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ARI Research Note 86-101

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THE DESIGN AND DEVELOPMENT OF AN INTELLIGENT PLANNING AID

Stephen J. Andrlole

Perceptronlcs

for

Contracting Officer's Representative

Judith Orasanu

BASIC RESEARCH LABORATORY

Milton R. Katz, Director

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MAR 2 5 1987 Hi

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Ü. $. Army

Retearch Institute for the Behavioral and Social Sciencet

July 1986

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AF Research Hot« 86-101

t. OOVT ACCIUWN NO.

4. TITLl (mt* »uMII»)

The Design and Development of

an Intelligent Planning Aid

V. AuTMonr«)

Stephen J. Andrlole

i. PBRPOSMINO OeOAMIlATION NAME AN0 AOORCM

Percepttonlcs

21111 Erwin Street

Woodland Hills. CA 91367-3713

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U.S. Army Research Institute for the Behavioral

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17. OlSTNtRUTION ITATtMBNT (»I *• aAMTMt M, If mamml I

IS. tURRLCMKNTARY NOTSS

Judith Orsssnu, contracting officer's representative

I», KKY voees o

Decision Making

Tactical Planning

Taxonomy

Problem Solving

ii M ifrntitr *r MM* I

TACPLAN

Graphics Interface

Knowledge Representation Strstegles

,. This research note discusses progress In developing an Intelligent tactical

planning aid. It documents the development of a decision-making, planning, and

decision-aiding analytical framework comprising a set of models, s generic task/

behavior taxonomy, and an Inventory of decision-aiding methods. Six U.S. Army

officers participated In two videotaped experiments designed to capture the

essence of tactical military planning. Results suggested that tactical planning

at the Corps level was goal-oriented and hierarchical, that tactical planning 1 o r^

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ARI Research Note 86-101

20. Abstract (continued) V

^

/ tasks were essentially medlatlonal In nature, and that the "best" methods

available to satisfy the requirements could be found in multi-attribute

utility assessment, artificial Intelligence, and interactive graphic display

techniques.

The TACPLAN aid assists tactical military planners at the Corps level by

developing new defensive plans via the use of an intelligent tactical planning

template which will elicit Judgements from the planner, link the Judgements tc

knowledge bases related to the planning process and the problem at hand, and

permit the planner to draw his candidate plans through an interactive video

disc-based graphic interface.

Phase Two research goals Include the development of these environmental and

challenger capabilities, the development of additional template dimensions,

the testing of an evaluation at the Army War College, and documentation.

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ACKNOWLEDGEMENTS

We vish to acknowledge the support and encouragement of Drs.

Robert M. Saamor and Judith Orasanu of the Army Research

Institute's (ARI's) Office of Basic Research. They have been the

source of administrative help and patience, and a lot of

technical insight) many of the insights in this report are

directly traceable to ideas and suggestions made by Drs. Sasmor

snd Orasanu. We would also like to thank Dr. David Regal, also

of ARI, for a number of excellent ideas and suggestions. We must

also note the valuable help of the following U.S. Arm; officers

who participated in two experiments at the Army War C^l^ege in

Carlisle, Pennsylvania in January 19641 Col. Frederick W. Kulik,

Col. Joseph H. Schwer, Col. Wayne P. Halstead, Lt. Col. Pobert

B. Magruder, Lt. Col. Carl F. Ernst, and Lt. Col. Kyron F.

Curtis. Col. Robert Moss also helped clear the way for the

experiments to take place, while Major General Thomas F. Healy,

Commandant of the Ar.^y War College, sanctioned the use of the

Army War College and its expert planners. Without all of the

above named sponsors and officers this research could not have

been conducted. Finally, we would like to thank Drs. Anos

Freedy, Jay Uhlaner, and Yee-Yeen Chu—all of Perceptronics—for

their help and support.

ill
SUMMARY

This report discusses progress made toward the development of an

intelligent tactical planning aid. It documents the folloving

steps:

e The development of a decision-making, planning#

and decision-aiding analytical framework comprised of a net of decision-making and planning models, a generic task/behavior taxonomy, and an inventory of decision-aiding methods;

e The conduct of a set of tactical planning experiments, conducted at the Army War College, designed

to capture the essence cf tactical planning;

e The development of an analytical framework based

on the results of the experiments comprised of

a model of the tactical planning process, a list

of tactical planning tasks, and a list of aiding

methods most likely to satisfy those tasks;

e The development of an aiding functional description, comprised of tasks and sub-tasks, goals and

sub-goals, for the. "TACPLAN" aid; and

e The development of a concept of operation for

TACPLAN.

The initial analytical framework comprised of decision-making and

planning models, a generic task/behavior taxonomy, and a list of

csndidate aiding methods was based on a great deal of work

conducted during the past decade. A number of characteristics of

this work are worth noting. First, by and large, it resulted

from "technology push" and not "requirements pull." Models and

computer-based aids were developed because the technology

underwrote their development, not because they were inherently

valid. At the same time, many of the task and methods taxonomies
that evolved were comprehensive end anchored in some defensible

notions about the range of analytical requirements and useful

tools.

Our requirements analysis involved six U.S. Army officers who

participated in two videotaped experiments designed to capture

the essence of tactical military planning. The results of these

experiments suggested that tactical military planning at the

Corps level vas goal-directed and hierarchical« that tactical

planning tasks were essentially mediational in nature, and that

the "best" methods available to satisfy the requirements could be

found in multi-attribute utility assessment, artificial

intelligence, and interactive graphic display techniques.

The TACPLAIJ aid reflects this tasks/methods assessment. It will

assist tactical military planners at the Corps level develop new

defensive plans via the use of an intelligent tactical planning

template, which vi.'il elicit Judgments from the planner, link the

judgments to knowledge bases about the planning process and

problem at hand, and permit the planner to draw his candidate

plans through an interactive video disc-based graphic interface.

The TACPLAN prototype aid will demonstrate several problem

structuring and problem-solving approaches, including the use of

"burdenless analytical spreadsheets," "graphic equivalent

displays," and "rules" expressed both as constraints on the

planning process and positive substantive guidelines to rapid yet

diagnostic planning.

1
Our research has also enabled us to develop a more elaborate

TACPLAN architecture, which includes the integration of

environmental variables, such as the smount of time available to

the planner, and the development of a tactical "challenger,"

which might be used to accelerate the planning process or, on

other occasions, query the planner about his plan by example,

alternative plan displays, and the presentation of substantive

planning rules.

