man’s remaining sense of his own uniqueness may befurther impaired, and he may come to seem in his own eyes little
more than a primitive animal, capable only of fleeting
enjoyments.50

To confront these fears, Stephen M. Omohundro (1959– ), an AI
researcher, founded Self-Aware Systems, an organization “devoted to bringing
wisdom into emerging technologies.”51 He thinks “we must be very careful”
about developing AI systems. That’s because they will have, by design,
various goals and drives. Among these are the goals to be self-improving andrational. They will attempt to accomplish these goals and the goals given tothem by humans in the most effective manner possible. To be maximallyeffective they will have drives to preserve themselves and to acquire resources.
These characteristics remind us of HAL 9000, the robot on the spaceship in
the book and movie 2001: A Space Odyssey. Omohundro wants to make surethat we build “wisdom,” and not just intelligence, into our technologies. By
that he means building in “human values, such as caring about human rights
and property rights and having compassion for other entities.” He thinks it“absolutely critical that we build these in at the beginning, otherwise we’ll get
systems that are very powerful, but which do not support our values.”52 I
think Omohundro brings up valid concerns, but to put his version of wisdom
into AI systems we’ll first have to agree on just what we mean by “humanvalues.” That will be tough given that our different opinions about values
often lead to wars.
After many years working on mobile robotics, Professor Ronald Arkin(1949– ) of the Georgia Institute of Technology has devoted attention to the
problem of ethical issues surrounding the use of military robots. His book
Governing Lethal Behavior in Autonomous Robots explores how to program an“artificial conscience” in robots.53 He maintains that such robots mightbehave more ethically in the battlefield than humans currently can. Of course
many people believe that even being on a battlefield is unethical – for humans
or robots.
The possibility of HLAI brings up many other interesting questions. Will
they have “rights”? Can they own property? Could they be participants incivil or criminal judicial proceedings? Would they be able to create literature,653
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The Quest Continues

music, and art? Will they have emotions or be capable of feeling pain or joy?
Will they be conscious? Would humans become emotionally attached to someversions of them (and vice versa)? These are all fascinating questions, andmany people have written about them.
Whether or not society accords intelligent artifacts rights and other legal
powers will be up to us humans. After all, we have decided that several
nonhumans (such as corporations) can have certain rights and obligations, andsome humans (such as children) will not have certain rights. We’ll have to
make similar decisions about intelligent artifacts.
What about creativity? Here’s an example. Professor emeritus David
Cope at the University of California at Santa Cruz has developed a set ofprograms he calls “Experiments in Musical Intelligence.”54 These programs
analyze the style of a musical composer and then use special “recombination”
procedures to create entirely new compositions in that same style. Cope has
used his software to produce works in the style of hundreds of composers.
From one of Cope’s Web sites you can download any of 5,000 MIDI files ofdifferent computer-created Bach-style chorales.55 For those more inclined tolisten to ragtime (in the style of Scott Joplin) check out
ftp://arts.ucsc.edu/pub/cope/joplin.mp3. Cope argues that “recombinancy
appears everywhere as a natural evolutionary and creative process.”56 Someversion of it might well be the basis of all creativity, whether in literature, inart, or in music.
Marvin Minsky, drawing on his many years of research in artificial
intelligence, has written an excellent book about emotions and other mental
phenomena.57 In its Introduction, Minsky claims that “Each of our major‘emotional states’ results from turning certain [parts of the brain] on whileturning certain others off – and thus changing some ways that our brains
behave.” The book describes what some of these brain parts, which he calls“resources,” do and how overlapping clusters of them get turned on and off –resulting not only in various emotional states but in “the processes that wecall ‘thinking.’”
Minsky’s book has a chapter about consciousness. He argues that
consciousness “is a suitcase word, which we each fill up with far more stuff
than could possibly have just one common cause.” He argues that being“conscious” of something involves dozens of mental activities; such collections
are different in different circumstances. Furthermore, neuroscience does not
yet have a proper scientific view of just how all of these mental activities
actually work. Minsky agrees with the philosopher Aaron Sloman, whom hequotes as writing58
The whole idea [of consciousness] is based on a fundamental
misconception that just because there is a noun “consciousness”
there is some “thing” like magnetism or electricity or pressure ortemperature, and that it’s worth looking for correlates of that654
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35.2

Toward Human-Level Artificial Intelligencething.
...
There will not be one thing to be correlated but a very large
collection of very different things.

Someday, I believe, AI researchers aided by (and aiding) neuroscience, willknow enough about how brain “resources” work that they will be able to build
artifacts that will convincingly argue that they are conscious. When that day
comes I would have to assent to their claim, just as I assent to yours.
However, we have not done it yet, so the argument remains open.
Computer scientist and Yale professor David Gelernter (1955– ) argues that“. . . it is hugely unlikely, though not impossible, that a conscious mind will
ever be built out of software.” Nevertheless, he thinks that “an unconscious
simulated intelligence certainly could be built out of software – and might beuseful.” [Might be?!] “Unfortunately, AI, cognitive science, and philosophy ofmind are nowhere near knowing how to build one.” He thinks that AI needs torefocus its efforts toward what he calls “the mechanisms (or algorithms) ofthought. . . ” Until that time, he laments “AI is lost in the woods.”59
Lost? I don’t think so. Do we have long way to go? Probably, but wewon’t know until we get there. And until we do, we’ll hear various calls to“refocus” both from within and from outside the field. In any case, we should
continue to pursue many different approaches, guided by our best judgments.
It’s like heuristic search for a goal that we’ll recognize when we achieve it.
As we work toward that goal, some of the consequences of HLAI will
emerge gradually. For one thing, we’ll become more and more dependent on
smart machines. Just as we have become dependent on the automobile andother inventions of the past century and a half, it is already the case that oursociety depends in many ways on the Internet, sophisticated trading andauction programs, spreadsheet programs, weather forecasting models, and ahost of other computer-related technologies. Furthermore, unlike as is the casewith the automobile and the radio, fewer and fewer people understand these
new technologies, putting us already in the precarious position of having totrust them.
As more and more “jobs” are performed by inexpensive
hardware–software combinations, people who used to get paid for those jobs
will have to find others or risk being jobless. That does not mean that the
total gross domestic product will shrink; in fact it will probably rise. Society
will need to find ways to let its members enjoy a just share of the wealth thatmachines create. In a thoughtful article60 about these matters, the economist
Robin Hanson likens the effects of machines substituting for human workers to
an inexorably rising sea level. . .
. . . with the tasks that are “most human” [those in which humans
have an economic advantage over machines] on the higher ground.
655
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The Quest Continues
Here you find chores best done by humans, like gourmet cooking or
elite hairdressing. Then there is a “shore” consisting of tasks thathumans and machines are equally able to perform and, beyond
them an “ocean” of tasks best done by machines. When machines
get cheaper or smarter or both, the water level rises, as it were,
and the shore moves inland.
This sea change has two effects. First, machines will substitute forhumans by taking over newly “flooded” tasks. Second, doingmachine tasks better complements human tasks, raising the valueof doing them well.

Hanson believes that it is possible that the “machine ocean” might ultimately
inundate all of “Task-Land” and that consequently “wages would fall so farthat most humans would not, through their labor alone, be able to live on
them, though they might work for other reasons.” (Of course, automation may
create new and higher peaks in Task-Land, slowing the effects of the risingocean.) Nevertheless, he imagines that any small part of the greatly expanded
wealth created by the machines should “allow humans to live comfortably. . . .”35.3

Summing Up

On that optimistic note, I come to the end of my story about the quest for
artificial intelligence – a quest that is not yet complete. AI has explored avariety of paths, and in doing so it has achieved several successes and has
assembled many powerful computational tools. One way to summarize theideas and achievements I have talked about is to divide them into four maincategories, namely, complete AI systems (ones that do things), architectures(organizational principles for AI systems), processes (routines that actually do
the work), and representations (structures that are created, modified, andaccessed by processes). Without trying to be complete, I’ll mention some ofwhat I think have been AI’s major accomplishments in each of these categories.
• Complete AI systems: LT, Heuristic DENDRAL, Shakey, expert systems(such as MYCIN and PROSPECTOR), MSYS, speech recognition systems(such as HARPY, DRAGON, and HEARSAY II), Genghis, driverlessautomobiles (the whole class of them), Deep Blue, and othergame-playing machines, RAX, and CALO.
• Architectures: Pandemonium, production systems, three-levelarchitectures, Blackboard architectures, BDI architectures (such as PRS),
behavior-based architectures, SOAR, ACT-R, and cortical models.
• Processes: edge- and region-finding filters (including Laplacian ofGaussian), spreading activation, parsing, resolution, A∗ and its progeny,
beam search, the Rete algorithm, STRIPS and other planning systems,
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NOTES
case-based reasoning, clustering (such as k-means and AutoClass),
constraint propagation, Bayes’s rule, genetic algorithms and genetic
programming, GSAT and DPLL-based methods, knowledge sources (suchas those used in blackboards and for computing intrinsic images),
backprop, circumscription, latent semantic analysis, Q-learning andprioritized sweeping, particle filtering, kernel computations (for support
vector machines), and expectation maximization (EM).

• Representations: state (problem) spaces, vectors, logical expressions(including IF–THEN rules), semantic networks, programs (as data
structures), blackboards, graphical models (including Bayesian networks
and HMMs), grammars, neural networks, decision trees, scripts, frames,
and augmented transition networks.
[The processes and representations they work on are usually intimately linked.
For example, GSAT works on sets of propositions (in the form of clauses),
Blackboard knowledge sources respond to and modify Blackboard items, andgenetic programming operates on LISP-program representations.]
Several disciplines have contributed to AI’s successes. As I wrote at the
beginning of this book, the early AI pioneers used many clues about how toproceed – clues from mathematics and logic, from neuroscience, fromlinguistics, from statistics and probability theory, from control engineering,from psychology, and from computer science. Indeed, the substantial progress
made in the quest for AI to date is due to the use of ideas from all of thosedisciplines. No overarching theory of AI has yet emerged, nor is one likely toin my opinion.
The quest will continue. What combinations of AI’s methods, buttressedby AI’s supporting disciplines, will be used in the intelligent systems of the
future? No one really knows, so we’ll have to keep all of them active on AI’s
“search frontier.” Some of the ones developed early in the quest (and now
perhaps forgotten) might with better technology be useful. Researchers whowant to pursue the quest should be familiar with the full variety of AI’s
methods, its contributing disciplines, and (yes) its history.
Future writers will doubtless continue to tell the story of the quest. Oneof them, someday, will be able to report that some two and half millennia after
Aristotle’s musings, we now do have tools that perform our tasks, “either atour bidding or [themselves] perceiving the need. . . ”

Notes
1. See, for example, http://www.iit.edu/∼it/delphi.html. [633]
2. Excerpted from Oscar Firschein et al., “Forecasting and Assessing the Impact ofArtificial Intelligence on Society,” Proceedings of the IJCAI, pp. 105–120, 1973. [633]657
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35

NOTES

3. Good places to start would be sites maintained by the Association for the Advancementof Artificial Intelligence (AAAI), accessible from http://www.aaai.org/home.html; theEuropean Coordinating Committee for Artificial Intelligence (ECCAI), accessible fromhttp://www.eccai.org/; the Japanese Society for Artificial Intelligence (JSAI), accessible
from http://www.ai-gakkai.or.jp/jsai/english.html (English version); the Chinese Artificial
Intelligence Society, accessible from http://caai.cn/; DARPA’s Information Processing
Techniques Office, accessible from http://www.darpa.mil/ipto/; the German Center for
Artificial Intelligence (DFKI), accessible fromhttp://www.dfki.de/web/welcome?set language=en&cl=en; and INRIA, accessible fromhttp://www.inria.fr/index.en. [634]4. See James Philbin et al., “Object Retrieval with Large Vocabularies and Fast Spatial
Matching,” Proceedings of the IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2007. Available online at
http://www.robots.ox.ac.uk/∼vgg/publications/papers/philbin07.pdf. [635]5. See Adrian Ulges et al., “Content-based Video Tagging for Online Video Portals,” Third
MUSCLE/ImageCLEF Workshop on Image and Video Retrieval Evaluation, 2007.
Available online at http://demo.iupr.org/videotagging/youtube.pdf. Also visit
http://demo.iupr.org/videotagging/tagging-description.html to see a demonstration of thesystem. [636]6. http://www.powerset.com/. [637]7. Marc Raibert et al., “BigDog, the Rough-Terrain Quaduped Robot,” Proceedings of the
17th World Congress of The International Federation of Automatic Control, Seoul, Korea,
July 6–11, 2008. Available online at
http://www.nt.ntnu.no/users/skoge/prost/proceedings/ifac2008/data/papers/4278.pdf.
[637]
8. See, for example, http://graphics.cs.cmu.edu/projects/behavior planning/. [637]9. See, for example, http://www.psrg.csail.mit.edu/. [637]
10. See, for example, http://www.dfki.de/pas/f2w.cgi?ltp/musing-e. [637]11. http://stair.stanford.edu/index.php. [638]12. See, Ashutosh Saxena, Justin Driemeyer, and Andrew Y. Ng, “Robotic Grasping ofNovel Objects Using Vision,” International Journal of Robotics Research (IJRR), Vol. 27,
No. 2, pp. 157–173, February 2008. Available online at http://ai.stanford.edu/∼asaxena/
learninggrasp/IJRR saxena etal roboticgraspingofnovelobjects.pdf. [640]
13. Siddhartha Srinvasa et al., “HERB: A Home Exploring Robotic Butler”; available online
at http://pittsburgh.intel-research.net/∼ssrin10/HERB09/HERB09.pdf. [640]
14. See the DOMO Web page at http://people.csail.mit.edu/edsinger/domo.htm. Edsinger’s
2007 Ph.D. dissertation, “Robot Manipulation in Human Environments,” can be
downloaded from that page. [640]
15. See http://www.yaskawa.co.jp/en/newsrelease/2007/04.htm. [641]16. For a list of these see the “Robot Competition FAQ” at http://robots.net/rcfaq.html.
[641]
17. More projects are described in a special issue on “Mixed-Initiative Assistants” of the AI
Magazine, Vol. 28, No. 2, Summer 2007. [642]18. http://www.darpa.mil/ipto/programs/pal/pal.asp. A DARPA brochure about PAL isavailable at http://caloproject.sri.com/PALbrochure.pdf, and a video is available at
http://www.darpa.mil/ipto/programs/pal/docs/PAL.wmv. [642]19. For more information, see http://caloproject.sri.com/. The first three years of the
CALO project ended in earlty 2009. It is anticipated that another three-year project will
follow to transfer the technology to actual applications. [642]658
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35.3

NOTES

20. For a technical description of PEXA, see Karen Myers et al., “An Intelligent Personal
Assistant for Task and Time Management,” AI Magazine, Vol. 28, No. 2, pp. 47–61,
Summer 2007. [643]
21. Thomas G. Dietterich and Xinlong Bao, “Integrating Multiple Learning Components
through Markov Logic,” Proceedings of the Twenty-Third AAAI Conference on Artificial
Intelligence, pp. 622–627, 2008. A preprint is available at
http://web.engr.oregonstate.edu/∼tgd/publications/aaai08-ilr.pdf. [643]22. Matthew Richardson and Pedro Domingos, “Markov Logic Networks,” Machine
Learning, Vol. 62, pp. 107–136, 2006. Available online at
http://www.cs.washington.edu/homes/pedrod/papers/mlj05.pdf. [643]23. http://www.openiris.org/. [643]24. http://www.companions-project.org/. [644]
25. Aaron Sloman, “Requirements for Digital Companions: It’s Harder Than You Think,”
Position Paper for Workshop on Artificial Companions in Society: Perspectives on the
Present and Future, Organised by the Companions Project, Oxford Internet Institute,
October 25–26, 2007. Available online at
http://www.cs.bham.ac.uk/research/projects/cogaff/sloman-oii-2007.pdf. [644]26. Mark Craven et al., “Learning to Construct Knowledge Bases from the World Wide
Web,” Artificial Intelligence, Vol. 118, No. 1, pp. 69–113, April 2000. A preprint is availableat
http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-11/www/wwkb/overview-aij99.ps.gz.
[645]
27. Noah S. Friedland et al., “Project Möbius: A study on the Feasibility of Learning by
Reading”; available online at
http://www.noahfriedland.com/uploads/Moebius Final Report.pdf. [645]28. See, for example, http://www.darpa.mil/ipto/solicit/baa/RFI-08-11.pdf. [645]29. http://turing.cs.washington.edu/index.htm. [645]30. See http://www.cs.washington.edu/research/knowitall/. [645]31. http://www.cs.washington.edu/research/textrunner/. [645]32. For technical details about TextRunner, see Michele Banko and Oren Etzioni, “The
Tradeoffs between Open and Traditional Relation Extraction,” Proceedings of ACL-08:
Human Language Technologies, pp. 28–36, Association for Computational Linguistics, June
2008. Available online at http://www.aclweb.org/anthology-new/P/P08/P08-1004.pdf.
[645]
33. John McCarthy, “From Here to Human-Level AI,” Artificial Intelligence, Vol. 171, No.
18, pp. 1174–1182, December 2007. Preprint available online at
http://www-formal.stanford.edu/jmc/human/human.html. [646]34. Edward A. Feigenbaum, “Some Challenges and Grand Challenges for Computational
Intelligence,” Journal of the ACM, Vol. 50, No. 1, pp. 32–40, January 2003. [646]35. Irving J. Good, “Speculations Concerning the First Ultraintelligent Machine,” in Franz
L. Alt and Morris Rubinoff (eds.), Advances in Computing, Vol. 6, pp. 31–88, 1965. [647]36. Jacob Schwartz, “Limits of Artificial Intelligence,” in Stuart C. Shapiro and David
Eckroth (eds.), Encyclopedia of Artificial Intelligence, Vol. 1, pp. 488–503, New York: John
Wiley and Sons, Inc., 1987. [647]37. Vernor Vinge, “The Coming Technological Singularity: How to Survive in the
Post-Human Era”; available online at
http://www-rohan.sdsu.edu/faculty/vinge/misc/singularity.html. The original version ofthis essay was presented at the VISION-21 Symposium sponsored by NASA Lewis Research659
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35

NOTES

Center and the Ohio Aerospace Institute, March 30–31, 1993. A slightly changed version
appeared in the Winter 1993 issue of Whole Earth Review. That version (with someannotations by the author) is available online at
http://wholeearth.com/ArticleBin/111-3.pdf. [647]38. Ray Kurzweil, The Singularity Is Near: When Humans Transcend Biology, New York:Viking Press, 2005. [647]39. See http://spectrum.ieee.org/singularity. [648]40. See http://singinst.org/. [648]41. http://www.agiri.org/wiki/Artificial General Intelligence Research Institute. [648]42. Patrick Hayes and Kenneth Ford, “Turing Test Considered Harmful,” Proceedings of
IJCAI-95, Vol. 1, pp. 972–997, 1995. Available online at
http://dli.iiit.ac.in/ijcai/IJCAI-95-VOL%201/pdf/125.pdf. [648]43. Nils J. Nilsson, “Human-Level Artificial Intelligence? Be Serious!,” AI Magazine, Vol.
26, No. 4, pp. 68–75, Winter 2005. Available online at
http://ai.stanford.edu/∼nilsson/OnlinePubs-Nils/GeneralEssays/AIMag26-04-HLAI.pdf.
[648]
44. See, for example, Kenneth Ford and Patrick Hayes, “On Computational Wings:
Rethinking the Goals of Artificial Intelligence,” Scientific American Presents, Vol. 9, No. 4,
pp. 78–83, 1998. [649]45. The quotations are from John McCarthy, op. cit. [650]46. Marvin Minsky, “Steps toward Artificial Intelligence,” Proceedings of the IRE, Vol. 49,
No. 1, pp. 8–30, January 1961. Online version available at
http://web.media.mit.edu/∼minsky/papers/steps.html. [650]47. Rodney Brooks, “I, Rodney Brooks, Am a Robot,” IEEE Spectrum, Vol. 45, No. 6, pp.
62–67, June 2008. Available online at http://spectrum.ieee.org/jun08/6307. [650]48. http://www.almaden.ibm.com/cs/people/dmodha/. [651]49. Bart Selman et al., “Challenge Problems for Artificial Intelligence,” Proceedings of
AAAI-96, Thirteenth National Conference on Artificial Intelligence, pp. 1340–1345, Menlo
Park, CA: AAAI Press, 1996. Available online at
ftp://ftp.research.microsoft.com/pub/ejh/selman.ps. [651]50. Jacob Schwartz, op. cit. [653]
51. http://selfawaresystems.com/. [653]52. Stephen M. Omohundro, “Self-Improving AI and the Future of Computation,” lecture
given at Stanford on November 1, 2007; transcript available at
http://selfawaresystems.com/2007/11/01/
standford-computer-systems-colloquium-self-improving-ai-and-the-future-of-computing/.
[653]
53. Ronald C. Arkin, Governing Lethal Behavior in Autonomous Robots, Boca Raton, FL:
CRC Press, 2008. [653]
54. See http://arts.ucsc.edu/faculty/cope/experiments.htm. [654]55. Visit http://arts.ucsc.edu/faculty/cope/5000.html. [654]56. The quotation is from http://arts.ucsc.edu/faculty/cope/experiments.htm. [654]57. Marvin Minsky, The Emotion Machine: Commonsense Thinking, Artificial Intelligence,and the Future of the Human Mind, New York: Simon and Schuster, 2006. A draft isavailable from Minsky’s homepage at http://web.media.mit.edu/∼minsky/. [654]58. For the note from which the quotation comes, see

660
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35.3

NOTES

http://www.cs.bham.ac.uk/∼axs/misc/consciousness/consciousness.june.18.96.txt. [654]59. David Gelernter, “Artificial Intelligence Is Lost in the Woods,” Technology Review,
MIT, July/August 2007; available online at
http://www.technologyreview.com/Infotech/18867/?a=f. [655]60. Robin Hanson, “Economics of the Singularity,” IEEE Spectrum, Vol. 45, No. 6, pp.
45–50, June 2008. Available online at http://spectrum.ieee.org/jun08/6274. [655]661
Copyright c 2010 Nils J. Nilsson
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35

NOTES

662
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35.3

List of Figures
1.1

Ramon Llull (left) and his Ars Magna (right). . . . . . . . . . . .

1.2

Model of a robot knight based on drawings by Leonardo da Vinci. 21

1.3Frédéric Vidoni’s ANAS, inspired by Vaucanson’s duck. (Photograph courtesy of Frédéric Vidoni.) . . . . . . . . . . . . . . . . .

23

1.4

A scene from a New York production of R.U.R.

. . . . . . . . .

24

2.1

Gottfried Leibniz. . . . . . . . . . . . . . . . . . . . . . . . . . . .

29

2.2

The Stanhope Square Demonstrator, 1805. (Photograph courtesyof Science Museum/SSPL.) . . . . . . . . . . . . . . . . . . . . .

30

2.3

George Boole. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

32

2.4

Expressing “All persons are mortal” in Begriffsschrift. . . . . . .

33

2.5

Ramón y Cajal.

. . . . . . . . . . . . . . . . . . . . . . . . . . .

35

2.6

Two neurons. (Adapted from Science, Vol. 316, p. 1416, 8 June
2007. Used with permission.) . . . . . . . . . . . . . . . . . . . .

36

2.7

Warren McCulloch.

. . . . . . . . . . . . . . . . . . . . . . . . .

37

2.8

Networks of McCulloch–Pitts neural elements. (Adapted from
Fig. 1 of Warren S. McCulloch and Walter Pitts, “A Logical
Calculus of Ideas Immanent in Nervous Activity,” Bulletin of
Mathematical Biophysics, Vol. 5, pp. 115–133, 1943.) . . . . . . .

38

B. F. Skinner. (Photograph courtesy of the B. F. Skinner Foundation.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

39

2.10 Noam Chomsky. (Photograph by Don J. Usner.) . . . . . . . . .

41

2.9

20

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35

LIST OF FIGURES
2.11 Grey Walter (top left), his Machina speculatrix (top right), andits circuit diagram (bottom). (Grey Walter photograph from
Hans Moravec, ROBOT, Chapter 2: Caution! Robot Vehicle!, p.
18, Oxford: Oxford University Press, 1998; “Turtle” photographcourtesy of National Museum of American History, Smithsonian
Institution; the circuit diagram is from W. Grey Walter, The
Living Brain, p. 200, London: Gerald Duckworth & Co., Ltd.,
1953.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45

2.12 A verge-and-foliot mechanism (left) and automata at the Munich
Glockenspiel (right). . . . . . . . . . . . . . . . . . . . . . . . . .

47

2.13 Reconstruction of a Jacquard loom. . . . . . . . . . . . . . . . . .

48

2.14 Watt’s flyball governor. . . . . . . . . . . . . . . . . . . . . . . .

50

2.15 W. Ross Ashby, Warren McCulloch, Grey Walter, and Norbert
Wiener at a Meeting in Paris. (From P. de Latil, Thinking by
Machine: A Study of Cybernetics, Boston: Houghton, Mifflin,
1957.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

51

2.16 Wilhelm Schickard (left) and Blaise Pascal (right). . . . . . . . .

54

2.17 Charles Babbage (left) and a model of his Analytical Engine (right). 56
2.18 Alan Mathison Turing. (Photograph by Elliott & Fry c andused with permission of the National Portrait Gallery, London.) .

57

2.19 A Turing machine. . . . . . . . . . . . . . . . . . . . . . . . . . .

58

2.20 Claude Shannon. (Photograph courtesy of MIT Museum.) . . . .

59

2.21 The Cambridge University EDSAC computer (circa 1949). (Photograph used with permission of the Computer Laboratory, University of Cambridge c .) . . . . . . . . . . . . . . . . . . . . . .

60

2.22 Herbert Simon (seated) and Allen Newell (standing). (Courtesyof Carnegie Mellon University Archives.) . . . . . . . . . . . . . .

65

3.1

Oliver Selfridge. (Photograph courtesy of Oliver Selfridge.) . . .

75

3.2

John McCarthy (left) and Marvin Minsky (right). (McCarthy
photograph courtesy of John McCarthy. Minsky photographcourtesy MIT Museum.) . . . . . . . . . . . . . . . . . . . . . .

78

Some of AI’s founders at the July 2006 Dartmouth fiftieth anniversary meeting. From the left are Trenchard More, John McCarthy, Marvin Minsky, Oliver Selfridge, and Ray Solomonoff.
(Photograph courtesy of photographer Joe Mehling and the Dartmouth College Artificial Intelligence Conference: The Next Fifty
Years.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

81

3.3

664
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35.3
4.1

LIST OF FIGURESAn early scanned photograph. (Photograph used with permissionof NIST.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

90

4.2

The MICR font set. . . . . . . . . . . . . . . . . . . . . . . . . .

91

4.3

Frank Rosenblatt (left) working (with Charles Wrightman) on a
prototype A-unit. (Courtesy of the Division of Rare and Manuscript
Collections, Cornell University Library.) . . . . . . . . . . . . . . 93

4.4

Rosenblatt’s neural element with weights. . . . . . . . . . . . . .

93

4.5

A perceptron. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

94

4.6

A separating plane in a three-dimensional space. . . . . . . . . .

95

4.7

MINOS. Note the input switches and corresponding indicatorlights in the second-from-the-left rack of equipment. The magnetic weights are at the top of the third rack. (Photograph usedwith permission of SRI International.) . . . . . . . . . . . . . . .

99

4.8

MINOS II: operator’s display board (left), an individual weightframe (middle), and weight frames with logic circuitry (right).
(Photographs used with permission of SRI International.) . . . . 100

4.9John Munson (left), Peter Hart (middle), and Richard Duda
(right). (Photographs courtesy of Faith Munson, of Peter Hart,and of Richard Duda.) . . . . . . . . . . . . . . . . . . . . . . . . 101

4.10 Recognition of FORTRAN characters. Input is above and output(with only two errors) is below. (Illustration used with permissionof SRI International.) . . . . . . . . . . . . . . . . . . . . . . . . 102
4.11 A two-dimensional space of feature points and a separating boundary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.12 A Philco tank-recognition system. (Adapted from Laveen N.
Kanal and Neal C. Randall, “Target Detection in Aerial Photography,” paper 8.3, Proceedings of the 1964 Western Electronics
Show and Convention (WESCON), Los Angeles, CA, Institute ofRadio Engineers (now IEEE), August 25–28, 1964.) . . . . . . . . 106
4.13 A typical tank image. (Photograph courtesy of Thomas Harley.) 108
5.1

Start (left) and goal (right) configurations of a fifteen-puzzle
problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114

5.2

The eight-puzzle. . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5.3

A search tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

5.4

A triangle with two equal sides (left) and its flipped-over version(right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

5.5

Arthur Samuel. (Photograph courtesy of Donna Hussain, Samuel’s
daughter.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
665

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35

LIST OF FIGURES
5.6

An illustrative checkers game tree. (From p. 74 of Edward A.
Feigenbaum and Julian Feldman (eds.), Computers and Thought,
New York: McGraw Hill, 1963.) . . . . . . . . . . . . . . . . . . . 126

6.1

An analogy problem. . . . . . . . . . . . . . . . . . . . . . . . . . 132

7.1

A parse tree for analyzing a sentence. . . . . . . . . . . . . . . . 144

7.2

A parse tree for generating a sentence. . . . . . . . . . . . . . . . 145

8.1

Site of the Stanford AI Lab from 1966 until 1980. (Photographcourtesy of Lester Earnest.) . . . . . . . . . . . . . . . . . . . . . 158

8.2

Donald Michie. (Photograph courtesy of the Michie Family.) . . 159

8.3

Michie’s MENACE for learning how to play tic-tac-toe. . . . . . . 159

8.4

J. C. R. Licklider. (Photograph by Koby-Antupit from MIT Collection (JCL8).) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

9.1

Two tables. (Illustration courtesy of Michael Bach.) . . . . . . . 170

9.2

Detecting changes in intensity. (Photographs used with permission of Lawrence Roberts.) . . . . . . . . . . . . . . . . . . . . . . 175

9.3

Producing the final line drawing. (Photographs used with permission of Lawrence Roberts.) . . . . . . . . . . . . . . . . . . . . 175

9.4

An array of image intensity values and an averaging window. . . 177

9.5

Sobel’s vertical (left) and horizontal (right) filters. . . . . . . . . 177

9.6

Finding abrupt changes in image brightness with the Sobel Operator. (Photographs taken by George Miller and available at http://en.wikipedia.org/wiki/Sobel operator. Used under the termsof the GNU Free Documentation License.) . . . . . . . . . . . . . 178

9.7

A Laplacian of Gaussian filtering window. . . . . . . . . . . . . . 178

9.8

A Laplacian of Gaussian surface. . . . . . . . . . . . . . . . . . . 179

9.9

An image (left) and its LoG-processed version (right). (Images
taken from David Marr and E. Hildreth, “Theory of Edge Detection,” Proceedings of the Royal Society of London, Series B,
Biological Sciences, Vol. 207, No. 1167, p. 198, February 1980.) . 179

9.10 The final result of a Marr–Hildreth edge-detecting operation.
(From David Marr and E. Hildreth, “Theory of Edge Detection,”
Proceedings of the Royal Society of London, Series B, Biological
Sciences, Vol. 207, No. 1167, p. 198, February 1980.) . . . . . . . 180
9.11 Links established by SEE for a sample scene. (Illustration usedwith permission of Adolpho Guzman.) . . . . . . . . . . . . . . . 182
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LIST OF FIGURES