Phase two research goals include the development of these

environmental and challenger capabilities, the development of

additional template dimensions (perhaps at different command

levels), testing and evaluation at the Army War College, and

documentation. Phase two research will also cor. inue development

of the generic problem structuring methodology that emerged from

our requirements analysis/knowledge engineering conducted at the

Army War College.

vil
CONTENTS

ACKNOWLEDGEMENTS ii

SUMMARY iü

1.0 INTRODUCTION 1

1.1 Overview of Planning and Decision-Aiding Problems 1

1.2 Overview of Project Plan and Objectives 1

2.0 PROGRESS ON THE CONCEPTUAL DESIGN OF AN INTELLIGENT

PLANNING AID 6

2.1* The Initial Analytical Framework 6

2.1.1 Analytical Perspectives on Decision-Making 6

2.1.2 Analytical Perspectives on Planning 8

2.1.3 A Generic Behavior Taxonomy 10

2.1.4 A Methods Inventory 12

2.1.5 The Research and Development Plan 17

2.2 Planning Requirements Analysist The Carlisle

Experiments 18

2.2.1 The Tactical Scenario 20

2.2.2 The Planning Experiments 21

2.2.3 Results 27

2.3 The Revised Analytical Framework 33

2.3.1 Tactical Military Planning as Goal-Directed 33

2.3.2 Planning Tasks 34

2.3.3 Planning Methods 36

2.4 TACPLAN Functional Design 43

2.4.1 The Applications Prototyping Strategy 43

2.4.2 Intelligent Planning Templates 46

2.4.3 TACPLAN Functional Description 50

2.4.4 TACPLAN Concept of Operation 52
3.0 PROGRESS ON THE DEVELOPMENT OF THE TACPLAN PROTOTYPE 59

3.1 Delivery System Configuration 59

3.1.1 "Personal" Planning Aid 59

3.1.2 The Graphics Interface 60

3.2 Status of TACPLAN Prototype 62

3.2.1 Menu Development 62

3.2.2 Analytical Routines 63

3.2.3 Procedural and Substantive Rule Development 63

3.2.4 IBM/PC/XT Link to Interactive Video Disc 63

3«3 The Iterative Strategy 64

4.0 PHASE TWO RESEARCH, DESIGN, AND DEVELOPMEKV PLAN 66

4.1 Iterative Analytical Framework 66

4.1.1 Environmental Variables 66

4.1.2 The Embedded -Challenger" 72

4.1.3 Additional Command Levels and Planning

Functions 75

4.1.4 Multiple Knowledge Representatici Strategies 78

4.2 Testing and Evaluation 79

4.2.1 Testing and Evaluation at the Army War

College 80

4.2.2 Testing and Evaluation in Operational

Environments 81

4.3 TACPLAN Documentation 81

5.0 CONCLUSION 83

NOTES 84

REFERENCES 85

APPENDIX At PROGRESS ON THE DEVELOPMENT OF A GENERIC

PROBLEM STRUCTURING METHODOLOGY 50

APPENDIX B: TACPLAN CONCEPT OF OPERATION 3 09
1.0 INTRODUCTION

t

1.1 Overviev of Planning and Decision-Aiding Problems

It would be a gross understatement to declare the design and

development of interactive computer-based "decision aids" a

growth industry. The last decade has seen the design and

development of countless information management, forecasting,

evaluation, and option selection systems, systems that today are

all more or less regarded as "decision aids." It is also safe to

say that initial decision aid investments were decidedly

technology driven and not specified by explicit requirements.

This distinction between "technology push" and "requirements

pull" explains as much about early investment decisions as it

does technical progress. Where would the design and development

of interactive decision aids be without minicomputers,

microcomputers, and affordable interactive graphics?

The early investments in decision-aiding technology were

instructive and suprisingly productive. Without explicit regard

for requirements an impressive number of successful technology

transfers occurred and were documented as much to the amazement

of the systems designers as the transfer targets (Andriole, 1961;

Kelly, 1981). Yet there were serious problems with the "push

strategy." More often than not, prospective users were alienated

from the the systems designed to solve all of their analytical

problems. The alienation was traceable to the concern voiced by

Meister (1976) that unless users are involved in the

1

H^IIWIM l«WJ>WMt»»^»H»>«W«Lr»l.f».J» .JJU
systems design and development process from the outset the

system is far more likely to satisfy the demands of the designers

than it is to help prospective users.

There were also massive conceptual problems. What is decisionmaking? Hov does one model it? Hov does one develop

quantitative measures of decision-making effectiveness? In

short, hov do ve knov vhen ve are witnessing a really "good"

decision process, and how do we know when we have just seen a

really bad one? What about the decision-making subset known as

planning? How are plans made? How should they be made? What

are the elements of a good plan? What feature of the planning

process predict to good and bad plans?

What happens when one shifts from personal decision-making and

planning to military decision-making and planning? Do the

personal models break down? How much can we generalize from the

personal to the military environment?

What are the best ways to model an analytical process? Not very

long ago the answer to this question could be found in the

literature on requirements analysis, but today the rush toward

the design and development of "intelligent" systems has redefined

the question according to the young principles of "knowledge

engineering" (Feigenbaum, et al., 1981, 1982, 1983} Hayes-Roth,

Waterman, and Lenat, 1983). One of the strengths of the

knowledge engineering approach to requirements definition is that

it seeks to uncover the processes by which analytical problems
mrm solved} conventional requirements analysis techniques simply

try to model the symbols and symptoms of problem-solving« symbols

and symptoms that may be unreliable predictors of problem-solving

success. In any case» a decade ago the emphasis was infrequently

on cognitive modeling» and vhen it was« it usually employed

techniques inappropriate to the identification of diagnostic

probiem-solving behavior.

The early years vere thus plagued by a variety of problems.

The insatiable technology push undermined attempts to conduct

systematic requirements analyses. The requirements analyses that

vere conducted employed techniques that could not possibly

provide insight into complicated cognitive problem-solving.

There was also a non-military bias built into many of rne early

decision-siding projects» not in any ideological sense, but to

the extent that unwarranted assumptions about the applicacility

of non-military modeling vere routinely made by some of the very

best and brightest participating in this revolution in analytical

computing. Last« but certainly not least« vas the perceived role

of the computer in the problem-solving process« a role that

regarded the human "user" as an "operator," not as s problemsolving sssistant or partner (Andriole« 1982). This

perspective—as much as anything else--doomed many early efforts.

We entered the 19808 with a set of experiences that wer»

instructive« with a set of tools and techniques that ve Knew

had validity« and with an inventory of interactive systems that
illustrated virtually every input, output, and display technology

known to man. But ve were still without a developmental

paradigm. We were still unsure of the role of the "user" in the

design and development process, still suspect of conventional

requirements analysis methods, and still married to the notion of

the "computer as tool." At the same time, there vere still a lot

of technology pushers around.

1.2 Overview of Proiect Plan and Objectives

Our project to design and develop an intelligent planning aid was

launched with the greatest of expectations about how the past

could be brought to bear full force against the present. Our

initial goals were, we believed, clean to develop an aid that

would have applicability in an important military problem-solving

area; to develop an aid that acted like a non-threatening

colleague; to work closely with those we were trying to aid) and

to resist temptations to integrate "new" (hardware/software)

technology into the aid unless it satisfied a bona fide

requirement. Above all else, we wanted to design an aid that was

anchored in a verifiable model of the problem domain, which, in

our case was—and remains—tactical planning.

This interim technical report documents the progress we have majs

thusfar in our attempt to satisfy these and other objectives. As

always, not everything went according to plan. We have been

disappointed with some of our results, but happily surprised by

others. We believe, for example, that great promise lies in the

4
development of a problem-structuring methodology for systems

design, a methodology that would necessarily combine elements of

conventional requirements analysis vith principles of knowledge

engineering» systems analysis« cognitive modeling, and rapid

applications prototyping (Boar, 1984).

This report documents progress made toward the development of

a problem structuring methodology within our larger effort to

model tactical planning for the purpose of designing and building

an interactive computer-based planning aid. The report also

documents progress made in the design and development of the aid

itself, nov known as "TACPLAN."