9.12 A scene analyzed by SEE. (Illustration used with permission of
Adolpho Guzman.) . . . . . . . . . . . . . . . . . . . . . . . . . . 183
9.13 The four different kinds of vertices that can occur in trihedralsolids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
9.14 An impossible object. . . . . . . . . . . . . . . . . . . . . . . . . 184
9.15 A scene with shadows analyzed by Waltz’s program. (Illustration
used with permission of David Waltz.) . . . . . . . . . . . . . . . 185
10.1 A block arrangement for the MIT copy demo. (Used with permission of Berthold Horn.) . . . . . . . . . . . . . . . . . . . . . . 191
10.2 Raj Reddy. (Photograph courtesy of Raj Reddy.) . . . . . . . . . 193
10.3 Diagram of a water pump assembly workspace. (Illustration usedwith permission of Robert Bolles.) . . . . . . . . . . . . . . . . . 194
10.4 Hitachi’s HIVIP robotic assembly system. . . . . . . . . . . . . . 195
10.5 FREDDY II, the University of Edinburgh robot. (Photographcourtesy of University of Edinburgh.) . . . . . . . . . . . . . . . . 196
11.1 Robert Kowalski (left) and Alain Colmerauer (right). (Photographs
courtesy of Robert Kowalski and of Alain Colmerauer.) . . . . . 204
11.2 Roger Schank. (Photograph courtesy of Roger Schank.) . . . . . 206
11.3 Conceptual structure for “John threw the pencil to Sam.” (FromRoger C. Schank, “Identification of Conceptualizations Underlying Natural Langauge,” in Roger Schank and Kenneth Colby(eds.), Computer Models of Thought and Language, p. 226, San
Francisco: W. H. Freeman and Co., 1973.) . . . . . . . . . . . . . 207
11.4 A scene in the restaurant script. (From Roger C. Schank andRobert P. Abelson, Scripts, Plans, Goals, and Understanding:
An Inquiry into Human Knowledge Structures, p. 43, Hillsdale,
NJ: Lawrence Erlbaum Associates, 1977.) . . . . . . . . . . . . . 208
12.1 The Johns Hopkins “Beast.” (Photograph courtesy of the Johns
Hopkins University Applied Physics Laboratory.) . . . . . . . . . 214
12.2 Excerpt from the typescript of the automaton work statement. . 215
12.3 Shakey as it existed in November 1968 (with some of its components labeled). (Photograph courtesy of SRI International.) . . . 217
12.4 Charles A. Rosen with Shakey. (Photograph courtesy of SRI
International.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
12.5 A navigation problem for Shakey. (Illustration used with permission of SRI International.) . . . . . . . . . . . . . . . . . . . . . . 219
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LIST OF FIGURES
12.6 Using vision to locate an object. (From the film Shakey: An Experiment in Robot Planning and Learning. Used with permissionof SRI International.) . . . . . . . . . . . . . . . . . . . . . . . . 226
12.7 Using vision to update position. (From the film Shakey: An Experiment in Robot Planning and Learning. Used with permissionof SRI International.) . . . . . . . . . . . . . . . . . . . . . . . . 227
12.8 The Stanford cart. (Photograph courtesy of Lester Earnest.) . . 232
13.1 Terry Winograd. (Photograph courtesy of Terry Winograd.) . . . 239
13.2 SHRDLU’s world. (Illustration used with permission of Terry
Winograd.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
13.3 A parse tree obtained for the sentence “John was believed tohave been shot.” (From William A. Woods, “Transition Network
Grammars for Natural Language Analysis,” Communications of
the ACM, Vol. 13, No. 10, pp. 591–606, 1970.) . . . . . . . . . . 246
15.1 Edward Feigenbaum (left), Joshua Lederberg (middle), and Bruce
Buchanan (right). (Photographs courtesy of Edward Feigenbaum.)255
15.2 The structure of the propane molecule. . . . . . . . . . . . . . . . 256
15.3 A mass spectrogram. (Illustration used with permission of Edward Feigenbaum.) . . . . . . . . . . . . . . . . . . . . . . . . . . 257
17.1 A speech waveform. (Used with permission of Gunish Rai Chawla.)268
17.2 Consonants and vowels in the ARPAbet phonetic alphabet. . . . 269
17.3 Two hierarchical levels in speech generation. . . . . . . . . . . . . 274
17.4 A partial network of the phones that might occur in a spoken
sentence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
17.5 The Blackboard architecture. . . . . . . . . . . . . . . . . . . . . 279
18.1 Proposed arrangement for helping an apprentice. (Used withpermission of SRI International.) . . . . . . . . . . . . . . . . . . 286
18.2 Part of a procedural net for assembling an air compressor. (Usedwith permission of SRI International.) . . . . . . . . . . . . . . . 288
18.3 Barbara J. Grosz. (Photograph courtesy of photographer Tony
Rinaldo.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
18.4 Bruce Buchanan (left) and Ted Shortliffe (right). (Photographcourtesy of Ed Feigenbaum.) . . . . . . . . . . . . . . . . . . . . 291
18.5 The structure of a MYCIN-style expert system. . . . . . . . . . . . 294

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35.3

LIST OF FIGURES

18.6 A partial geologic taxonomy. (Used with permission of SRI International.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
18.7 Simplified version of a PROSPECTOR inference network. (Usedwith permission of SRI International.) . . . . . . . . . . . . . . . 297
18.8 Some of the Mount Tolman input maps. (Photographs courtesyof Richard Duda.) . . . . . . . . . . . . . . . . . . . . . . . . . . 299
18.9 Favorability map. (Photograph courtesy of Richard Duda.) . . . 300
19.1 George Heilmeier. (Photograph courtesy of DARPA.) . . . . . . 311
19.2 Gary Hendrix. (Photograph courtesy of Gary Hendrix.) . . . . . 314
19.3 Sample interactions with LADDER. (Used with permission of SRI
International.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
19.4 Files used in a TEAM database. (Used with permission of SRI
International.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319
19.5 A parse tree for “Show each continent’s highest peak.” (Used withpermission of SRI International.) . . . . . . . . . . . . . . . . . . 320
19.6 A network structure linking data at different levels. (Illustration from H. Penny Nii, Edward A. Feigenbaum, John J. Anton, and A. J. Rockmore, “Signal-to-Symbol Transformation:
HASP/SIAP Case Study,” AI Magazine, Vol. 3, No. 2, p. 26,
Figure 2, c 1982, Association for the Advancement of Artificial
Intelligence. Used with permission.) . . . . . . . . . . . . . . . . 322
20.1 Berthold Horn (left) and a shaded circle (right). (Photographcourtesy of Berthold Horn.) . . . . . . . . . . . . . . . . . . . . . 328
20.2 Light incident on and reflected by a small piece of a surface.
(Illustration used with permission of Berthold Horn.) . . . . . . . 329
20.3 A 2 12 -D sketch. (From David Marr and H. K. Nishihara, “Representation and Recognition of the Spatial Organization of ThreeDimensional Shapes,” Proceedings of the Royal Society of London, Series B, Biological Sciences, Vol. 200, No. 1140, p. 274,
February 23, 1978.) . . . . . . . . . . . . . . . . . . . . . . . . . . 330
20.4 Jay Martin Tenenbaum (left) and Harry Barrow (right). (Photographs courtesy of J. Martin Tenenbaum and of Harry Barrow.) 331
20.5 Intrinsic images. (Used with permission of Harry Barrow and Jay
M. Tenenbaum.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
20.6 An MSYS scene with some regions detected and labeled. (Illustration used with permission of SRI International.) . . . . . . . . 334

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35

LIST OF FIGURES
20.7 An example of one of Marr’s 3-D model hierarchies. (From David
Marr, Vision, San Francisco: W. H. Freeman and Co., p. 306,
1982.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
20.8 Primitive generalized cones and piston models constructed fromgeneralized cones. (From Rodney A. Brooks, “Symbolic Reasoning among 3-D Models and 2-D Images,” Artificial Intelligence,Vol. 17, Nos. 1–3, pp. 285–348, 1981.) . . . . . . . . . . . . . . . 337
20.9 An illustration of IU goals. (Illustration used with permission of
SAIC.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
21.1 Scene from one of the AAAI trade shows during the 1980s. (Photograph from Bruce B. Buchanan, “Some Recollections about the
Early Days of AAAI,” AI Magazine, Vol. 26, No. 4, p. 14, c 2005
Association for the Advancement of Artificial Intelligence. Used
with permission.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 344
22.1 Kazuhiro Fuchi (left) and Koichi Furukawa (right). (Fuchi photograph courtesy of Tohru Koyama. Furukawa photograph courtesyof Koichi Furukawa.) . . . . . . . . . . . . . . . . . . . . . . . . . 351
22.2 Fifth-generation system architecture. (Illustration used with permission of Edward Feigenbaum.) . . . . . . . . . . . . . . . . . . 352
22.3 The PIM/p parallel computer system. (Photograph from http://
www.icot.or.jp/ARCHIVE/Museum/MACHINE/pim-spec-E.html.)353
23.1 Robert E. Kahn. (Photograph courtesy of Robert E. Kahn.) . . . 360
23.2 The SC program structure and goals. (Illustration used withpermission of DARPA.) . . . . . . . . . . . . . . . . . . . . . . . 361
23.3 Martin Marietta’s ALV (top) and its system configuration (bottom). (ALV photograph courtesy of DARPA; diagram from R.
Terry Dunlay, “Obstacle Avoidance Perception Processing for theAutonomous Land Vehicle,” Proceedings of the IEEE Robotics
and Automation Conference, pp. 912–917, Los Alamitos, CA:
CS Press, 1988.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 367
24.1 Sir Roger Penrose. . . . . . . . . . . . . . . . . . . . . . . . . . . 384
24.2 John Searle. (Photograph courtesy of John Searle.) . . . . . . . . 385
24.3 Hubert Dreyfus. (Copyright photo: Sijmen Hendriks. Used withpermission from Sijmen Hendriks.) . . . . . . . . . . . . . . . . . 389
24.4 Joseph Weizenbaum. (Photograph courtesy MIT Museum.) . . . 395
24.5 A search tree. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400

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LIST OF FIGURES

24.6 Jacob (Jack) Schwartz. (Photograph used with permission of
Diana Schwartz.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 403
24.7 Richard Korf. (Photograph courtesy of Richard Korf.) . . . . . . 405
25.1 Lotfi Zadeh. (Photograph courtesy of Lotfi Zadeh.) . . . . . . . . 415
25.2 Rodney Brooks (top) and his Crawling Robot, Genghis (bottom).
(Photographs courtesy of Rodney Brooks.) . . . . . . . . . . . . 418
25.3 David Rumelhart (left) and James McClelland (right). (Rumelhart photograph courtesy of Donald Rumelhart. McClelland photograph courtesy of James McClelland.) . . . . . . . . . . . . . . 423
25.4 A cross-over operation. . . . . . . . . . . . . . . . . . . . . . . . . 427
25.5 John Koza. (Photograph courtesy of John Koza.) . . . . . . . . . 428
26.1 A hierarchy of office machines. (From Nils J. Nilsson, Artificial
Intelligence: A New Synthesis, p. 311, San Francisco: Morgan
Kaufmann Publishers, 1998.) . . . . . . . . . . . . . . . . . . . . 437
26.2 A frame. (Adapted from Nils J. Nilsson, Artificial Intelligence:
A New Synthesis, p. 313, San Francisco: Morgan Kaufmann
Publishers, 1998.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 442
26.3 Ronald Brachman (left) and Hector Levesque (right). (Courtesyof Ronald Brachman and of Hector Levesque.) . . . . . . . . . . 442
26.4 Parts of a KRYPTON T Box and A Box. (Adapted from Ronald J.
Brachman, Richard E. Fikes, and Hector J. Levesque, “KRYPTON:
A Functional Approach to Knowledge Representation, IEEE Computer, Vol. 16, No. 10, p. 71, October 1983.) . . . . . . . . . . . 444
26.5 Douglas Lenat (top) and the structure of the Cyc knowledge base(bottom). (Photograph courtesy of Douglas Lenat.) . . . . . . . 448
27.1 A constraint graph for the Four-Queens problem. (From Nils J.
Nilsson, Artificial Intelligence: A New Synthesis, p. 185, San
Francisco: Morgan Kaufmann Publishers, 1998.) . . . . . . . . . 457
27.2 A constraint graph illustrating constraint propagation. (FromNils J. Nilsson, Artificial Intelligence: A New Synthesis, p. 187,
San Francisco: Morgan Kaufmann Publishers, 1998.) . . . . . . . 458
27.3 A solution to the Four-Queens problem. . . . . . . . . . . . . . . 459
27.4 Alan Mackworth. (Photograph courtesy of Alan Mackworth.) . . 459
27.5 A DPLL search tree.

. . . . . . . . . . . . . . . . . . . . . . . . . 463

27.6 Bart Selman (left) and David G. Mitchell (right). (Photographs
courtesy of Bart Selman and of David Mitchell.) . . . . . . . . . 464
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LIST OF FIGURES
27.7 Thomas K. Landauer. (Photograph courtesy of Libby Landauer.) 469
27.8 A two-dimensional subspace within a three-dimensional space. . . 470
28.1 Judea Pearl. (Photograph courtesy of Judea Pearl.) . . . . . . . 478
28.2 A network representation. . . . . . . . . . . . . . . . . . . . . . . 479
28.3 A Bayesian network from an interactive Web site. (Used withpermission of Alan Mackworth and David Poole.) . . . . . . . . . 481
28.4 A large medical Bayesian network. (Used with permission ofGregory Provan.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 483
28.5 Learning a Bayesian network. (From Nils J. Nilsson, Artificial
Intelligence: A New Synthesis, p. 350, San Francisco: Morgan
Kaufmann Publishers, 1998.) . . . . . . . . . . . . . . . . . . . . 485
28.6 Daphne Koller. (Photograph courtesy of Daphne Koller.) . . . . 487
28.7 Example of a PRM. . . . . . . . . . . . . . . . . . . . . . . . . . 487
28.8 A hidden Markov model. . . . . . . . . . . . . . . . . . . . . . . . 489
29.1 Andrew Moore (left) and Chris Atkeson (right). (Photographs
courtesy of Andrew Moore and of Christopher Atkeson.) . . . . . 497
29.2 “Devil Stick” as played by a human and by a robotic memorybased learning system. (Illustrations from Christopher G. Atkeson, Andrew W. Moore, and Stefan Schall, “Locally Weighted
Learning for Control,” Artificial Intelligence Review, Vol. 11, pp.
75–113, 1997. Available online at http://www.cs.cmu.edu/∼cga/
papers/air1.ps.gz.) . . . . . . . . . . . . . . . . . . . . . . . . . . 498
29.3 Janet Kolodner (left) and Edwina Rissland (right). (Photographs
courtesy of Janet Kolodner and of Edwina Rissland.) . . . . . . . 499
29.4 A decision tree for predicting responses. . . . . . . . . . . . . . . 501
29.5 An EPAM discrimination net. (From Edward A. Feigenbaum, “AnInformation Processing Theory of Verbal Learning,” Ph.D. dissertation, Carnegie Institute of Technology, p. 99, 1959, published
as Report P-1817 by The RAND Corporation, Santa Monica, CA,
October 9, 1959. Used with permission of Edward Feigenbaum.)