Finally, this report outlines the directions in which future

research should move.
2.0 PROGRESS ON THE CONCEPTUAL

DESIGN OF AN INTELLIGENT PLANNING AID

2.1 The Initial Analytical Frawvork

Our initial framework was comprised of a set of decision-making

and planning models, a generic behavior/task taxonomy, and an

inventory of aiding methods.

2.1.1 Analytical Perspectives on Decision-Making - There

are a variety of perspectives on the decision-making process.

Some regard it as goal-directed (Pearl, Leal, and Saleh, 1982),

some as utility-driven (Raiffa, 1968? Brovn, Kahr, and Peterson,

1974), and some as simplistically sequential (Devey, 1910).

Nearly all of the perspectives can be distilled to a common

diagnostic property: optimization. All decision-making is

designed—at least in the abstract—to produce scie measurable

return, yield some goal, or accomplish some task. Disagreements

arise regarding the best routes to optimization, but a rough

consensus exists about the purpose of decision-making.

The simple notion of optimization guided us through the initial

conceptual stages. We noted that in spite of the simplicity of

the concept many previous decision aids did not help their users

make oplimal decisions. In fact, some were designed to increase

the number of options identified and scrutinized during the

decision-making process, some to decompose complex "one shot"

problems, and some to evaluate a limited number of decision

6

*
«lt«rnative» against a limited number of «valuativ« criteria.

Som« of these "aids" were excellent in the limited operations

that they performed, but many of them fell far short of

calculating an optimal course of action.

Previous research suggested clearly that decision-making is a

behavior targeted at optimization» and that a decision aid can

only be effective if it leads its partner toward some kind of

measurably optimal action.

This perspective on decision-making excludes those that regard

decision-making as part of a larger "decision support" process.

While we have no quarrel with the broad view of decision support

as encompassing problem identification, problem structuring,

information gathering, information fusion, information display,

option identification, option evaluation, option selection,

option execution, and evaluation, our view of decision-making is

more restricted. Why? Because our research goal is to build an

aid that assists real tactical planners formulate good, i.e.,

optimal, tactical plans. Our goal is not to design an

interactive, overarching tactical planning support system. It is

significant that such a system could not be built until its

decision-making core was described) in other words, the essence

of decision support should only be measured in terms of the

extent to which the support contributes to optimal decisionmaking. Designers of decision (or planning) support systems that

do not understand the (optimal) core requirements csnnot hope to

build a useful system because all decision support by definition
must support optimal decision-making. (It is interesting to

overlay this view of decision support onto the commercial decision

support system marketplace» vhich is fond of describing nearly

everything but optimization as "decision support.")

2.1.2 Analytical Perspectives on Planning - Planning is a

special kind of decision-making that is inherently goal-directed,

though not all views of planning recognize it as such. Efficient

planners are in full command of their resources, have a clear

viev of their requirements, have an explicit schedule, a

definition oz their environment, and are rational information

processors. Such planners sre not always successful, however.

Just as it is possible to make a "good" decision that results in

a "bad" outcome, so too is it possible to formulate a "good" plan

that fails to realize its goals. The disconnect results from the

uncertainty that can never be completely eliminated in the

decision-making or planning process. The best decision-makers

and planners can do is to implement a process likely to result in

a "good" outcome. Keep in mind that "good" in this context means

optimal—the best that can be achieved given a set of

constraints, resources, opportunities, end an "algorithm" for

calculating the course of action likely to achieve the greatest

percentage of one's (qualitatively or quantitatively) articulated

goal.

These general planning concepts have yielded some perspectives

worth noting here. One, developed largely at the Rand
Corporation by the Hayes-Roths (1978» 1980) regards the planning

process as "opportunistic." Opportunistic planning is adaptive

planning, or planning that reacts optimally to changes in one's

planning progress and one's (friendly or adversarial)

environment. Opportunistic planners are realistic planners that

never lose sight of their goals as their plans unfold, fall

behind, and break down. They are also relatively unrestricted

planners, vith the ability to alter their plans quickly and

effectively. As an analytical perspective on the planning

process the opportunistic perspective is useful, but not

entirely satisfactory. Among other problems, it is abstract to

the detriment of efforts to extrapolate its components to

reality, especially military reality.

Other perspectives on planning include the view that planning is

goal-directed (Sacerdoti, 1974, 1977) or "top-down." Top-down

planning is believed to be initially abstract, but, over time,

increasingly specific and substantive. This view of planning is

akin to the military view of planning as "mission oriented":

candidate plans are iterated against a set of goals and sub-goals

until an optimal plan emerges.

In 1978 the Hayes-Roths concluded that the top-down or

hierarchical model of planning was less diagnostic than their

multi-directional, opportunistic model. In subsequent tests they

demonstrated empirically that aspects of the planning process can

be explained by the opportunistic model, but failed to generate

enough evidence to displace the hierarchical model. When the
problem domain is military» the evidence is even lass convincing,

Nevertheless, and as the Hayes-Roths and others suggest» there

is merit in both vievs of the planning process. As always, the

requirements-driven application should determine vhich model is

most likely to satisfy the most cognitive requirements.

2.1.3 A Generic Behavior Taxonomy - A cornerstone of any

decision aid design is the identification of the tasks that the

aid is expected to perform. As already suggested, there are a

variety of requirements analysis techniques. Some are better

than others, but they are all designed to identify and define a

set of (in this case, planning) tasks amenable to

computerization. Previous research (Crolotte and Saleh, 1979;

Galitz, 1980; Bennett, 1971) has, however, already taken some

steps to reduce the complexity of the requirements

analysis/knowledge engineering process via the development of

generic behavior taxonomies, such as the one below (Crolctte and

Saleh, 1979):

A GENERIC BEHAVICF TAXONOMY

e Perceptual Behavior

Searching for and Receiving Information

— Detects

Inspects

Observes

— Reads

— Receives

— Scans

— Surveys

Identifying Objects, Actions, and Events

10
Discriminates

Identifies

Locates

• Mediational Behavior

• Information Processing

Categorizes

Calculates

Codes

Computes

Interpolates

Itemizes

Tabulates

— Translates

- Problem-Solving and Decision-Making

Analyzes

Calculates

Chooses

Compares

Computes

Estimates

Plans

e Communication Behavior

Advises

- Answers

Communicates

Directs

Indicates

- Informs

- Instructs

- Requests

- Transmits

e Motor Behavior

Simple/Discrete

Activates

Closes

Connects

Disconnects

— Joins

— Moves

— Presses

— Sets

11
- Complex/Continuous

— Adjusts ;

— Aligns

Regulates

— Synchronizes

— Tracks

TABLE 2.It A Generic Behavior Taxonomy

Behavior taxonomies are extremely valuable compasses. They

forbid one to assume or ignore the components of behavior; they

also help structure and interpret one's specific requirements

analysis.

2.1.4 A Methods Inventory - Our initial analytical

framework also included an assessment of decision-aiding methods.

Unfortunately, the selection of a decision-aiding method can

usually be explained by the preferences of aid designers, rather

than by a matching of available methods with the tasks revealed

by a systematic requirements analysis.

There are four general classes of aiding methods. They include

decision analytic methods, operations research methods, methods

grounded in computer science, and methods that extend from the use

of advanced man-machine interfaces and interactive graphics. The

chart below, modified from Crolotte and Saleh (1979), suggests

the variety of methods available to the decision aid designer, as

veil as the designer of all kinds of interactive aids.