503

29.6 J. Ross Quinlan. (Photograph courtesy of Ross Quinlan.) . . . . 504
29.7 Terrance Sejnowski (top) and the neural network used in NETtalk(bottom). (Photograph and illustration courtesy of Terrance Sejnowski.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510
29.8 CMU’s Navlab vehicle used by ALVINN. (Photograph courtesyof Carnegie Mellon University.) . . . . . . . . . . . . . . . . . . . 511

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35.3

LIST OF FIGURES

29.9 The ALVINN network (left) and a typical road image (right).
(From Dean A. Pomerleau, “Neural Network Vision for Robot
Driving,” Michael Arbib (ed.), The Handbook of Brain Theory
and Neural Networks, Cambridge, MA: MIT Press, 1995. A version of this paper is available online at http://www.ri.cmu.edu/
pub files/pub2/pomerleau dean 1995 1/pomerleau dean 1995 1.pdf.)512
29.10Unlabeled points in a feature space. . . . . . . . . . . . . . . . . 514
29.11Andrew Barto (left) and Richard Sutton (right). (Photographs
courtesy of Andrew Barto and of Richard Sutton.) . . . . . . . . 516
29.12A maze. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517
29.13Initial stages of the Q-learning process. . . . . . . . . . . . . . . . 519
29.14Stages leading to the goal. . . . . . . . . . . . . . . . . . . . . . . 520
29.15Backward propagation of goal-achieving action valuations. . . . . 520
29.16Andrew Ng (top) and his model helicopter during a roll maneuver(bottom). (Photographs courtesy of Andrew Ng. Helicopter photographs from http://heli.stanford.edu/images/roll mosaic.eps.) . 524
29.17Points and a linear separating boundary in a two-dimensional
space. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526
30.1 Two parse trees for “John shot elephants in pajamas.” . . . . . . 537
30.2 Typical outdoor images. (Images courtesy of Derek Hoiem.). . 54130.3 Original image (left), hand-labeled image (center), and system’s
output (right). (Images courtesy of Derek Hoiem.) . . . . . . . . 542
30.4 A multiscale Markov random field network. (Photographs courtesy of Andrew Ng and Ashutosh Saxena.) . . . . . . . . . . . . . 543
30.5 Typical images (left) and predicted depth maps (right). (Photographs courtesy of Andrew Ng and Ashutosh Saxena.) . . . . 544
30.6 Michael Isard (left) and Andrew Blake (right). (Photographs
courtesy of Michael Isard and of Andrew Blake.) . . . . . . . . . 545
30.7 Tracking a leaf in the wind. (From the initial still frame and froma frame one-half second later of the movie at http://homepages.
inf.ed.ac.uk/rbf/CVonline/LOCAL COPIES/ISARD1/images/leafmv.
mpg. Used with permission of Michael Isard and Andrew Blake.) 545
30.8 Tracking multiple people. (From the movie at http://www.cs.
washington.edu/ai/Mobile Robotics/mcl/animations/floor3D.avi.
Used with Permission of Dieter Fox.) . . . . . . . . . . . . . . . 547

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35

LIST OF FIGURES
30.9 Tai Sing Lee (top left), David Mumford (top right), and theirlayers of visual processing (bottom). (Photographs courtesy of
Tai Sing Lee and of David Mumford. Diagram adapted fromTai Sing Lee and David Mumford, “Hierarchical Inference in the
Visual Cortex,” Journal of the Optical Society of America, A ,Vol. 20, No. 7, July 2003.) . . . . . . . . . . . . . . . . . . . . . . 549
30.10Seeing a face more clearly. (Adapted from Tai Sing Lee and David
Mumford, “Hierarchical Inference in the Visual Cortex,” Journalof the Optical Society of America, A , Vol. 20, No. 7, July 2003.) 551
30.11Geoffrey Hinton (left) and the deep belief network (right). (Photograph from http://www.scholarpedia.org/article/Image:Geoffnew3.
jpg. Network diagram and photograph used with permission of
Geoffrey Hinton.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 552
30.12Some images generated by the trained network. (Used with permission of Geoffrey Hinton.) . . . . . . . . . . . . . . . . . . . . . 552
30.13The HTM model. (Used with permission of Dileep George.). . 553

30.14Parsing an image. (From Song-Chun Zhu and David Mumford,“A Stochastic Grammar of Images,” Foundations and Trends
in Computer Graphics and Vision, Vol. 2, No. 4, pp. 259–
362, 2006. Available online at http://www.stat.ucla.edu/∼sczhu/
papers/Reprint Grammar.pdf.) . . . . . . . . . . . . . . . . . . . 556
31.1 Shakey’s three-level architecture. . . . . . . . . . . . . . . . . . . 564
31.2 A three-layered architecture for a driverless automobile. (Usedwith permission of Ernst D. Dickmanns.) . . . . . . . . . . . . . 565
31.3 A behavior-based architecture. . . . . . . . . . . . . . . . . . . . 565
31.4 James Albus. (Photograph courtesy of James Albus.) . . . . . . 566
31.5 The NASREM architecture. [From Figure 2 of James S. Albus,“A Reference Model Architecture for Intelligent Systems Design,”
in P. J. Antsaklis and K. M. Passino (eds.), An Introduction toIntelligent and Autonomous Control, Chapter 2, pp. 27–56, Dordrecht: Kluwer Academic Publishers, 1993.] . . . . . . . . . . . . 567
31.6 Triple-tower architecture. . . . . . . . . . . . . . . . . . . . . . . 568
31.7 Michael Georgeff. (Photograph courtesy of Michael Georgeff.) . . 570
31.8 PRS, a BDI architecture. (From Michael P. Georgeff and François
F. Ingrand, “Decision-Making in an Embedded Reasoning System,” Proceedings of the Eleventh International Joint Conferenceon Artificial Intelligence, pp. 972–978, August, 1989.) . . . . . . 571
31.9 Manuela Veloso (top) and soccer-playing Aibo robots (bottom).
(Photographs courtesy of Manuela Veloso.) . . . . . . . . . . . . 574
674
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35.3

LIST OF FIGURES

31.10A production rule architecture. . . . . . . . . . . . . . . . . . . . 578
31.11John Anderson. (Photograph courtesy of John Anderson.) . . . . 579
31.12The basic ACT-R architecture. (Used with permission of John
Anderson.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581
31.13John Laird (left) and Paul Rosenbloom (right). (Photographs
courtesy of John Laird and of Paul Rosenbloom.) . . . . . . . . . 582
31.14Memory structures in SOAR. (Illustration used with permissionof John Laird and Paul Rosenbloom.) . . . . . . . . . . . . . . . 583
32.1 Garry Kasparov playing chess against Deep Blue in game twoof a six-game rematch. (Photograph used with permission of
AP/Wide World Photos. c ) . . . . . . . . . . . . . . . . . . . . 592
32.2 Jonathan Schaeffer. (Photograph courtesy of Jonathan Schaeffer.) 596
32.3 Schematic for the checkers proof. (Illustration used with permission of Jonathan Schaeffer.) . . . . . . . . . . . . . . . . . . . . . 598
32.4 Artist’s rendering of DS1 approaching a comet. . . . . . . . . . . 601
32.5 Remote agent architecture. (Used with permission of P. Pandurang Nayak.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 601
32.6 Illustration of RAX activities. (Illustration courtesy of Mark
Shirley at NASA.) . . . . . . . . . . . . . . . . . . . . . . . . . . 603
32.7 Stanley on Beer Bottle Pass followed by a DARPA chase vehicle.
(Photograph courtesy of DARPA.) . . . . . . . . . . . . . . . . . 606
32.8 Sandstorm on Beer Bottle Pass. (Photograph courtesy of DARPA.)607
32.9 Sebastian Thrun (left) and Michael Montemerlo (right). (Photograph courtesy of John Markoff.) . . . . . . . . . . . . . . . . . . 608
32.10Tartan Racing team leader William (Red) Whittaker and Bosspose with first place trophy. (Photograph courtesy of Carnegie
Mellon University.) . . . . . . . . . . . . . . . . . . . . . . . . . . 611
35.1 Searching for objects in images. (From James Philbin et al., “Object Retrieval with Large Vocabularies and Fast Spatial Matching,” Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), 2007. Available online at http://www.robots.ox.ac.uk/∼vgg/publications/papers/philbin07.pdf.) 635
35.2 Marc Raibert (left) and BigDog (right). (Photographs courtesyof Boston Dynamics c 2008.) . . . . . . . . . . . . . . . . . . . . 638
35.3 STAIR unloading a dishwasher at the Stanford AI Lab. (Photograph courtesy of Andrew Ng.) . . . . . . . . . . . . . . . . . . . 639

675
Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

LIST OF FIGURES
35.4 HERB (top left), DOMO (top right), and SMARTPAL V (bottom). (HERB photograph used with permission of Siddhartha
Srinivasa; DOMO photograph used with permission of Aaron
Edsinger; SmartPal photograph used with permission of Yaskawa
Electric.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 641
35.5 CALO’s functions. (Used with permission of Karen Myers andThomas Garvey.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 643

676
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All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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35.3

Index
2 12 -D sketch, 329, 335, 338, 541
3-LISP, 571
A Box and T Box, 443
A∗ , 216–221, 278, 518, 538, 620
its use in computer games, 620
its extensions by Richard Korf,
221
its use in parsing, 221, 538
its use in route finding, 618
AAAI
founding of, 343
abductive reasoning, 438
ABE, 372
Abelson, Robert, 207, 417
ABSTRIPS, 230
AC-3, 456
ACLS, 506
ACRONYM, 336, 338
ACT-R, 577–581, 584
its applications, 580
actions
in reinforcement learning, 517
Adaboost, 525
ADALINE, 98
Adams, James, 231
adaptive cell decomposition, 218
add lists
in STRIPS, 223
Adelson-Velskiy, Georgi, 251
Advanced Research Projects Agency,
see DARPA
advice taker, 82
Agent Communication Language(ACL), 576
AgentSpeak, 576
Agin, Gerald, 336

AI and cognitive science, 43, 72
AI complete, 533
AI Projects
early
at CMU, 157
at Edinburgh, 158
at MIT, 157
at Stanford, 157
AI winter, 345, 381, 408–409
Albert, Lev, 593
Albus, James, 565
Alder, Phillip, 599
Allen, Paul, 229
Aloimonos, Yiannis, 337
ALPAC, 150, 237, 398
alpha–beta procedure, 127, 251
Alvey Program, 345, 355
its research areas, 355
Alvey, John, 355
ALVINN, 510–513
Amarel, Saul, 118
ambient intelligence, see ubiquitousAI
Ames Research Center, 600
analogy problems
solving of, 131–134
analysis of photographs, 105–108, 338,
370
anaphora, 247
Anderson, Alan Ross, 381
Anderson, John, 205, 578
his co-authored book Human
Associative Memory, 205
photo of, 579
Andreae, John, 516
antenna systems

677

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

INDEX

derived by genetic programming,
628
applicationsof SOAR, 584
architectures
ACT-R, 578–581
SOAR, 581–584
based on cortical models, 554
BDI, 569–571
behavior-based, 563, 569
Blackboard, 279, 321, 323, 331,
393, 548, 568
cognitive, 576–584
reference model of James Albus,
565
subsumption, 419, 563
three-layer, 563
triple tower, 567
ARGO project, 604
Aristotle, 19, 20, 27, 28, 200, 657
syllogism of, 27, 200
Arkin, Ronald, 653
ARPA, see DARPA
Arpanet, 173, 343, 359
Ars Magna, 20
artificial general intelligence (AGI),
648
Artificial General Intelligence
Research Institute (AGIRI),
648
Artificial Intelligence Applications
Institute, 500
Ashby, Ross, 50, 54, 81
photo of, 51
ASK, 318
assignment problems, see constraint
satisfaction problems
Association for Computational
Linguistics (ACL), 150
association units
in perceptrons, 96, 527
associative links
in Quillian’s network, 136
Athena DSS

for hypertension management,
624
678

Atkeson, Christopher, 497
photo of, 497
Atkin, Larry, 252
auctions among agents, 576
augmented transition networks,
243–245, 315
AURORATMfor scheduling, 625
Austin, John, 575
AutoClass, 515
automata, 21, 46
automated trading, 626–627
Automatic Language Processing
Advisory Committee, see
ALPAC
automaton memoof Charles Rosen, 213
Autonomous Land Vehicle (ALV)
project, 366–369, 604
its termination, 369
milestones for, 368
participants in, 366
autonomous vehicles, 362, 366–369,
513, 540, 547, 563, 603–612
axiom modelin STRIPS, 222
Axline, Stanton, 292
axons, 34
Babbage, Charles
his Analytical Engine, 55, 64
his Difference Engine, 55
his interest in chess, 123
Bach, Michael, 170back propagation, see backprop
algorithm
backgammon, 522
background knowledge
in inductive logic programming,507
backing up scores in a game tree,
125–127
backprop algorithm, 424, 508–509
backtracking, 456, 459
Backus, John, 153
backward connections

Copyright c 2010 Nils J. Nilsson
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All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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35.3

INDEX

in neural networks, 548
bagging, 524
Baker, James, 272
Baker, James and Janet, 281
Baker, Janet, 275
Bao, Xinlong, 643
Bar-Hillel, Yehoshua, 148
his comment on McCarthy’s
Teddington paper, 150
Barlow, Horace, 172
Barnett, Jeffrey, 282
Barricelli, Nils, 43
Barrow, Harry, 196, 329, 332, 333,
350, 550
Barto, Andrew, 516, 523
photo of, 516
BASEBALL, 150
Basic English, 152
battle management systems, 362,
364–366
Bayes’s rule, 52–53
definition of, 52
use ofin PROSPECTOR, 296
in Bayesian networks, 480
in pattern recognition, 103
in signal detection, 52
in tracking, 546
Bayes, Thomas, 52
Bayesian networks, 416, 475–491, 495,
507
applications of, 482, 488
automatic construction of,
482–486
temporal, 488
BBN, 157, 161, 243, 246, 270, 271,
281, 313, 318, 359, 365,
370–373, 443, 575, 628
its work on speech recognition,
271–272
BBN-LISP, 292
BDI architecture, 569–571
beam search, 278
BeastJohns Hopkins robot, 213
Beer, Randall, 425

Begriffsschrift, 33
behavior-based architectures, 563
being in the world, 392
belief networks, see Bayesian networks
beliefs, desires, and intentions, 569
Bell Laboratories, 150, 269
Bell, Anthony, 172
Bellman, Richard, 516
Berliner, Hans, 253
Bethe, Hans, 478
BigDoga walking robot, 637
its sensors, 637
Binford, Thomas, 190, 336
Binford–Horn line finder, 190
bionics, 46
Bisson, Charles, 172
Black and White 2
a computer game, 620
Black, Fisher, 200
Blackboard architectures, 323, 331,
393, 548, 568, 572
in HASP, 321
in HEARSAY, 279
Blackbox, 466
Blake, Andrew, 545
photo of, 545
BLAZE ADVISOR 6.1, 627
Bledsoe, Woodrow
at MCC, 354
his work on face recognition, 172
his work on simulating evolution,
44
his work on theorem proving, 201
his N-tuple method, 97
Blei, David, 472
Block, H. David, 96
block-sorting and stacking
at SAIL, 192
blocks world, 241, 439
Bobrow, Daniel, 156, 248
his GUS system, 246
his KRL system, 210
his STUDENT system, 152
his transition network, 245
Bod, Rens, 538
679

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

INDEX

Boden, Margaret, 238
Bolles, Robert, 198
Bolt, Beranek, and Newman, see BBN
Boltzmann machines, 424, 491
book gamesin Samuel’s checker-playing
program, 128
book moves
in Deep Blue, 593
Boole, George, 31, 53, 200
Boolean algebra, 31–32, 58
boosting, 525
Booth, Taylor, 536
bootstrap samples, 524
Bossentrant in Urban Challenge, 609,
610
Boston Dynamics, 637
bottom-up search, 146
Bower, Gordon, 205, 578
his co-authored book Human
Associative Memory, 205
BOXES, 516
Boyer, Robert, 160
Brachman, Ronald, 441
photo of, 442
Brain, Alfred E. (Ted), 98, 99
brain-style computation, 423
branching factor, 399
reduction of, 401
Bratley, Paul, 245
Breiman, Leo, 506, 524
Bremermann, Hans, 44
Bresnan, Joan, 637
Brice, Claude, 225
Broadcast Monitoring System, 628
Brooks, Rodney, 336, 417, 563
his graded list of AI challenges,
650
photo of, 418
brute-force methods, 594
Bryson, Arthur, 508
Buchanan, Bruce, 256, 291
photo of, 255
buffersin ACT-R, 579
680

BUILD, 439

Burstall, Rod, 160
business rule engines, 303, 627
business rule management systems(BRMSs), 627
business rules, 303, 627
Butterfly Multiprocessor, 370
C, 506
C4.5, 506
C5.0, 506

caching resultsin SOAR, 583
calculususe of in backprop, 508
calculus ratiocinator, 29
CALO
a cognitive assistant, 642–643
its AI components, 642
Caltech, 637
Campbell, Alan, 295, 298
Campbell, Murray, 123, 593
cancellation of inheritance, 436
Canny edge detector, 180
Canny, John, 180
Carlstrom, David, 338
Carnegie Institute of Technology, see
CMU
Carnegie Mellon University, see CMU
CART, 503, 506
CART 5, 506
cartography, 370
case-based reasoning (CBR), 498–500
CASES, 365, 366
CASNET, 301
categorial grammars, 534
categorical data, 500
causality reasoning, 480
CCH-ES, 302
cell assemblies, 36
Centre National de la Recherche
Scientifique, 204
Cerf, Vinton, 359
his Turing Award, 359
certainty factors, 293