12
A DECISION-AIDING METHODS INVENTORY

• Decision Analytic Methods

Value Models

— Utility Assessment Techniques

— Multi-Attribute Utility Analysis

— Cost-Benefit Analysis

— Discounting Models

— Monte Carlo Methods

— Group Utility Aggregation

- Probability Models

— Probability Elicitation

Bayesian Updating

Sensitivity Analysis

— Group Probability Aggregation

Probability and Value Models

Subjective Expected Utility

— Probabilistic Multi-Attribute Utility Analysis

— Risk-Benetit Analysis

Decision Tree Structuring

— Group Decision Analysis

Partial Information Based Decision Analysis

Fuzzy Decision Analysis

e Operations Research Methods

Analytic Models

Warfare Area Models

Lanchester's Theory of Combat

Game Theory

Detection and Search Techniques

Time Invariant Statistical Detection

Signal Detection

Search Modeling

Optimization Methods

Simulation and War Gaming

Scheduling

— Mathematical Programming

Facilitation Techniques

Tactical Simulation

13
Coverage Templates

— Time/Distance Algorithms

e Computer Science Methods

Information Management Techniques

Data Base Organization

Man-Machine Communication

— Message Processing

Pattern Recognition Techniques

— Clustering

— Classification

— Information and Discrimination Measures

Linear Discriminant Functions

- Artificial Intelligence Techniques

Problem Representation

— Problem Solving

— Learning Systems

— Pattern-Directed Inference Systems

— Planning Mechanisms

Knowledge Representation Techniques

— Natural Language Interfaces

e Advanced Man-Machine Interface/Graphic Methods

- Situation Displays

Dynamic Displays

— Geographic Highlighting

— Alternative Backgrounds

— Automated Overlays

Selective Gallup Techniques

Innovative Symbology

- Pattern Analysis

Center-of-Mass Computations

— Movement Trend Analysis

Terrain Models

Graphic Summarization

— Zooming/Windowing

— Annotation Techniques

- Planning Tools m

— -What If" Projections

— Sketch Models

— Optimal Routing

14
— Target Aggregation

TABLE 2.2t A Decision-Aiding Methods Inventory

At the very least» a methods inventory can assure the

consideration of a variety of aiding methods and techniques» and

permit the matching of a vide range of methods, initially with a

generic taxonomy of behavior and then vith a more specific

definition of requirements.

A mijor initial concern of ours vas to avoid—at all cost—a

"methodological forcefit." Far too many aids are driven by

methods which must be learned by their users» methods that are

at once complicated and often inappropriate to the aiding

function. An example will illustrate the danger. In the late

1970s several aids for intelligence analysis were developed based

upon the use of Bayes' Theorem of Conditional Probabilities.

Hayes' Theorem permits forecasters to revise the probability of

an event based upon new information according to the formula

presented below, it is a poverful and deceptively simple formula

BAYES THEOREM OF CONDITIONAL PROBABILITIES

PCHJ/DJ) PCDJ/HJ) P(H1)

PCHJ/DJ) P(D1/H2) P(H2)

where

15
H * hypotheses or explanations of information

D = data or Information

P(Di/Hi) « the probability that a piece of information, Dj, would

be observed assuming that hypothesis one, Hj, is true

PCHJ/DJ) the probability that hypothesis one, Hi# is true

assuming that a piece of information, Dj, has been

observed

P(H^) * the forecaster's initial or prior opinion that

hypothesis one, H^, is true, i.e., before D^ is

considered

P(DI/HT) « the likelihood ratio, i.e., the forecaster's relative

PCDJ/HJ) judgment regarding the likelihood that a piece of data

or information would be observed given that the first

hypothesis, H^, is true over the second hypothesis,

H2

PtHQ = the prior odds that Hj is true over H2, i.e., the odds

PTH^T before a piece of data or information is considered

TABLE 2.3: Bayes' Theorem of Conditional Probabilities

for assessing the impact of new information on a prior

probability estimate. Unfortunately, and as the above suggests

all too painfully, the logic that underlies Bayes' Theorem is

convoluted. Intelligence analysts are not accustomed to asking

questions about the likelihood of international crises by first

inquiring about the likelihood of observing a piece of data given

the likelihood of one versus another hypothesis. In test after

test, intelligence analysts found it almost impossible to deal

with the requirements of Bayes' Theorem until the designers

solved the problem by developing yet another "aid" called

"SCORE," which was intended to train analysts how to use Bayes'

Theorem and think "probabilistically" (Gulick, 1980).

16
Bmyuimn aids continue to be designed for intelligence

applications with little or no regard for the inapplicability of

the methodology to many intelligence problems (Hunter, 1983).

Bayes* Theorem remains popular with many designers because it

represents a normative forecasting tool, normative, the* is, from

the designer's perspective. Intelligence analysts resis the

theorem because it is incompatible with the processes by which

intelligence analyses and estimates are produced, and, relatedly,

because no organizational support can be found for aids that

require a great deal of time to learn hov to use, and that, in

turn, produce forecasts that are difficult if not impossible to

assimilate into the overall intelligence process. In retrospect,

it appears that the forcefitting of analytical methods usually

occurred when the designers preferred to skip the tedious

requirements analysis process, vhen they vere more interested in

championing a nev method than solving real problems, or vhen they

believed that the analytical process should be changed to

accommodate a more powerful method. While their intentions may

have been good, the results vere usually bad.

2.1.5 The Research and Development Plan - Our initial

analytical framework, comprised of decision-making and planning

models, a generic behavior taxonomy, and an inventory of

analytical methods, enabled us to identify an analytical

strategy for the design and development of a tactical planning

aid

17
The strategy included the exploitation of previous research

findings, the conduct of a detailed requirements analysis« the

re-modeling of the planning process, the selection of aiding

methods based upon the results of the requirements analysis and

re-modeling procedure, the functional design of a prototype

planning aid, and the development of the TACPLAN aid itself. The

following sections discuss each of these steps in the order in

which they were taken.

2.2 Planning Requirements Analysis; The Carlisle Experiments

Previous research in decision-making and planning suggests that

these two "mediational" behaviors are context-dependent.

Generalizations from non-military environments are virtually

impossible to make. The first order of business was thus a

thorough requirements analysis of the planning process.

A search was launched to find expert planners, not because we

were predisposed to the development of an expert planning aid,

but because the best way to model a behavior is to observe

experts in action. We contacted a number of U.S. Army offices

and agencies, the National Defense University's War Gaming and

Simulation Center, and planners at the Army's War College at

Carlisle Barracks, Pennsylvania. The planners at the War College

were expert and enthusiastic} they were also the acknowledged

Army experts in the formulation of tactical plans, especially as

they involved the defense of Western Europe. After a series of

meetings an agreement was reached with planners in the College's

18
Contingency Planning Group (which lat«r «volvad into the Center

for Land Warfare). The planners agreed to provide us with

information about the planning process via some experiments.

It is important to note that the Army War College has several

programs of r :udy and research in strategic and tactical

planning. Students from throughout the Airmy spend time at the

College} there are also students from other services as well as

some foreign students. All of these students are expected to

learn about the planning process generally and specifically with

reference to a number of explicit scenarios. Given the Army's

role in the defense of Western Europe, scenarios involving the

defense of Germany and the whole of Western Europe are common.