Copyright c 2010 Nils J. Nilsson
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All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

their relationship to probabilities,
294
character recognition, 90–92
of hand-printed characters, 101
ONR’s support of, 161
using templates, 90
Charniak, Eugene, 535
chart parser, 534
CHAT-80, 315–318
checkers, 61, 71, 78, 124, 156, 160,
202, 251, 399, 516, 591,
595–598, 619
Arthur Samuel’s research on,
123–128
Jonathan Schaeffer’s research on,
595–598
optimum play in, 597
proof that optimum play ends ina draw, 597
Cheeseman, Peter, 515
chemical structure, 256
elucidation of, 256
CHESS, Northwestern University
programs, 252, 406, 593
chess, 71, 160, 251, 406, 591–595
Chinese Room
Searle’s thought experiment,
384–387
CHINOOK, 596
Chomsky, Noamhierarchy of grammars, 244
his arguments with Skinner, 40
his book Syntactic Structures,
142, 144
on universal grammar, 40
phrase-structure grammars, 42,142
chromosomes, in genetic algorithms,
44
chunksin ACT-R, 579
of short-term memory items, 41
Churchland, Patricia, 337
CIA, 370
its 1960s face-recognition
research, 172

circumscription, 438
Clark, Wesley, 74, 92
CLASSIC, 443
ClearFlow, 618
cliffsin the error surface, 508
closed-world assumption (CWA), 436
in Cyc, 449
Clowes, Max, 185
cluster analysis, 515
clusters, 513
CMU, 76, 109, 136, 151, 156–158, 161,
167, 173, 270, 271, 301, 366,
502, 510, 572, 573, 578, 593,
605, 609, 640, 645
its work on speech recognition,
272–280
Cocke–Younger–Kasami (CYK)
algorithm, 534
Cognitec Systems GmbH, 629
cognitive architectures, 576–584
cognitive science, 42, 155birth of, 42
foundations of, 73
Cohen, Philip, 575
Cohen, Stanley, 291
Coles, Stephen, 228
collaborative filtering
in recommending systems, 619
Collins, John, 160
collision avoidance systems, 490
Colmerauer, Alain, 204, 316, 350
photo of, 204
combinatorial explosion, 116, 263,
399–401, 404, 475
combinatorial optimization problems,426
command and control, 312
common sense, 82, 207, 407, 447, 450,
534
Commonsense Computing Initiative,
450
COMPANIONSa personal assistant project, 644
complex cells
in visual cortex, 171
681

Copyright c 2010 Nils J. Nilsson
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All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

complex information processing
phrase used to describe CMU’s
AI work, 157
complexity theory, 401
computational learning theory, 524
computer games, 619–621
Computer Professionals for Social
Responsibility (CPSR), 373,
394
computer vision, 169–185, 313, 327,
340, 539–556
active, 540
applications of, 555
as part of the Strategic
Computing program, 370
for scene analysis, 540
journals and textbooks, 340
of Shakey, 224–226, 540
of Stanley, 608
purposive, 540
computer-based consultant (CBC)project at SRI, 230, 285–290
computers
early history of, 53–56
stored-program, 58–61
concept learning system (CLS), 502
conceptual dependency, 206–207
CONDENSATION algorithm, 545
conditional independence, 479
conditional probability table (CPT),
479
conflict resolution
in production systems, 577
connectionist computation, 424
consciousness, 654–655
consistencyof line labels, 183, 185constraint graph, 456
constraint propagation, 456
constraint satisfaction problems, 185,
455–459
consulting systems, 293
content-based filtering
in recommending systems, 619
content-based image retrieval,
634–636682

contract nets, 573
Control Data Corporation, 354
convolution, 176
Cook, Stephen, 402
Cooper, Greg, 482
Cooper, Robert, 361, 374
Cope, David, 654
copy demo at MIT, 190, 540
Cornell Aeronautical Laboratory, 92,
97
Cornell University, 92, 96, 97, 507,
610
corporanatural language, 535
correspondence problem
for stereo vision, 169
cortexmodels of, 107, 323, 548, 549,
552, 554, 651
Cover, Thomas, 104
creativity
in AI systems, 654
Cristianini, Nello, 527
cross-overin genetic algorithms, 426
in genetic programming, 428
CSAIL, 161
cybernetics, 49, 160
Cyc, 446–450, 533, 644
its knowledge base, 449
CycL, 448
Cycorp, 448, 449
Da Vinci, Leonardo, 20
DAML-ONT, 443
DARPA, 160, 214, 263, 265, 281, 285,
309, 321, 338, 345, 357, 373,
575
DARPA speech understanding
research program, 271–281
DARPA’s Grand Challenges, 604–610
DARPA’s PAL program, 642
DARPA’s Urban Challenge, 609–610
DART, 373
Dartmouth 1956 Workshop on AI,
77–80

Copyright c 2010 Nils J. Nilsson
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All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

data mining, 495, 500–502
relational, 507
using memory-based learning,
498
Davis, Martin, 461
Davis, Ruth, 214
DDR&E, 214
de Kleer, Johan, 440
de Vaucanson’s duck, 21
de Vaucanson, Jacques
his loom, 47
Dean, Thomas, 490, 554
Dechter, Rina
her book on constraint
processing, 457
decision support systems (DSSs)
in medical practice, 623
decision theory, 53
its use in meta-level reasoning,571
decision trees, 500–507, 525, 527, 542
their use in constructing
IF–THEN rules, 506
declarative knowledge, 82, 199, 205,
242, 414, 439, 584, 645
in ACT-R, 579
deduction
example of, 200in symbolic logic, 200–202
deep belief network, 551
Deep Blue, 591–595
Deep Fritz, 595
Deep Space 1 (DS1), 600
deep structure, 245
Deep Thought, 593
default logic, 437
Defense Mapping Agency, 339
degree of belief, 293, 414
delete lists
in STRIPS, 223
Delphi method, 633
DELTA, 302
demonsin Pandemonium, 83
Dempster, Arthur, 304
Dempster–Shafer theory, 304, 414

DENDRAL, 255–259, 265, 291, 319

dendrites, 34
Denicoff, Marvin, 161
Dennett, Daniel
on Roger Schank, 209
Department of Machine Intelligenceand Perception, 160, 193
dependency grammars, 534
depth information
clues for, 543
from single images, 541–544
description logics, 441–443
devil-sticking
example of memory-based
control, 497
Devol, George, 190
Dewar, Hamis, 245
diagnostic reasoning, 480
DIAGRAM, 318, 534
DIALOGIC, 318
DIAMOND, 318
Dickmanns, Ernst, 547, 563, 604
Diebenkorn, Richard, 555
Dietterich, Thomas, 643
differencesin GPS, 121differences between brains andcomputers, 392
Digital Equipment Corporation, 75,
157, 158, 239, 301
Digitalsmiths, 636
Dijkstra, Edsger, 398
Dinneen, Gerald, 74–76, 89, 91, 176
directed acyclic graphs (DAGs), 480
disambiguation, 138
discrimination net, 502
disparity
in stereo vision, 169
display terminals, 157
distance
between data points, 496
distributed AI, see multiagent systems
DISTRIBUTED HEARSAY-II, 572
Distributed Vehicle Monitoring
Testbed (DVMT), 573
Djerassi, Carl, 258
683

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

INDEX

DOMO
an MIT robot, 640
Donahue, Christopher, 523
Doyle, Lauren, 152
Doyle, Worthie, 91
DPLL, 461
example of, 462
DRAGON, 272–275
Dragon Systems, 281
Dresden University of Technology, 438
Dreyfus, Hubert, 163, 252, 389, 392
his books and articles on AI, 391
his paper “Alchemy and Artificial
Intelligence”, 389
Dreyfus, Stuart, 389
Dreyfus-MAC HACK VI chess match,
252
driver assistance systems, 617
driverless automobiles, seeautonomous vehicles
droughts, see checkers
Druffel, Lawrence, 339
Duda, Richard, 101, 221
his co-authored textbook, 104,515
his work on PROSPECTOR, 295
his work on the Houghtransform, 225
on joining Syntelligence, 303
photo of, 101
Dumais, Susan, 472
dynamic Bayesian networks (DBNs),
490, 546
dynamic programming, 516
use of in character recognition,101
use of in parsing, 534
use of in speech recognition, 275
dynamic vision, 547
dynamical processes, 424–425
Dyson, George, 21
Early parser, 534
Earnest, Lester, 157, 162, 191, 231
edge detectors, 172, 174
Edsac, photo of, 60
684

Efros, Alexei, 541
eigenfaces
their use in face recognition, 629
eight-puzzle, 114, 155, 399, 404
Einaudi, Marco, 295
Ejiri, Masakazu, 193
Elias, Peter, 175
ELIZA, 62–63, 150, 394, 619
Elschlager, Robert, 335
embodiment, 390, 392
employment
effects of AI on, 655
employment test, 648
EMYCIN, 294, 303, 372
endgame databasein checkers, 597
in chess, 593
energy-based models (EBMs), 555
entailment
in WordNet, 446
envisioning, 440
EON

for protocol-based medical care,
624
EPAM, 502–503
episodic knowledge
in SOAR, 584
epistemic logic, 575
ERMA check processing machine, 90
Erman, Lee, 272, 572
Ernst, George, 156
Ernst, Heinrich, 189
error-correction training procedure,
96, 525, 526
ESPRIT, 345, 355–357
its research areas, 356
Etzioni, Oren, 645
EUFID, 318
evaluation functions, 76
in Samuel’s checker-playing
program, 516
used by Deep Blue, 593
Evans, Thomas, 131, 156
evidential reasoning, 480
evolution
recapitulation of, 417

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

simulation of, 43–44, 425–429
as an invention machine, 628
exception principle
as nonmonotonic reasoning, 436
in SIR, 135
expectation maximization (EM)algorithm, 538
Experimental Programming Unit, 160
expert system shells, 365
expert systems, 259, 291–304, 343,
363, 364, 391, 505, 577, 627
as part of the Strategic
Computing program,
372–373
brittleness of, 407
expert systems companies, 303
explaining away, 480
exploitation vs. explorationin reinforcement learning, 521
face recognition, 172–173, 555,
628–629
Face Sensing Engine (FSE), 629
FaceVACS

face recognition system, 629
FACTorya Cyc game, 450
Fahlman, Scott, 439
Fair Isaac Corporation, 304, 627
Fairchild Laboratory for Artificial
Intelligence Research, 443
Falk, Gilbert, 185
Farley, Belmont, 74, 92
fault diagnosis, 440
favorability maps
as produced by PROSPECTOR,
298
FCCBMP, 364
F.E.A.R.
a computer game, 620
featuresin case-based reasoning, 499
in checkers, 126, 131
in face recognition, 172
in images, 75, 83, 176
in Pandemonium, 91

in pattern recognition, 91, 103,
104, 195, 513
of chess board positions, 506
used by Deep Blue, 593
feedback, 47–51
Feigenbaum and Feldman
their co-edited book, 262
Feigenbaum, Edward, 253, 255, 291,
313, 319, 372
and HLAI, 646
his EPAM program, 502
his co-authored book about theFifth Generation, 362
his congressional testimony about
the Fifth Generation, 362
photo of, 255
Feist, Mathias, 595
Feldman, Gary, 186
Feldman, Jerome, 190, 424
Fennema, Claude, 225
FGCS, 345, 349–353, 355, 357, 359
its role in selling the U.S.
Congress on Strategic
Computing, 362
fifteen-puzzle, 114
Fifth Generation Computer Systems,
see FGCS
Fikes, Richard, 223, 224, 443
filteringby HMMs, 490
Firby, R. James, 563
Firschein, Oscar, 633
first-order logic, see predicate calculus
Fischler, Martin, 335, 370
fitness
in genetic algorithms, 426
fitness landscape, 427
Fix and Hodges, 103
Fletcher, Tristan, 526
float regulator, 49
FLPL, 156
flyball governor, 49
flythroughgraphical simulation of, 370
focus
in dialog, 288
685

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

Fogel, Lawrence, 44
FOIL, 507
FOL, 571
Forbus, Kenneth, 441
Ford, Kenneth
on HLAI, 649
on the Turing test, 648
Forgie, James, 282
Forgy, Charles, 301, 303
FORTRAN, 156, 251, 350
FORTRAN coding sheets, 101, 275
forward-backward algorithm, 490
Foundation for Intelligent Physical
Agents (FIPA), 576
Four-Queens problem, 455
Fox, Dieter, 546
frame problem, 222, 438
Frames, 209–210, 441, 499
Franz Inc., 344FranzLISP, 344
Fraser, Bruce, 245
FREDDY, 193–197, 263
Fredkin, Edward, 157
Frege, Gottlob, 33, 53, 200
FRESH, 364, 365
Freud, Sigmund, 39
Freuder, Eugene, 190
Freund, Yoav, 525
Friedberg, R. N., 43
Friedman, Jerome, 506
Friedman, Nir, 486
fringe consciousness, 390
FRL, 210
Frog
vision system of, 171
Fuchi, Kazuhiro, 350
Furukawa, Koichi, 350, 351
fuzzy logic, 304, 414
fuzzy set theory, 414
Galatea, 19
Game AI, 619
game theory, 576
game-playing programs, 251–253,
591–599
garbage collection
686

in LISP, 562
Garvey, Thomas, 333
Gaschnig, John, 296
Gasser, Les, 573
Gat, Erann, 563
Gates, Bill, 229
Gelernter, Davidhis views on AI andconsciousness, 655
Gelernter, Herb, 118, 120, 121, 138,
156, 160, 390
General Electric, 90, 302, 372, 623
General Problem Solver, see GPS
General Syntactic Processor, GSP,
248
generalized cones and cylinders
use of in computer vision, 336
generalizing plans, 224
Genesereth, Michael, 571, 576
genetic algorithms, 44, 426–427
as a search process, 427
genetic programming, 427–428
as an invention machine, 628
Genghis, 418
Geological Survey, U.S., 295, 299
geometry theorem proving, 80,
118–121, 160
George, Dileep, 552
Georgeff, Michael, 569
photo of, 570
Georgia Institute of Technology, 499
German Research Center for Artificial
Intelligence (DFKI), 356
Getoor, Lise, 486
GIB, 599
Gideonfor infectious diseases, 624–625
Gilmore, Paul, 200
Ginsberg, Matt, 599
Glucksman, H., 526
Go, 407, 599
Goebel, Randy
his co-authored textbook, 491
Goertzel, Ben, 648
Goldstein, Ira, 210
golems, 20