The scenario we used to conduct our requirements analysis is one

that has been used repeatedly at the Army War College, though

always with some twists. Frequently, however, the mission

involves the defense of Western Europe given the likelihood of a

Soviet, or "Red," invasion of the West.

We used the scenario because the expert planners were familiar

with it, and because it is representative of a "typical" tactical

planning problem. At the same time, our use of the scenario

called for the development of a Corps level plan, or one that

would yield many sub-plans down the chain of command.

We ran two experiments, one involving three 06 officers and one

three 05s. They took place over a two day period in January

1964. The results, along with a description of the experiments

19

m*mma*mm^mammmmmmmmmmmmmmm*mmm*m*mm
themselves, are reported belov.

r

2.2.1 The Tactical Scenario - The U.S. Corps/Soviet

Combined Arm» Army Coimnand Post gxercise (Letort 64) and the

Soviet Combined Arms Army Scenario# Command Post Exercise (Letort

84) served as our scenario. Very succinctly, this scenario

describes a hypothetical situation vhere:

. . . political and economic actions have led

to greatly increased tension between NATO and

the Warsav Pact. Over a period of time, US

reserve units had been activated and some

regular Army units had been reconstituted.

In early 1987 a number of these units had

been deployed to Europe . . . by 1 July 1987,

the United States had deployed the XI Corps

headquarters, 4th Armored Division, 90th

Infantry Division (Mech), 80th Infantry

Division (Mech), 14th Armored Cavalry

Regiment, and the 22nd Aviation Brigade

to West Germany. The theater commander

assigned the XI Corps and attached units

to the nevly formed Middle Army Group

(MIDAG), which had been given responsibility for a sector in the vicinity of

Hannover, West Germany . . . (Letort 84)

This situation is further defined by the theater commander who

offers the following guidance to his planners:

Gentlemen, you have been issued, and have

had an opportunity to review, OP PLAN 3-87.

Our mission is to defend in zone, focusing

on the destruction of Pact forces. I see

our initial operations occurring in three

phases. First, ve must defend against an

attacker who can pick the time and place

in which substantial force in the form of

maneuver units and firepower will be

employed. We will face an attacker intent

on shattering our forward deployed units

and making a rapid thrust to cross the

river and canal barriers in our sector

20
a« he carries his attack forward across

the Rhine, into the low countries and

the ports which are the heart of our

logistical lifeline . . . his immediate

objective will remain crossing the Rhine.

He expects the Ruhr as a bonus ... XI

Corps will conduct the main defensive

effort in the northern portion of the

MIDAG sector . . • vou must preserve

your command, while inflicting maximum

damage on the attacker . . . (Letort 84)

Figure 2.1 illustrates the XI Corps' area of responsibility, an

area that had to be defended with a limited number of combat

assets. The two groups of planners were each required to develop

a plan that would result in the successful defense of their area.

The planners were given, as part of the scenario, intelligence

about the location and composition of Soviet forces, information

about the terrain and general area characteristics, and

intelligence about their own combat capabilities. As expert

planners, they also had insight about adversary doctrine and the

opportunities and constraints provided by their own doctrine.

Their task was to optimally deploy their own forces given

assessments about likely adversary courses of action.

2.2.2 The Planning Experiments - The two experiments were

conducted on January 16 and 19, 1984 at the Army Kar College at

Carlisle Barracks in Carlisle, Pennsylvania. We ran the scenario

with the 05s (three Lieutenant Colonels) on January 18 and with

the 06s (three Colonels) on January 19. Both sessions were

prepared by setting up a tactical map of the situation, which

involved spending approximately one hour placing forces around

21
tnciwü

etQOu<aouUa50

22
the map. The group that was to conduct the exercise did the

placing, which they saw for the first time. After the 05s were

finished, their "solution" vas removed. The 06s repeated the

process. Both groups were confronted vith exactly the same

deployment of adversary forces and were given exactly the same

combat assets and planning guidance.

Each group also spent some time reviewing the scenario prior to

the simulation. Since both groups were familiar with the

scenario, not unlike the way a Corps commander would be

responsible for formulating plans for a set list of

contingencies, a good deal of realism was preserved.

The problem backdrop assumed that a Corps commander was about to

formulate a plan in response to a set of adversary actions that

were not totally unanticipated, though perhaps never really

imagined in the detail that the scenario assumed. The actual

placing of Soviet/"RedMforces was done within certain

constraints that have been built into a computer program that the

Army War College uses to run its war games. It is modestly

random, in the sense that it will deploy adversary forces within

certain realistic assumptions about the nature of Red force

structures, orders of battle, doctrine, and the like. Before the

exercise, we ran the computer program to determine where

adversary forces should be positioned. We then positioned the

forces on the large map on the wall, a 4* by 8* map that was

overlayed with a clear sheet of acetate. Grease pencils were

provided, and adversary and friendly forces were represented by

23
magnetic symbols, or "icons," that could be moved as the

simulation evolved.

A number of assumptions were made about Red and Blue forces. We

assumed that, for example. Red had been moving around for

approximately three months prior to the snapshot that ve took for

experimental purposes. We assumed that the Blue Corps commander

had approximately seventy-two hours to formulate his plan. We

assumed that the "plan" would be in the form of a concept

definition, or "concept of operation," the required fcrm for a

Corps level plan. A good concept of operation illustrates and

describes the deployment of Blue forces in a way likely to

satisfy mission objectives. It is also flexible and relatively

abstract, enabling lower level commanders to interpret some of

its components with reference to their specific mission

objectives.

The planning problem itself saw massive deployment of Red forces

along the NATO/Warsaw Pact boundary. Red forces were deployed

all along the boundary, from North to South, and very deep in the

East. Blue forces were relatively scattered and outnumbered, at

least in terms of the number and size of available divisions.

Again, the Corps commander had seventy-two hours to formulate a

plan for deploying his forces given an imminent attack by Red

forces.

The 05s and 06s were videotaped as they formulated their plans.

The suggestion to videotape was made by Dr. Robert M. Sasmor, who

24
hypothesized that • great deal of information about planning

could be gleaned not only from the conventional audiotaping of

planning utterances, but from the observation of actual motor

behavior as veil. In retrospect, the suggestion yielded enormous

dividends. The videotape reference of the planning process has

enabled us to study the process from a variety of cognitive and

analytical perspectives. It has also augmented the conventional

(audio) recording of "protocols," and permitted a much more

thorough analysis of their content.

But perhaps most importantly, the videotape documentation

permitted us to test a number of hypotheses about planning veil

after the fact. Months after the experiments took place, and

after we were veil into the functional modeling of the TACPLAN

planning aid, ve vere able to refer back to the tapes to

determine the analytical methods that might best satisfy specific

planning requirements, and to design input, output, and display

routines likely to capture and support the essence of tactical

defense planning at the Corps level.

Six hours of planning vere filmed over the tvo day period. Setup time involved another tvo to three hours. During the filming

the planners were asked to develop their plans but also to step

away from the process from time to time to describe exactly what

they vere doing and vhy they vere doing it. Dr. Stephen J.

Andriole served as an informal "moderator" of the process,

prodding and challenging the planners for clearer explanations of

their behavior. This "inside/outside" role that the planners

25
v«r« forced to play threatenad the realism of the experiments,

but, just as in the conventional audio collection of protocols,

was necessary if our requirements analysis was to yield

diagnostic results. As it turned out, given the experience of

our planners, there were few real instances of "cognitive break"}

the planners were able to continue with the planning process

vhile commenting upon their behavior within that process.

Clearly, the level of experience is crutial to the success of

any conventional or unconventional collection of protocols. If

one's experts are really not very expert then e data is likely

to be inconsistent and unreliable; conversely, genuine experts

can rather easily time-share across several analytical roles

before, during, and after the process in question. Indeed, one

of the characteristics of expertise lies in its ability to

introspect upon its ovn application, and at least on^ way to

measure the depth of expertise involves determining the extent to

which an expert can roam within and beyond the area in which he

or she is supposed to be expert. "Experts" that have great

difficulty introspecting upon the application of their expertise

are either (a) inherently inarticulate, or (b) not very expert.

Once the first possiblity has been eliminated, then a search for

experts with a deeper understanding of the problem area should be

launched.

A final point about the experiments. We assumed a seventy-two

hour window, yet captured only six hours of actual planning. In

fact, given the nature of the planning process that was actually

26
taped, vc captured but four to five hours of tactical planning.

This apparent unrealism was mitigated by the absence of a large,

time-consuming planning staff, the absence of continual 6-3

(intelligence) on the tactical situation, end—most importantly—

by tactical planning precedent, which nearly alvays yields an

initial concept of operation fairly early on in the seventy-two

hour (or however long) planning process. In oO er words, Corps

commanders frequently develop a concept of operation several

hours into a planning session and then iterate on it until happy

with its form. Interviews with expert planners, conducted to

corroborate this hypothesis, went even farther. One expert

planner actually suggested that any "Corps commander worth his

salt already knows what he's going to do as soon as he receives

even the grossest guidance." This suggests that what we captured

was perhaps a first iteration on what would eventually emerge (in

seventy two hours) as a final concept of operation. It vas also

suggested that Corps commanders frequently accelerate the

planning process because of the likelihood of additional theater

guidance and requirements. Again, regardless of how much time is

available, good Corps commanders can formulate plans rapidly.

While we would have ideally preferred filming actual or simulated

Corps commanders for seventy-two hours, ve believe that

sufficient realism was captured during the two experiments that

ve conducted.

2.2.3 Results - Figures 2.2 and 2.3 summarize the results

of the planning experiments. It is interesting to note that

27
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above all else the tactical military planning process is

•xcrutiatingly procedural and doctrinal. During the planning

process both groups made repeated references to the "book" on

planning and vhat doctrine had to say about operations and

deployment. Unlike personal planning» the military planning

exercise is relatively inflexible, permitting judgment and

intuition to play limited—though not insignificant—roles.

Generalizations about planning gleaned from non-military

experiments are of little real utility to attempts to model the

military planning process, except perhaps at the most general

level.

There is no question that the tactical planning process is

sequencial and iterative. The first step involves understanding

or defining the problem. In tactical language, this means

defining the mission on several levels. More abstractly, this

means that goals and sub-goals must be specified. This

hierarchical process supports the view of planning as "top-down"

and "goal-directed." The first definition of the mission lists

the desired effects of a successful operation via the

implementation of an optimal plan. Our scenario called for the

defense of Western Europe against Red aggression at various

points in time and at various geographical locations.

During the experiments both groups then turned their attention to

an aggregate assessment of adversary intentions. Intentions

estimation is, of course, a classic intelligence problem, though

in this case the groups were aided by intelligence (comprised of

30
enemy capabilities» doctrine» rules of engagement, and the like),

and notions of Red commander behavior developed from prior

planning exercises and some structured vicarious observations and

analyses. This vicarious aspect is worth noting. Both groups

kept saying things like, "that's what I'd do," and "if I were him

then I'd . . . " After a while they were queried about what they

were doing and they said that they were listing the adversary's

preferred courses of action and goals and then, based upon

intelligence data, ranking the courses of action in terms of

their likelihood. This is akin to what formal decision analysts

try to get decision-makerf> to do. Interestingly, tactical

planners—due more to training, procedure, and doctrine—do this

routinely.

They also spend a lot of time studying the specific

characteristics of the area in which they both (Red and Blue)

must maneuver. This analysis is also highly procedural and

involves studying everything from geography to transportation,

telecommunications, culture, and the like. During the

experiments references were frequently made to cities, canals,

rivers, and other area realities, suggesting the importance of

seemingly unspectacular characteristics.

Relative combat power was also assessed. It was interesting that

the tendency was to look for weaknesses within enemy and friendly

forces end then look for strengths.

The planners next formulated several concepts of operation by

31
re-»nalyzing their own mm well mm enemy capabilities, interests,

goals« and courses of action. Courses of action (COAs) were

extremely important focal points for the planners. COAs guided

assessments about combat capabilities and area characteristics;

they also performed double duty as the primary mechanism for

challenging alternative planning concepts.

There was also a great deal of discussion and disagreements as

the planners began to convert their COAs into a concept of

operation. In a sense, all of the analyses and assumptions made

during the planning process became exposed during this

translation process) the newly tangible naturally triggered a lot

of verbal response.

As suggested in Figures 2.2 and 2.3, a number of explicit results

emerged from the experiments. Nearly all of these results have

implication for the design and development of an interactive

computer-based planning aid. The procedural nature of the

planning process, its iterative character, and its overwhelmingly

visual components all suggest a number of very specific aid

characteristics.

The differences uetveen the two groups, summarized in Figure 2.3,

are also vorth noting. While genuine differences did exist,

they were not the Kind of differences that would suggest the

design and development of two different planning aids. In fact,

most of the differences were explained by the differences in

experience within the two groups. Significantly, there was

32
little intra-group disagreement and only precedural inter-group

disagreements. We hope to conduct additional experiments to

control for variables like planning experience, rank, age, and

prior combat performance.

2.3 The Revised Analytical Framework

Recall that ve began our research vith some models of the

decision-making process, models of the planning process, a

generic behavior/task taxonomy, and an inventory of methods. Our

target area was pre-set as tactical military planning. We

conducted a set of experiments to gain insight into the planning

process and then developed a working analytical perspective fron

which to design our aid.

2.3.1 Tactical Military Planning as Goal-Directed - The

variety of available models of the decision-making and planning

processes provided a rich source of information against which ve

could interpret our experimental findings, which suggested

initially and unequivocally, that tactical military planning at

the Corps level is top-down, hierarchical, and goal-directed.

The very fabric of military planning is weaved with

considerations of the goal, and how to implement a very explicit

planning procedure. Military planners are trained to adhere to

this perspective, practice this perspective, and quite normally

resist any attempts to alter it. It is fascinating to postdiet

the reactions that planners and decision-makers had to decision

33
aids that challenged such perspectives. It is clear now that

such aids were doomed to failure, that aids can only succeed if

they find just the right combination of technology, requirements.

and doctrine. Many past aids suffered from too much technology«

vhile some never ones from inadequate requirements. We now know

that technology and requirements can only be usefully combined

through an understanding of doctrine, training, and precedent.