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

Good, I. J., 150
and ultraintelligent machines, 647
at Bletchley Park, 158
good-old-fashioned AI (GOFAI), 391,
413
Google, 411, 468, 488, 539, 542, 589,
618
Gorlen, Keith, 252
GPS, 121–123, 156, 223, 576, 581
gradient ascent, gradient descent, seehill climbing
gradient operator
in computer vision, 174, 177, 178
grammars, 142categorical, 534
context-free, 143, 534, 536
definite clause, 317, 534
dependency, 534
example of, 142–143
government and binding, 534
head-driven phrase structure, 534
lexical functional, 534, 637
learning of, 538
of images, 555
phrase structure, 142
probabilistic, 536
semantic, 276, 315
statistical, 535–539
systemic, 241, 534
transition network, 243–245, 315,
534
tree adjoining, 534
graphof states in reinforcement
learning, 518
graphical user interfaces, 351
greedy search, 464
Green, Bert, 151
Green, Cordell, 83, 201, 202, 204, 270,
309
Greenblatt, Richard, 252
Grenander, Ulf, 549
grid model, 218
Grosz, Barbara, 289
photo of, 289
GSAT, 464

example of, 465
Gulko, Boris, 593
GUS, 246–249
Guzman, Adolfo, 181
his program for analyzing line
drawings, 181–182
Gödel, Kurt, 382
H1ghlander
entrant in Grand Challenge, 605,
606
habile systems, 646
HAL 9000, 392, 600, 653
HAM, 205, 578
hand–eye research, 189–197, 213, 239
at Edinburgh, 193–197
at MIT, 189–190
at Stanford, 190–193
in Japan, 193
Hanson, Alan, 370
Hanson, Robin
his views on AI and employment,
655
Harbaugh, John, 295
Harley, Thomas, 105
Harmon, Paul, 302
HARPY, 271, 272, 276–278
Harrison, John, 49
Hart, Peter, 101, 104, 172, 187, 443
his co-authored textbook, 104,515
his work on PROSPECTOR, 295
his work on A∗ , 220
his work on the Houghtransform, 225
his work on triangle tables, 224
on founding Syntelligence, 303
photo of, 101
HASP, 313, 319–323, 568
Haugeland, John, 391, 413
Hawkins, Jeff, 552
Hayes, Patrick, 440, 571
on HLAI, 649
on the Turing test, 648
Hayes-Roth, Frederick, 272, 373

687

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

head-driven phrase structure
grammars (HPSGs), 534
Hear Here, 192
HEARSAY-II, 271, 272, 278–280, 321,
568, 572
Hebb rule, 36
Hebb, Donald, 35, 74, 92
Hebert, Martial, 541
Hebrew University, 486
hedonistic neurons, 516
Heidegger, Martin, 392
Heilmeier, George, 310, 360
photo of, 311
helicopter control
using reinforcement learning, 523
Hendrix, Gary, 296, 313
photo of, 314
Hephaistos, 19
HERB
a CMU robot, 640
its sensors, 640
Herskovits, Edward, 482
heuristic estimate
in tree search, 220
heuristic search, 117, 122, 401, 404,
595
in SOAR, 583
in checkers, 597
in Remote Agent, 600
used by Deep Blue, 593
using A∗ , 221
Hewitt, Carl, 205, 223, 241, 436, 572
hidden Markov models, see HMMs
hidden nodes
in Bayesian networks, 486
hidden states
in reinforcement learning, 521
hidden units
in NETtalk, 509
in ALVINN, 511
hierarchical models
in computer vision, 548–555
hierarchical planningat Edinburgh, 230–231
at SRI, 229–230
hierarchical temporal memory, 552
688

Hildreth, Ellen, 178
hill climbing, 44, 427, 464
for SAT problems, 463
in SOAR, 583
in backprop, 508
in constructing Bayesian
networks, 484
in Pandemonium, 85
Hinton, Geoffrey, 424, 508
photo of, 552
histories, 440
HIVIP, the Japanese hand–eye
system, 193
Hixon Symposium, 73, 77
HLAI, 634, 646–657
its possible consequences,652–656
HMMs, 275, 488–490
HMMWV (humvee)
as an autonomous land vehicle,
369
Ho, Y. C., 508
Hobbes, Thomas, 20
Hoff, Marcian (Ted), 98
Hofmann, Thomas, 472
Hoiem, Derek, 541
Holland, John, 44, 426
Hollerith, Herman, 47
Hollister, Floyd, 313
Hollister, Victor, 298
holonymin WordNet, 446
homeostasis, 50, 221
Homer’s Iliad, 19
Honeywell, 338
Hopfield networks, 424
Hopfield, John, 424
Horn clauses
guarded, 350
Horn, Berthold, 190, 327, 329
Horvitz, Eric, 571
Hough transform, 225
Hovland, Carl, 502
Hsu, Feng-Hsuing, 593
Hubel, David, 171
Huffman coding, 182

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

Huffman, David, 182
his analysis of line drawings,
182–185
Huffman–Clowes line-labeling, 185
Hugin Expert, 492
human memory organization
model of, 137
human-level artificial intelligence, see
HLAI
Hunt, Earl, 502, 504
Hurd, Cuthbert, 148, 343
Huskey, Harry, 147
HWIM, 271, 272
hypernym
in WordNet, 445
hyponymin WordNet, 445
IBM, 43, 77, 78, 98, 100, 109, 118,
123, 124, 148, 149, 157, 160,
200, 251, 281, 343, 628, 651
IBM and Deep Blue, 591–595
ICOT, 350, 351
ID3, 503–506
IDA, 315
IF–THEN rules, 280, 292, 296, 303,
321, 505, 506
as productions, 577
Iida, Fumiya, 392
IJCAI, 261, 589
Iliad

for internal medicine, 624
ILOG, 627
image averaging, 176
image filtering, 176–181
image grammars, 555
stochastic, 555
image retrieval
content-based, 634–636
Image Understanding program, 313,
338–340Imperial College, 438
inductive inference, 495
inductive logic programming, 507
applications of, 507
inference engine, 294, 303, 627

in Cyc, 449
inference networks, 296
Information Builder, 627
information gainin decision-tree learning, 505
Information Processing Techniques
Office, see IPTO
information retrieval, 467
Information Sciences Institute, see
USC-ISI
inheritance of properties, 446
in Cyc, 448
Inman, Bobby Ray, 354, 447
Institut für Systemdynamik und
Flugmechanik, 547
Institute for Human and Machine
Cognition, 648
Institute for New Generation
Computer Technology, see
ICOT
Institute for Theoretical andExperimental Physics, 251
IntelliCorp, 372
IntelliGenetics, 372
intelligent assistants, 642–644
intensive search methods
in checkers, 597
intentionality, 383
INTERLISP, 292, 343
intermediate-level programs
as used by Shakey, 221, 419
International Business Machines, see
IBMInternational Computer Games
Association, ICGA, 599
Internet
DARPA’s support of, 161
INTERNIST-1, 300, 482, 624
INTERPLAN, 230
interrupts
in computer operating systems,
561
intrinsic images, 329–333, 338, 550
IPL-V, 150, 502
IPL, 156, 164
IPTO, 161, 290, 359, 370
689

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

IraqComm

SRI’s speech translation system,
628
IRIS, 643
IRUS, 371
Isard, Michael, 545
photo of, 545
Jacobstein, Neil, 373
Jacquard, Joseph-Marie
his loom, 46
James, William, 38
JANUS, 281, 371
Jason interpreter, 576
Jelinek, Frederick, 281
Jet Propulsion Laboratory (JPL), 600
JETA, 302
Jochem, Todd, 513
JOHNNIAC computer, 113
Johnson, George, 344
joint probabilities, 475, 477
Jordan, Michael, 472
JRules 6.0, 627
Juniorentrant in Urban Challenge, 609
Kahn, Robert, 359, 374
his Turing Award, 359
photo of, 360
Kalman filtering, 490
Kanade, Takeo, 173, 370
Kanal, Laveen, 105, 509
Kanazawa, Keiji, 490
Kaplan, Ronald, 243, 246, 248, 538,
557, 637
Kapor, Mitchellhis bet on the Turing test, 649
Kasparov, Garry, 591
photo of, 592
Kat-5
entrant in Grand Challenge, 606
Kautz, Henry, 466
Kay, Martin, 534
KBMS, 315
KEE, 372
Kelly, Michael, 172690

kernel machines, 527
kernelsin support vector machines, 527
use of in nearest-neighbormethod, 496
Kirsch, Joan, 559
Kirsch, Russell
experiments with image
processing, 176
use of drum scanner, 89
use of image grammars, 555
use of reinforcement learning, 40
KL-ONE, 443
Klatt, Dennis, 280, 282
Klein, Danon the use of A∗ in parsing, 233
Klopf, A. Harry, 516
kludginess, 416
KnowItAll Project, 645
knowledgeof chemists, 257
Knowledge Areas (KAs)in PRS, 569
knowledge baseof rules, 294
knowledge engineers, 391
Knowledge Interchange Format(KIF), 575
knowledge principle, 258
knowledge representation andreasoning, 199–210
knowledge sources, 321, 393, 568, 572
in HARPY, 276
in HEARSAY-II, 279
knowledge-based systems, 373
knowledge-is-power hypothesis, 258
Knuth, Donald, 398
Koller, Daphne, 486
photo of, 487
Kolodner, Janet, 499
photo of, 499
Konolige, Kurt, 296, 298
Korf, Richard, 221, 404
photo of, 405
Kornfeld, William, 572
Kotok, Alan, 251

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

Kowalski, Robert, 160, 204
photo of, 204
Koza, John, 427, 628
his books on genetic
programming, 428
photo of, 428
Kozdrowick, Ed
and the Levy chess wager, 252
KQML, 575
Kramnik, Vladimir, 595
KRL, 210
Kronrod, Alexander, 123, 251
KRYPTON, 443
Ktesibios, 49
Kuehner, Donald, 204
Kuipers, Benjamin, 407, 441
Kulikowski, Casimir, 301
Kumar, Vipin, 457
Kunze, Fritz, 344
Kurzweil, Ray
and the singularity, 647
his bet on the Turing test, 649
Lafferty, Don, 597
Laird, John, 581, 584
on computer games, 620
photo of, 582
Landauer, Thomas, 469
photo of, 469
Langley, Patrick, 584
Laplacian of Gaussian (LoG), 178
laser range finderof Shakey, 216
Lashley, Karl, 73, 229
Latent Semantic Analysis (LSA),
469–472
applications of, 471
Latent Semantic Indexing (LSI), 471
LCS, 161
LDC-1, 318
learning, see machine learning also
by reading, 644–645
by Stanley, 609
in SOAR, 583
in Pandemonium, 84–85
memory-based, 496–498

of Bayesian networks, 482
of decision trees, 502–507
of LFGs, 538
of PCFGs, 537
of plans, 224
Q-learning, 518
supervised, 513
temporal-difference, 522
unsupervised, 513–515, 548, 551,
552
Learning by Reading (LbR), 645
Lebowitz, Michael, 499
LeCun, Yann, 555
Lederberg, Joshua, 255, 291
photo of, 255
Lee, Kai-Fu, 281, 371
Lee, Tai Sing, 549
legal reasoning, 499
Lehigh University, 610
Leibniz, Gottfried, 28, 31, 53, 55
his Step Reckoner, 55
Lenat, Douglas, 446
photo of, 448
lensesderived by genetic programming,
628
Leonardo’s knight, 20
Lesser, Victor, 272, 572
Levesque, Hector, 441, 464
photo of, 442
Leviathan, 20
Levin, Leonid, 402
Levy, David, 252, 406
lexical functional grammars (LFGs),
534, 637
learning of, 538
Licklider, J. C. R., 161, 282, 339
his 1975 “Easter Message”, 311
his difficulties at DARPA,
310–313
his impressions of SRI’s CBC
project, 289
his succession at DARPA byDavid Russell, 290
his support of McCarthy, 161
photo of, 162
691

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

INDEX

LIFER and LADDER, 313–315

LUNAR, 243–244, 265, 271, 313

Lighthill, Sir James, 263
his report, 263, 343, 355, 401
likelihood, 53, 103
Lincoln Laboratory, 74–76, 81, 91,
151, 157, 161, 173, 270, 271,
282
Lindsay, Robert, 151line detectors, 172line drawings
in computer vision, 174
Linguistic Data Consortium, 535
linguistic levels, 141
LISP, 131, 135, 156, 164, 181, 202,
204, 207, 213, 223, 241, 292,
343, 365, 398, 427, 562
on Remote Agent, 603
Lisp machines, 343
Lisp Machines Incorporated, 343
list structures
examples of, 155
Llull, Ramon, 20, 31
Lo, Andrew, 626
local maxima
in GSAT, 464
local minima
in backprop, 508
Loebner Prize, 62
logic programming, 160, 203–205
Logic Theorist, see LT
logical inferences per second (LIPS),
350
logicists, 413, 435
Long Now Foundationand the Turing test bet, 649
long-term memory (LTM), 577
in SOAR, 583
Lovelace, Ada, 55, 64
low-level vision, 176
Lowerre, Bruce, 272, 276
lowest cost paths
Dijkstra’s algorithm, 398
found by A∗ , 221
LT, 79, 113, 116, 156
Lucas, John, 382, 393
Lukasik, Stephen, 310

Lungarella, Max, 392

692

MAC HACK, 252, 406

Machina speculatrix or tortoise
photo of, 45
Machina speculatrix or tortoise, 45
Machine Intelligence Company, 343
machine learning, 124, see learning
also, 495–527, 539
importance of data, 527
in checkers, 127–128
Machine Learning Toolbox (MLT),
356
machine translation, 146–150,
237–238, 398, 628
Russian to English, 148, 238
the Georgetown-IBM experiment,
153
machine vision, see computer vision
Mackworth, Alan, 456
his co-authored textbook, 491
photo of, 459
MADALINE, 98, 507, 525
magical number seven, 41–42
Malik, Jitendra, 547
Manning, Christopher, 535
on the use of A∗ in parsing, 233
Mansfield Amendment, 263, 265, 309,
343
marginin support vector machines, 526
Marin, Janet, 503
Markov assumptionin speech recognition, 274
Markov decision process (MDP), 521
Markov Logic, 643
Markov process, 488
Markov random fields, 490, 543, 548,
550
Markov tables, 221
Markov, Andrey Andreyevich, 274
Marr, David, 178, 335, 336, 540, 541,
608
his 2 21 -D sketch, 329
his book on vision, 329

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

his model of the cerebellum, 566
his primal sketch, 180
his third stage of vision, 335
Marr–Hildreth Operator, 178–180
mass spectrogram, 255
Massachusetts Institute ofTechnology, see MIT
MASTORfor translating speech between
English and Mandarin, 628
maximum-likelihood, 53
MCC, 354, 447
its consortium members, 354
its research areas, 354
McCammon, Richard, 299
McCarthy, John, 123, 159–161, 190,
201, 203, 407, 413, 417, 440
and LISP, 131, 156
and chess programs, 251, 595
and HLAI, 646
and Licklider, 161
and list-processing languages, 156
and nonmonotonic reasoning, 438
and the “monkey-and-bananas”
problem, 228
and the “mutilated
checkerboard”, 117
and the alpha–beta procedure,
127
and the frame problem, 222
and the Levy chess wager, 252,406
and time-sharing, 156, 157
as a co-organizer of theDartmouth Workshop, 77
as originator of the name“Artificial Intelligence”, 78
as part of project MAC, 161
at the 1958 Teddington
Symposium, 81
his founding of the Stanford AI
Lab, 157
his later reflections about theDartmouth Workshop, 80
his move to MIT, 157
his move to Stanford, 157

his opinion of clause form, 201
his paper “Programs with
Common Sense”, 82
his reason for interest in robots,
190
his situation calculus, 203
his use of predicate calculus, 200
joining Dartmouth College, 77
on the advantages of declarative
information, 82
photo of, 78
toward achieving HLAI, 649
McCarthy–Soviet computer chessmatch, 252
McClelland, James, 423
photo of, 423
McCorduck, Pamela
her book Machines Who Think,391
her co-authored book about theFifth Generation, 362
McCulloch, Warren, 34, 73, 92
photo of, 37
McCulloch–Pitts neuron, 34, 92
McCune, William, 201
McDermott, Drew, 413
McDermott, John, 301
meaningof a document, 471
Quillian’s view of, 137
meaning representation languages,
146, 207, 243
meaning-based Web search, 636–637
means-ends analysis
in GPS, 121
in SOAR, 583
in STRIPS, 223
medical systems
use of AI in, 623–625
MEDIPHOR, 291
Meltzer, Bernard, 160, 201
memistor, 98
MENACE, 158, 516
photo of, 159
MENS, 205
MENTAL, 205
693

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

INDEX

Merigan, Thomas, 292
meronymin WordNet, 445
meta-information, 441
meta-level reasoning, 571
Metamorphoses, 19
MH-1, 189
Michie, Donald, 158, 193, 516
and his company, Intelligent
Terminals, Ltd., 506
and Machine Intelligenceworkshops, 262
and the eight-puzzle, 220
and the Levy chess wager, 252,406
and the Lighthill Report, 263
his co-edited book on StatLog,527
his course given at Stanford, 504
on chess end games, 506
photo of, 159
micro-theories
in Cyc, 449
Microelectronics and Computer
Technology Corporation, see
MCC
Microsoft, 55, 229, 281, 538, 589, 618,
635, 636
Miller, George
his influence on behavior
programs, 42
his TOTE units, 42
his WordNet project, 444
on limits of immediate memory,
41
Miller, Randolph, 300
minimax strategy, 127
MINOS systems, 98–102, 507
Minsky, Marvin, 77, 119, 131, 161,
210, 475
and HLAI, 646
as a co-organizer of theDartmouth Workshop, 77
as a consultant to SRI, 213
as co-director of MIT AI Lab, 157
as part of project MAC, 161
694

Frames proposal, 499
his book The Emotion Machine,654
his co-authored book on
perceptrons, 262
his criticism of behaviorism, 40
his criticism of logic, 210
his Dartmouth paper, 82
his frames proposal, 209
his ideas about a program forproving geometry theorems,
80, 118–120
his opinion of LT, 117
his optimistic 1968 prediction,
163
his Ph.D. students, 122, 134, 152,
181, 262
joining MIT, 157
on Friedberg’s program evolution
experiment, 43
on importance of hierarchicallearning, 650
on intelligence being a kludge,
416
on Society of Mind, 572
on the mind being a machine, 382
on value of games for AI work,123
photo of, 78
missionaries and cannibals, 117
MIT, 78, 98, 109, 131, 148, 156–158,
161, 163, 167, 171, 173, 175,
181, 182, 185, 204, 213, 215,
223, 238, 251, 252, 269, 270,
327, 336, 338, 343, 359, 389,
413, 417, 419, 439, 440, 554,
603, 609, 626, 637, 640
its hand–eye research, 189–190
MIT Media Lab, 450
Mitchell, David, 464
photo of, 464
Mitchell, Thomas, 645
MITI, 349
MITRE Corporation, 323
modelsCopyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35.3

INDEX

of the cortex, 107, 323, 548, 549,
552, 554, 651
use of in computer vision, 335
Modha, Dharmendra, 651
modus ponens, 200, 201
MoGo, 599
monkey-and-bananas problem, 228
Monte Carlo methods
in Bridge, 599
in Go, 599
Montemerlo, Michael, 607
photo of, 608
moon rocks, 243
Moore, Andrew, 497
photo of, 497
Moore, J Strother, 160
MOPS, 209
Moravec, Hans, 231
morphology, 142
Morsch, Frans, 595
moving images, 552
MSYS, 333, 550
Multi-Agent Computing Environment(MACE), 573
multiagent systems, 572–576
Mumford, David, 549
Munson, John, 101, 224, 282
photo of, 101Musen, Mark, 624
music composition
automation of, 654
mutationsin genetic algorithms, 426
in genetic programming, 427
mutilated checkerboard, 117
MYCIN, 291–296, 407, 475, 624
Myers, Jack, 300
Myhrvold, Nathan, 55
n-grams, 538
Nagy, George, 104
naive physics, 440
NASA, 515, 600
NASREM, 566
natural language, 141

access to computer systems,
313–319
front ends, 313
generation of, 365
understanding of, 365, 449
natural language corpora, 535
natural language processing, 141–152,
237–249, 533–539
as part of the Strategic
Computing program,
371–372
participants in, 371
using memory-based learning,
498
Navlab, 511, 513
Nealey, Robert, 128
nearest-neighbor method, 103, 104,
173, 496, 525, 527
neats, 417
negation as failure, 436
Neocognitron, 548
NETtalk, 509
neural element, 35, 98, 525
as used in perceptrons, 92
Neural Information Processing
Systems (NIPS)
Conferences, 527
neural networks, 35, 74, 92–102,
423–424, 472, 491, 507–513,
522, 525, 527, 533, 562
applications of, 509
at SRI, 98, 213
feedforward, 548
neuron doctrine, 34
neurons, 34
Nevatia, Ramakant, 336
New Generation Computing, 351
Newell and Simon, 161
and IPL-V, 150
and IPL, 155–156
and chess, 123
and HLAI, 646
and production systems, 280, 577
and the 1956 DartmouthWorkshop, 78

695

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

and the alpha–beta procedure,
127
and the name “Artificial
Intelligence”, 79
and the physical symbol system
hypothesis, 64–66
on heuristics, 116
on linking symbols to world
objects, 387–388
on searching a space of symbol
structures, 113
photo of, 65
their LT program, 79–80, 113
their book Human Problem
Solving, 577
their GPS program, 121
their interest in models of humanproblem solving, 576, 577
their Turing Award, 113
Newell, Allen, 42, see Newell andSimon also, 76, 272, 295
and SOAR, 581
and chess, 76, 86
and speech-understanding
research, 272
and ubiquitous AI, 615
as chair of speech-understanding
study group, 270
attends 1954 RAND seminar, 76
his move to CMU, 157
his Ph.D. students, 223, 581
NEWTON, 440
Ng, Andrew, 472, 523, 542, 543, 638
photo of, 524
Nilsson, Nils, 42, 98, 159, 161, 183,
209, 295, 344, 382, 399
as a USAF Lieutenant, 216
his “Eye on the Prize” article,646
his book on pattern recognition,
104, 262
his challenge problem for AI, 660
his co-authored AI textbook, 413
his employment test, 648
his sabbatical at MIT, 419
on HLAI, 649
696

NLP-DBAP, 318

No Hands Across America, 530
NOAH, 230, 287
noise
added to training samples, 526
non-numeric data, 500
nondeterministic polynomial (NP),402
NONLIN, 230
nonmonotonic reasoning, 435–438
in PLANNER, 436
in PROLOG, 436
in STRIPS, 436
nonplayer characters (NPCs), 619
nonsymbolic methods, 66, 108
nonterminal symbols
in a grammar, 143
Norris, William, 354
Northwestern University, 253
Norvig, Peter, 488, see Russell andNorvig also
noughts and crosses, see tic-tac-toe
Novak, Gordon, 152
Novikoff, Albert, 97
NP-complete, 461
NSS, 156
Numenta, 554
O-PLAN, 231

Oakley, Brian, 355
object locationin scenes, 335
object recognition
in images, 335
using templates, 335
Odin
entrant in Urban Challenge, 609
Office of Naval Research, see ONR
Ohlander, Ronald, 340, 370, 372
Oki Electric Industry Co., 629
Olsen, Kenneth, 75
Olshen, Richard, 506
Omohundro, Stephen, 653
ONCOCIN, 624
ONR, 97, 160
ontology, 446

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

ontology languages, 443
OpenCyc, 450

operators
in GPS, 121
in SOAR, 582
OPI, 643
OPS family of languages, 301
optimal policy
in reinforcement learning, 518
Oregon State University, 643
Orkin, Jeff, 620
Osindero, Simon, 551
outdoor scenes, 541
overcounting of evidence, 295, 476
overfitting, 506
Ovid, 19
Owen, Kenneth, 355
OWL, 443
paired-associate learning
by EPAM, 502
PAL
personalized assistants, 642
Pandemonium, 83–85, 91, 107, 393,
548, 562
Panoramic Research, 172
Papert, Seymour
and the “summer vision project”,
175
and the Levy chess wager, 252
as co-director of MIT AI Lab, 157
his co-authored book on
perceptrons, 262
on toy problems, 71, 265
parallel distributed processing (PDP)
systems, 423
parallel inference machines (PIMs),
351
parallelism, 562
PARC, 246, 248, 443, 637
Park, Charles, 295
parse trees, 243–245, 247, 318, 536
for images, 555
multiple, 146, 534
probabilities of, 536
parsersfor PCFGs, 538
parsingdata-oriented, 538
parsing algorithms, 534
partially observable Markov decision
processes (POMDPs), 521
particle filters, 490, 546, 550
PASCAL, 506
Pascal, Blaise, 54
portrait of, 54
pattern matcher
in ACT-R, 579
pattern recognition, 74, 81, 83, 89–109
applied to photo interpretation,
105–108
Paul, Richard, 192, 198
PDP group, 423, 529
PDP-1 computer, 157
PDP-10 computer, 157, 192, 216, 239,
292
PDP-6 computer, 157, 252
Pearl, Judea, 296, 477
photo of, 478
Penrose, Sir Roger, 383
photo of, 384
perceptrons, 92–97
alpha, 96, 507, 526
back-coupled, 96
cross-coupled, 96
series coupled, 96
Pereira, Fernando, 315
Perrault, C. Raymond, 575
perspicuous grouping, 390
Pfeffer, Avi, 486
Pfeifer, Rolf, 392
phenomenology, 392
Philco, 105
phonesas speech sounds, 268
network of in HARPY, 277
phonetic alphabets, 268
phonetics, 142
phonology, 142
physical symbol system hypothesis,
65, 66, 76, 387, 419, 423
Piaget, Jean, 45, 157
697

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

Pierce, John, 150, 271, 398
Pilot’s Associate, 362–364
Pingle, Karl, 186, 190
Pitts, Walter, 34, 92
plan execution, 224
Plankalkül, 60, 123
PLANNER languages, 205, 223, 241,
242, 436
planning
as a SAT problem, 466
by STRIPS, 216, 222–224
communicative actions, 575
hierarchical, 229–231
in autonomous vehicles, 604
in Remote Agent, 602
of communicative actions, 572
of robot motions, 219
Poggio, Tomaso, 554
poker, 598
policies
in reinforcement learning, 518
optimalin reinforcement learning, 518
polynomial time and complexity, 401
Pomerleau, Dean, 510, 513
pons asinorum, 119
Poole, David, 486
his co-authored textbook, 491
POP-2, 160, 164, 196
Pople, Harry, 300
Popplestone, Robin, 160
Powerset, 538
its acquisition by Microsoft, 636
pragmatics, 142
Prawitz, Dag, 200
preconditions
in STRIPS, 223predicate calculus, 33, 76, 82, 200,
201, 222, 243, 448, 486, 507,
569, 575
prediction
by HMMs, 490
predictionsof AI achievements, 633
prioritized sweeping, 521

698

probabilistic context-free grammars(PCFGs), 536
learning of, 537
probabilistic dependencies, 475
probabilistic graphical models, 476,
490
probabilistic inference, 480
probabilistic reasoning, 475
probabilistic relational models(PRMs), 486–488, 507
applications of, 488
probabilistic terrain analysis (PTA)
algorithm
in Stanley, 608
probabilities of sentences, 536
probability theory, 52–53, 475
problem spaces
in SOAR, 582
procedural embedding of knowledge,
205
procedural knowledge, 199, 205, 242,
439, 580, 584
in ACT-R, 579
in SOAR, 584
procedural networks, 230, 287
Procedural Reasoning System (PRS),
569–571
applications of, 571
production rulesin ACT-R, 580
production systems, 577, 581
in SOAR, 583
Production Systems Technologies, 303
productions, 280, 577
Project Möbius, 645
Project MAC, 161
PROLOG, 203–205, 316, 343, 350, 351,
436, 507
Prometheus driverless-automobile
project, 604
propositional calculus, seepropositional logic
propositional logic, 32, 200
for expressing IF–THEN rules,
507
proving theorems in, 42

Copyright c 2010 Nils J. Nilsson
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All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
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35.3

INDEX
solving problems using, 460–466

PROSPECTOR, 295–300, 475

its identification of a porphyrymolybdenum deposit, 298
PROTEUS, 371
Protosynthex, 152
PUFF, 624
PUNDIT, 371
pure splits
in decision-tree learning, 505
purposive vision, 337, 540
Putnam, Hilary, 381, 461
Pygmalian, 19
Q-learning, 518
QA3, 201, 202, 222

qualitative models, 440
qualitative physics, 440
qualitative reasoning, 439–441
question answering, 134, 150–152
Quillian, Ross, 136, 141, 156, 199,
205, 387
Quinlan, J. Ross, 503, 529
photo of, 504
Quintus, Inc., 343
R1, 301

Rabinow, J., 90
RADC, 97, 215
RADIUS, 370
Raibert, Marc, 637
photo of, 638
RALPH, 513
Ramón y Cajal, Santiago, 34
photo of, 35
Ramachandran, V. S., 337
RAND Corporation, 76, 113, 156,
157, 161, 389, 391
Randall, Neil, 105
Raphael, Bertram, 134, 156, 199, 202,
213, 215
his SIR program, 134–136, 141,
150, 202, 210
his book on AI, 262
his work on A∗ , 220
RAX, 602

RCA, 269, 310
Reactive Action Packages (RAPs),
563
real-time control systems (RCSs) ofJames Albus, 565
Reboh, René, 296
Rechenberg, Ingo, 44
recommending systems, 618–619
use of collaborative filtering, 619
use of content-based filtering, 619
recursionin GPS, 122
in LISP, 156
recursive backtracking, 462
recursive functions, 56
as a basis for LISP, 156
recursive transition networks, 245
Reddy, Raj, 177, 192, 272, 280, 282
his joining CMU, 272
photo of, 193
reference model architecture, 565
region findingin computer vision, 225
reinforcement learning, 40, 515–524
in animals, 523
some applications, 523
Reis, Victor, 373
Reiter, John, 296
Reiter, Raymond, 437
relational data mining, 507
Remote Agent (RA), 600–603
representations, 117
ResearchCyc, 450
resolution, 201
in Cyc, 449
restaurant script, 207
Rete algorithm, 301, 627
Reuters NewsScope Archive, 626
rewards
in reinforcement learning, 519,
520
Ridgway, William, 98, 507
Rindfleisch, Thomas, 341, 625
Risch, Tore, 296
Riseman, Edward, 370
Rissland, Edwina, 499
699

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

INDEX

photo of, 499
Robbins algebra, 201
Robbins, Herbert, 201
Roberts Cross operator, 174, 177
Roberts, Bruce, 210
Roberts, Lawrence, 173
and ship tracking, 319
and speech-understanding
research, 270, 271
and the Arpanet, 173
at DARPA, 270, 309
Robinson, Alan, 201, 204
RoboCup, 573
robot competitions, 641
robot motion planning, 219
Robotics Institute of CMU, 513, 541
robots, 600–612
behavior-based, 417–419
factory, 47
general purpose, 638–642
in the military, 653
legged, 637
mobile, 213–232
that play soccer, 573, 576
Rochester, Nathaniel, 77
and geometry theorem proving,
118, 160
as co-organizer of the DartmouthWorkshop, 77
Rogers, Seth, 584
Roland, Alex, 360–374
Rome Air Development Center, see
RADC
Rosen, Charles, 98, 99, 213, 343, 507
photo of, 218
Rosenberg, Charles, 509
Rosenblatt, Frank, 92–97, 262, 424,
507, 526
his consulting at SRI, 98
his Ph.D. students, 97, 104
photo of, 93
Rosenbloom, Paul, 581
photo of, 582
Rosenfeld, Azriel, 370, 555
Rosenschein, Jeff, 576
Roszak, Theodore, 396–398, 644
700

rote learning
in Samuel’s checker-playing
program, 127
Roussel, Philippe, 204route finding in maps, 618
rulesin PROSPECTOR, 296
rules of inference, 200, 201
RulesPower, Inc., 303
Rumelhart, David, 423, 424, 508
photo of, 423
IRUS, 318
Russell and Norvig
their AI textbook, 280, 294, 457,
478, 490, 527, 568
Russell and Whitehead’s Principia
Mathematica, 113
Russell, David, 290, 313
Russell, Stuart, 571
Rutgers University, 301
Sacerdoti, Earl, 230, 343
SAD SAM, 151
SAIL, 157, 190, 192
photo of, 158
SAINT, 123
Salford Systems, 529
Salton, Gerard, 473
Samuel, Arthur, 78, 124, 156, 251,
282, 399, 516
and the alpha–beta procedure,127
his checkers program, 123–128
his interest in machine learning,124
his learning method in checkers,
127–128
photo of, 125
Sandstorm
entrant in Grand Challenge, 605
Sapir, Edward, 535
Sastry, Shankar
on face recognition, 629
SAT problems, 461–466
solving of using local searchmethods, 463–466