2.3.2 Planning Tasks - The experiments also suggested that

planning is essentially mediational, as suggested below in Table

2.3. But note also that the requirements analyses also revealed

several perceptual and communication behaviors. Analysis of the

videotaped experiments even suggested that some planning tasks

can be found in our list of motor behaviors. The solid lines in

Table 2.3 suggest the primary planning tasks, vhile the dotted

lines suggest the secondary tasks. When in doubt about the

inclusion and weighting of individual tasks ve referred back to

the videotapes, studied Army Field Manuals and Officers'

Handbooks (U.S. Army, 1983), and consulted the interviews with

our expert planners.

A GENERIC AND TACTICAL PLANNING BEHAVIOR TAXONOMY

Perceptual Behavior

Searching for and Receiving Information

— Detects

Inspects

Observes

Reads

— Receives

34
— Scans

— Surveys

- Identifying Objects, Actions, and Events

— Discriminates

— Identifies

— Locates

Mediational Behavior

Information Processing

— Categorizes

— Calculates

Codes

— Computes

Interpolates

Itemizes

— Tabulates

— Translates

Problem-Solving and Decision-Making

— Analyzes

— Calculates

— Chooses

— Compares

— Computes

— Estimates

— Plans

e Communication Behavior

- Advises

- Answers

Communicates

- Directs

Indicates

Informs

Instructs

- Requests

- Transmits

Motor Behavior

- Simple/Discrete

~ Activates

— Closes

~ Connects

— Disconnects

— Joins

35
— Moves

Presses

— Set« ;

Complex/Continuous

— Adjusts

— Aligns

Regulates

Synchronizes

— Tracks

TABLE 2.3t Primary and Secondary Planning Behavior

Table 2.3 identifies the tasks that goal-directed planners perforr

whenever they develop a tactical plan at the Corps level. A

further stipulation is that these tasks hold only for the

development of defensive plans. The stipulations are stressed

here because it is difficult to generalize up or down command

levels without studying each level thoroughly. While planning is

likely to remain goal-directed up and down command levels, aspects

of the process may change substantially across the levels.

Platoon leaders, for example» may well plan very lifferently fror

Corps commanders because of the nature of their missions, their

areas of responsibility, and training, among other possible

differences. Before an aid designed for use at other command

levels could be extended to these levels, additional requirements

analyses would have to be conducted.

2.3.3 Planning Methods - Table 2.4 suggests the methods

most likely to satisfy the task requirements identified in Table

2.3. The bold lines suggest the primary methods, while the

36
dotted lines suggest the secondary ones* or those less likely to

satisfy primary planning tasks. But keep in mind that there is

not a one to one correspondence between the tasks identified in

A DECISION-AIDING AND TACTICAL PLANNING METHODS INVENTORY

e Decision Analytic Methods

- Value Models

Utility >jsessment Techniques

Multi-Attribute Utility Analysis

Cost-Benefit Analysis

Discounting Models

Monte Carlo Methods

Group Utility Aggregation

Probability Models

Probability Elicitation

Bayesian Updating

Sensitivity Analysis

Group Probability Aggregation

Probability and Value Models

Subjective Expected Utility

Probabilistic Multi-Attribute Utility Analysis

— Risk-Benefit Analysis

Decision Tree Structuring

Group Decision Analysis

Partial Information Based Decision Analysis

Fuzzy Decision Analysis

Operations Research Methods

Analytic Models

Warfare Area Models

Lanchester's Theory of Combat

Game Theory

Detection and Search Techniques

Time Invariant Statistical Detection

Signal Detection /

Search Modeling -'

Optimization Methods

37
Simulation and War Gaining

Scheduling

— Mathematical Programming

Facilitation Techniques

— Tactical Simulation

Coverage Templates

Time/Distance Algorithms

IT Computer Science Methods

Information Management Techniques

Data Base Organization

— Man-Machine Communication

Message Processing

Pattern Recognition Techniques

Clustering

Classification

Information and Discrimination Measure^

Linear Discriminant Functions

- Artificial Intelligence Techniques

Problem Representation

Problem Solving

Learning Systems

Pattern-Directed Inference Systems

Planning Mechanisms

Knowledge Representation Techniques

Natural Language Interfaces

Advanced Man-Machine Interface/Graphic Methods

Situation Displays

Dynamic Displays

Geographic Highlighting

— Alternative Backgrounds

— Automated Overlays

Selective Callup Techniques

Innovative Symbology

Pattern Analysis

Center-of-Mass Computations

Movement Trend Analysis

— Terrain Models

Graphic Summarization

Zooming/Windowing

1

-i

38
Annotation Techniques

- Planning Tool» I

I — "What If" Projections j

I — Sketch Models

! — Optimal Routing !

j — Target Aggregation 1

!... . .—. ... J

TABLE 2.4: A Decision-Aiding and Tactical Planning Methods

Inventory

in Table 2.3 and the methods identified in Table 2.4. The goaldirected planning model, as veil as the (tactical military)

context, intervened between the tasks and methods tables to make

certain that the matching procedure vas compatible with the

substance of the application. What does this mean? In short, it

suggests that methods will only satisfy task requirements if they

are appropriate to the operational environment in which they are

to be applied. This dictum applies regardless of whether the

application involves the use of scratch pads or software. If a

method disrupts, threatens, or confuses a process anchored in

decades of precedent, it will fail.

This is not to suggest that one should never apply sophisticated

methods to military problems, but rather that the application

cannot expect to uproot years of experience, training, and

doctrine. Moreover, the prudent application of new methods

assumes that the method will be unobtrusive and, to some extent

at least, transparent to the problem-solver. (See Appendix A for

more discussion of abstract problem structuring and the use and

mis-use of sophisticated analytical methodology.)

39
The methods identified in Table 2.4 represent those most likely

to satisfy the requirements of a Corps commander developing a

tactical defensive plan. Some of the specific methods are worth

discussing in detail here vhile some of the others were selected

according to some obvious criteria. The use of some artificial

intelligence techniques» for example* was justified '. '.hree

levels. First« artificial intelligence (AI) techniques lend

themselves nicely to procedural management, that is, to the

management of the planning process. AI techniques can be used to

"moderate" the planning process. They can be used to determine

if the planner is behaving soundly and consistently. They can

order the tactical planning process and regulate the number and

variety of deviations from an "ideal" process. AI techniques can

also provide substantive direction to the planning process.

Knowledge about terrain, trafficabiiity, force strengths,

doctrine, and orders of battle can be represented in several ways

and brought to bear on the planning process at various points and

in response to various judgments made by the planner during the

process. Such knowledge can be used to check the consistency and

accuracy of the planner's judgment, and to stimulate the planner

with alternative scenarios and planning solutions. Finally, AI

techniques can be used to reduce the cognitive burden placed upon

the planner.

All of these applications of AI techniques are consistent with

what the planning task analysis revealed. Note that we are not

proposing to use AI as a means to the automation of the planning

40
process. Our research has suggested that the kinds of inferences

made during the planning process by human planners naturally

result from a properly conducted planning exercise, and that the

formulation of a plan bears no resemblance to the diagnosis of a

disease. By nature, planning is a much fuzzier domain, and

therefore resistent to techniques that deduce inferences from

"knovn" rules of thumb, or heuristics.