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

solving of using systematic
methods, 461–462
SATPLAN, 466
Saunders, Rin, 364, 365
Saxena, Ashutosh, 543
scaling dimensions, 496
scene analysis, 176
using models, 335–338
scenesreasoning about, 333–334
Schütze, Hinrich, 535
Schaeffer, Jonathan, 124, 595, 599
photo of, 596
Schaeffer, Stephanie, 597
Schank, Roger, 206, 207, 209, 417, 499
photo of, 206
Schapire, Robert, 525
scheduling systems, 625–626
Scheinman, Victor, 192
Schickard, Wilhelm, 54
portrait of, 54
Schmidt, Rodney, 231
Schuler, Karin, 446
Schultz, Alan, 580
Schwartz, Jacob, 374, 402, 405
and artificial superintelligences,647
on the consequences of HLAI, 652
scientific community metaphor, 572
SCORPIUS, 370
Scrabble R , 599
Scripts, 207–209
scruffies, 417
SDC, 152, 161, 270, 271, 282, 318, 371
SDS 910 computer, 100
SDS 940 computer, 202
searchexponential nature of, 399
search process, 113
search tree, 117
breadth and depth of, 399
for checkers, 125
for robot navigation, 220
Searle, John, 383, 388, 575
photo of, 385
SEE, 181

See5, 506

Sejnowski, Terrence, 172, 337, 509,
540
photo of, 510
Self-Aware Systems, 653
self-organizing systems, 51, 160
Selfridge, Oliver, 74, 76, 89, 91, 103,
157, 562
at the 1956 DartmouthWorkshop, 78
at the 1958 Teddington
Symposium, 81
his 1954 seminar at RAND, 76
his “Pandemonium”, 83–85
photo of, 75
Selman, Bart, 461, 464, 466
photo of, 464
semantic analysis
in TEAM, 318
of a sentence, 146
semantic knowledge
in SOAR, 584
semantic networks, 33, 136–138,
205–207, 248, 301, 441–450
partitioned, 296
semantic representations, 131
semantics, 142, 242
sensor networks, 572
distributed, 573
Seo, Hyojung, 523
separating boundaries, 525
session on learning machines in 1955,73
shadows and cracks in scenes, 185
Shafer, Glenn, 296, 304
Shakey, 213–229, 285, 392, 569, 638
experiments with, 228–229
funding difficulties, 229
in Robot Hall of Fame, 216
its intermediate level programs
influence of George Miller, 42
its three-layer architecture, 563
its vision routines, 224–226, 337
Shanahan, Murray, 438
Shannon, Claude, 123, 189

701

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35

INDEX

as a co-editor of Automata
Studies, 78
as a co-organizer of theDartmouth Workshop, 77
development of switchingcircuits, 58
his use of heuristics in chess, 117
photo of, 59
use of his definition of amount of
information, 505
shape from shading, 327, 328
Shapiro, Stuart, 205
Shawe-Taylor, John, 527
Shepherd, Roger, 170
Shirai, Yoshiaki, 185
short-term memory, 577
in SOAR, 583
Shortliffe, Edward (Ted), 291
SHRDLU, 238–242
SIAP, 321
SIGART, 262
signal detection, 52
Simmons, Robert, 152, 205
Simon, Herbert, 42, see Newell andSimon alsoand “blackboards”, 280
and his IBM 6500, 157
his 1957 predictions, 163, 633
his biographical sketch of Allen
Newell, 76
his continuing work on EPAM, 502
his MIT talk attended by the
Dreyfuses, 389
his Ph.D. students, 136, 157, 228,502
his summary of Newell’s paper onchess, 76
on hand simulating LT with hischildren, 80, 386
on inventing a “thinking
machine”, 80on the physical symbol system
hypothesis, 66
simple cells
in visual cortex, 171
Simpson, Robert, 340, 370
702

Singer, Jonathan, 190
Singular Value Decomposition (SVD),
470
singularity, 647
Singularity Institute for Artificial
Intelligence (SIAI), 648
SIPE-2, 231
SIR, 134–136, 141, 213, 436
situation board
in HASP/SIAP, 321
situation calculus, 83, 202–203, 222
Skinner, B. F., 39
on explaining verbal behavior, 40
SL-resolution, 204
Slagle, James, 123, 156
his book on AI, 264
Slate, David, 252sliding tile puzzles, 114, 402, 404
Sloman, Aaron, 644
on consciousness, 654
smart tools, 623–630
SMARTPAL V
the Yaskawa robot, 641
Smith, Brian, 571
Smith, Reid, 573
smoothingby HMMs, 490
SNePS, 205
SOAR, 577, 581–584
its applications, 582, 584
Sobel Operator, 177–178
Sobel, Irwin, 177
soccer-playing robots, 573, 576
soft computing, 416
sombrero function, 179
speech acts, 575
speech recognition, 267–282, 488
as part of the Strategic
Computing program,
370–371
by Raj Reddy at Stanford, 192
speech understanding
goals of the DARPA study group,
270
Speech Understanding Research
program, 312, 360

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

speech waveform, 268
Speech-Understanding Study Group,
270–271SPEECHLIS, 271
SPHINX, 281, 371
Spiegelhalter, D., 527
spreading activation, 137
Sputnik, 161
SRI, 42, 90, 97, 161, 167, 172, 183,
201, 230, 231, 270, 271, 281,
313, 329, 338, 344, 350, 366,
369–371, 526, 628, 642
its CBC project, 285–290
its MINOS systems, 98–102
its NLP projects, 313–315,
318–319
its PROSPECTOR project,295–300
its Shakey project, 213–229
SRI International, see SRI
STAIR
a Stanford robot, 638, 651
learning to pick up objects, 639
Stanford AI Lab, see SAIL
Stanford Cart, 231–232
Stanford Research Institute, see SRI
Stanford University, 83, 98, 109,
157–159, 161, 167, 172, 177,
185, 201, 205, 206, 213, 220,
230, 231, 251, 253, 303, 319,
333, 336, 338, 372, 446, 486,
503, 506, 523, 542, 552, 605,
609, 610, 624, 638
its hand–eye research, 190–193
the Dendral Project, 255–259
the Mycin Project, 291–295
Stanhope Demonstrator, 30–31
Stanhope, Charles, 29–31
Stanley
entrant in Grand Challenge, 605,
607–609
its sensors, 607
on Beer Bottle Pass, 606
stare decisis, 498
State University of New York,
Buffalo, 205

statesin reinforcement learning, 517
statistical NLP, 535–539
statistical regression
use of in nearest-neighbormethod, 497
statistical techniquesin pattern recognition, 102–104
in speech recognition, 273
statisticians
their collaboration with AI
researchers, 506
statistics, 52–53
StatLog, 527
Stefik, Mark, 363
STeLLA, 516
stemming, 467
stereo vision, 169
stereopsis, 169
Stone, Charles, 506
Stone, Philip, 503
Stork, Davidhis co-authored textbook, 515
Stottler Henke, 625
Strachey, Christopher, 124
Strat, Thomas, 605
Strategic Computing program, 281,
345, 359–375
assessment of, 373–375
its major projects, 362–369
its plan, 359–362
its technology base, 369–373
STRIPS, 222–224, 228, 230, 241, 436,
466, 575
strong and weak AI, 388, 399, 429
subgoals, 76
in SOAR, 583
in STRIPS, 223
in expert systems, 577
in Gelernter’s geometry program,
118
subjective probabilities, 480
subproblems
in GPS, 121
subspace, 470
subsumption architectures, 419, 563
703

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

Summer Vision Project at MIT, 175
superpixels, 542
support vector machines (SVMs), 107,
525, 657
support vectors
in support vector machines, 526
Sutherland, Georgia, 285, 296
Sutherland, Ivan
as director of IPTO, 215
Sutton, Richard, 251, 516, 523
photo of, 516
syllogism
form of, 27
Symantec, 315
symbol structures
examples of, 113, 155
for representing declarativeknowledge, 199
in the physical symbol system
hypothesis, 387
use ofin LT, 79, 113
in analogy program, 133
in GPS, 121
in the eight-puzzle, 114
symbol systems
Turing machines, computers, 65
Symbolics, 343, 365
symbols
examples of, 65
lists of, 113
on military maps, 99
use ofin AI reasoning, 28
in Aristotle’s syllogism, 27
in genetic algorithms, 44
in grammar rules, 142–144
synapse, 34
synsets
in WordNet, 445
syntactic categories, 142
syntactic structure, 142
syntax, 142, 242
Syntelligence, 303
System Development Corporation, see
SDC
704

Systems Control Technology, Inc., 321
Systran, 237
table of differencesin GPS, 122
TacAir-SOAR, 584
tag words
on videos, 636
Talos
entrant in Urban Challenge, 609
Tate, Austin, 230
Taube, Mortimer, 383
taxonomic hierarchies, 436, 441, 446,
448
in PROSPECTOR, 296
Taylor, C. C., 527
TCP/IP, 359
TD-GAMMON, 522
TDUS, 287
TEAM, 318–319
Teh, Yee-Whye, 551
Teknowledge, 303, 372
teleo-reactive programs, 419–422, 577
an example, 420–422
and the Action Tower, 567
influence of George Miller, 42
their motivation, 51
their simularity to RAPs, 563
temporal-difference learning, 522
TEMPORISTM

for scheduling, 626
Tenenbaum, J. Martin, 329, 333, 338,
550
photo of, 331
terminal symbols
in a grammar, 143
TerraMax
entrant in Urban Challenge, 606
Tesauro, Gerald, 522
TextRunner, 645
TherapyEdge HIV

for HIV, 624
Thielscher, Michael, 438
Thomas, Lewis, 396
Thor time-sharing system, 157
Thorndike, Edward, 516

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
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35.3

INDEX

Thorne, James, 245
Thorpe, Charles, 370
three-dimensional representationin computer vision, 174
three-layer control architectures, 216,
563
thresholds
in neural networks, 508
replacement of, 508
of neural elements, 35
Thrun, Sebastian, 607
his prediction about driverlessautomobiles, 611
photo of, 608
tic-tac-toe, 158, 516
Tick, Evan
his reflections on FGCS, 352
Tilden, Mark, 425
Tinsley, Marion, 596
Tolhurst, David, 172
top-down search, 146
Torrance, Markhis role in teleo-reactive
programs, 419
tortoise, see Machina speculatrix
TOTE units, 42, 221
toy problems, 71, 114, 263, 265, 400,
533
tracking moving objects, 544–546
Trafton, Greg, 580
training procedures, 551, 554
for neural networks, 96–98, 100,
507, 509
in ALVINN, 512transition network grammars,
243–245, 315, 534
traveling salesman problem, 426
tree adjoining grammars (TAGs), 534
tree banks, 535, 537
trial-and-error learning, seereinforcement learning
triangle table, 224
trihedral solids
as analyzed by Huffman, 183
triple-tower archtecture, 567
truth-maintenance systems, 438

Turing Centerat the University of Washington,
645
Turing machine, 56, 58
as a symbol system, 65
Turing test, 61–63, 648, 649
betting on it, 649
Turing, Alan, 56, 123, 381
and HLAI, 646
at Bletchley Park, 158
his Child programme, 64, 650
his universal machine, 57, 59
his views on possibility of AI, 61,
63–64
photo of, 57
tutorials
at AAAI and IJCAI conferences,344
ubiquitous AI
its everyday applications,
615–621
Uchida, Shunichi, 350
ultraintelligent machines, 647
uncertainty, 414, 475
understanding
definitions of, 134of natural language, 141, 205,
238–249, 365
of speech, 267–282
United Space Alliance, LLC, 626
universal subgoalingin SOAR, 583
Universität der Bundeswehr, 547
University of Alberta, 595, 598
University of Amsterdam, 538
University of Birmingham, 644
University of British Columbia, 456,
486, 559
University of California, Berkeley, 44,
215, 262, 271, 344, 389, 524,
547, 629
University of California, Los Angeles,
404, 477
University of California, San Diego,
423
705

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35

INDEX

University of California, Santa Cruz,
183, 654
University of Colorado, 469
University of Edinburgh, 158, 160,
193, 201, 204, 230, 245, 262,
315, 500
University of Illinois, 124, 629
University of Indiana, 425
University of Leeds, 547
University of Manchester, 124
University of Maryland, 107, 337, 366,
370, 555
University of Massachusetts, 370–372,
573
University of Michigan, 44, 256, 407,
620
University of New Hampshire, 190
University of Oxford, 124, 545
University of Pennsylvania, 371, 535,
610
University of Pittsburgh, 300
University of Rochester, 338, 424
University of Sheffield, 644
University of Southern California,
265, 573
University of Sussex, 185
University of Sydney, 503
University of Tel Aviv, 624
University of Texas, 152, 172, 201,
205, 206, 296, 441
University of Toronto, 443, 575
University of Washington, 261, 645
University of Wisconsin, 205
unsupervised learning, 548, 551, 552
USC-ISI, 265, 371, 443
valuation numbers
in reinforcement learning, 518
VaMoRs-P, 563
VaMP vehicle, 547, 604
van Gelder, Timothy, 424
van Melle, William, 294
Vaucanson, Jacques, 21
vectors
definition of, 466
in nearest-neighbor method, 496
706

in pattern recognition, 95, 466,
513
representing images as, 635
representing text as, 467
similarity between, 467
Veloso, Manuela, 573
photo of, 574
VerbNet, 446
Vhayu Technologies Corporation, 626
Vhayu Velocity

for automatic trading, 626
VideoSurf, 636
Vidoni, Frédéric, AutomatierCinéticien-Mechanical Arts,
22
Vinge, Vernor, 647
Vision Zero
the Swedish Road Safety Bill, 617
Viturbi algorithm, 490
von Neumann architecture, 60, 393,
561
von Neumann, John, 60, 73, 113
Waibel, Alex, 281
WALKSAT, 464, 465
Walter, Grey, 44, 54
his Machina speculatrix or
tortoisephoto of, 45
his Machina speculatrix or
tortoise, 44, 47, 213, 417
photo of, 45, 51
Waltz, David, 185
his analysis of line drawings, 185
Wang, Hao, 200
Warren, David, 315
water pump assembly at Stanford, 192
Watkins, Christopher, 518
Watt, James, 49
weak methods
in SOAR, 583
Weaver, Warren, 148
Webber, Bonnie, 243
WebFOCUS, 627
Wefald, Eric, 571
weights

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937

35.3

INDEX

in neural networks, 507
in perceptrons, 92
on features in checkers, 127
Weiss, Sholom, 301
Weizenbaum, Joseph, 150, 394
Werbos, Paul, 508
Weyhrauch, Richard, 571
Wichman, Bill, 190
Widrow, Bernard, 98, 159, 507
Widrow–Hoff algorithm, 98
Wiener, Norbert, 49, 78, 148
photo of, 51
Wiesel, Torsten, 171
Wilkins, David E., 231
Wilks, Yorick, 644
Williams, Brian, 603
Williams, Ronald, 424, 508
Winograd, Terry, 210, 238, 248, 533
his move away from NLP, 242
his work on SHRDLU, 238–242
photo of, 239
Winston, Patrick, 185, 190
Wolf, Helen (Chan), 172
Wong, A, 473
Woods, William, 243, 248, 282, 313,
442
WordNet, 444–446
wordnets, 446
working memory (WM), 280, 577
in SOAR, 583
workstation, 350
World Computer Chess tournaments,
253
world knowledge
needed for machine translation,
149
World Wide Web, 403, 449, 466, 589,
652
Wundt, Wilhelm, 38

Yovits, Marshall, 160
Zadeh, Lotfi, 304, 414
zChaff, 462
Zhu, Song-Chun, 555
Zuse, Konrad
his Z3 computer, 59
invention of stored program, 59

XCON, 301

Xerox, 246, 443, 637
Yang, C. S., 473
Yaskawa Electric Corporation, 640
YouTube, 636
707

Copyright c 2010 Nils J. Nilsson
http://ai.stanford.edu/∼nilsson/
All rights reserved. Please do not reproduce or cite this version. September 13, 2009.
Print version published by Cambridge University Press.
http://www.cambridge.org/us/0521122937