The identification of advanced man-machine/graphic interface

methods is a direct outgrovth of the planning experiments and our

subsequent analysis of the experimental data, which suggested

that huge components of the planning process are graphic,

symbolic, and decidedly non-numeric. Our data suggested that it

would be impossible to design an interactive planning aid without

an interactive graphic interface that was directly manipulatable

by the planner. Non-computer-based planning is accomplished only

with the aid of maps and other "props,*1 such as acetate overlays,

grease pencils, tactical icons, and the freedom to move easily

through the map space. Time after time during the experiments

discussions were organized around the drawing of yet another

course of action, the moving of icons, and the erasing of

previously discarded ideas. It was obvious after a very short

period of time that an interactive, "intelligent" planning aid

would also have show movies, or more accurately, "movie maps."

It would do no goou to integrate static maps, either. The

requirements called for zooming capabilities, capabilities to

annotate dxrectly onto dynamic maps, and capabilities that would

41
permit planners to test tactical hypotheses without having to

learn hov to program a sophisticated computer. One of the

capabilities that grease pencil/acetate planners do not have is

the ability to store old ideas for comparison with new ones.

Computer-based graphics provide the capability to store and

retrieve ideas that are quickly forgotten once they are erased

from acetate. The use of advanced man-machine/graphic interface

methods thus satisfies a variety of important manual requirements

as well as a number of requirements that conventional planning

apparati can never hope to satisfy.

We should also note that the identification of methods is never

really complete. This is because the aiding design and

development process is iterative (with expert planners) and

therefore likely to uncover new requirements and new

corresponding methods as time goes by.

There are also a variety of hybrid combinations of methods that

can sometimes satisfy a particularly recalcitrant requirement.

Sometimes, for example, totally new methods can be developed for

a special, one-of-a-kind application, while other problems may

require a multi-method strategy.

This overall approach to problem structuring is documented in

more detail in Appendix A. One of the most exciting by-products

of our planning research has been the refinement of this

approach, an approach which we believe can be applied to a

variety of cognitive modeling, requirements analysis. Knowledge

42
engineering, and interactive systems design and development

problems.

2.4 TACPLAN Functional Design

Our approach to the design and development of .-• -teractive

planning aid floved directly from our revised analytical

framework, which told us a great deal about the problem that we

faced. Because of the characteristics of the problem, we decided

to implement an applications prototyping strategy toward the

design and development of intelligent planning templates. We

then produced a strawman functional design of the TACPLAN aid.

The next, step involved developing a concept of operation for the

aid.

2.4.1 The Applications Prototyping Strategy - From the

outset we have assumed that the conventional approach to

interactive systems design and development (Andriole, 1983) would

not satisfy all of our requirements. Instead, we expected to

implement some form of rapid applications prototyping strategy

(Boar, 1984). Why? For many interactive systems design and

development problems the target tasks are primarily perceptual

(see Table 2.1). The methods available to satisfy perceptual

tasks are well known and effective. Consequently, it is often

possible to develop a system likely to satisfy nearly all of

its tasks and users. Systems Intended to satisfy mediational

tasks, however, are much more difficult to design and develop.

43
Mediational systems have a high rate of failure with their

intended "users," vho often make distant use of the systems, or

ignore them altogether (Ramsey and Atvood, 1979). There is also

usually considerable debate about the essence of the cognitive

tasks themselves and the methods most likely to satisfy them.

The conventional approach to interactive systems design and

development, shown in Figure 2.4 (Andriole, 1983), will not often

lead to the design and development of a successful mediational

system because it does not institutionalize the iterative

process. According to Bernard H. Boar (1984), the only sensible

way to proceed is to implement a procedure known as applications

prototyping, which, according to Boar, is "an alternative

approach to requirements definition." The idea is simple enough.

Build a strawman system and then let your intended users tell

exactly what it is they like and don't like about it. Check

these suggestions against your task/requirements analysis, make

some changes, and go back to the users. Repeat the process until

you get close enough to satisfy as many requirements as time,

money, and patience permit. Not unlike Mumford and Henshall's

(1979) "participatory approach," Boar's applications prototyping

strategy can save countless dollars and hours of frustration. It

can also support basic research, since the strategy assumes that

hypotheses can be efficiently tested Mon>line," and that a lot

can be learned from the utterances of—in this case—planners

planning on TACPLAN.

44
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2.4.2 Intelligent Planning Templates - The use of templates

to solve analytical problems is not unusual. In fact, templates

have been used to solve all kinds of perceptual, mediational,

communication, and motor problems (Kelly and Stewart, 1980).

Templates are really procedural blueprints for solving problems.

They can be detailed or general, abstract or substantive. In

their simplest form, they represent checklists} in their more

complex form, they represent a set of hierarchical tasks and subtasks.

Templates have been used in a number of decision aids to solve a

variety of military problems (GulicK, 1981i Kelly and Weiss,

1980» Kelly and Stewart, 1980). They have been used to determine

optimal evacuation strategies, effective hostage negotiation

positions, and likely adversary hostilities. Why use templates?

First, they accelerate problem-solving. They direct the problersolving process, and they reduce some of the burden placed upon

the problem-solver, regardless of whether a computer, scratch

pad, or unaided group of experts is applied to the problem. They

stimulate creativity by proposing a strawman problem strucutre to

which the problem-solver can react» and they accommodate change

and revision in the problem-solving process.

"Unintelligent" templates have been successful problem

structuring and problem-solving tools» "intelligent" templates

have yet to be developed or applied. We believe that

artificially intelligent substantive and procedural problemsolving templates hold great promise for accelerated, burden46
'

reduced tactical planning.

Figures 2.5 and 2.6 list the elements and functions that we

expect to be in our Corps level defensive tactical planning

template. The template itself will contain information about the

planning process and the substance of planning generally and

specifically (at the Corps level in a defensive posture). The

use of templates is also consistent with the design and

development of a modular aid, where additional template

(representing different command levels in different postures)

modules can be added to the TACPLAN aid as they are developed and

tested.

The use of templates also supports the applications prototyping

strategy and larger participatory approach to systems design and

development. Since templates are by nature comprised of bits of

substantive and procedural information, they lend themselves veil

to structured definition. Not unlike the way circuit boards are

tested one by one until the fault is found in modern solid state

electronic appliances, template components can be tested against

a set of internal and external performance criteria, and then

modified or deleted as the results suggest. Whole new componei.ts

can be added if new or previously undiagnosed needs are disovered,

But perhaps most importantly, templates suggest how to

functionally describe a prototype aid. The TACPLAN aid,

described in detail below, was designed with reference to an

intelligent planning template that was the result of our

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2.4.3 TACPLAN Functional Description - Figure 2.7 presents

the large functional description of TACPLAN. Keep in mind that

this description is as comprehensive as possible« a blueprint in

its own right for the development of an intelligent planning aid.

The above discussed template is represented in the top left hand

node, "Develop a New Plan"i the other nodes are procedural and

substantive. Some refer to plan editing and manipulation

functions, while others refer to changes in procedure rooted in

alternative (situational) environments. There is also a node

representing the computerization of a critical function observed

over and over again during the Carlisle experiments, "TACPLAN

'Challenges' to Planner."

All of the nodes in the functional description have correlates in

the manual (grease pencil/acetate) planning process. The

description itself, however, should be understood on two

dimensions, one that occurs above the dotted line which extends

from left to right in the figure and one that occurs below the

dotted line. All of the functions above the line represent the

tasks that an unintelligent template should perform, while those

below the line suggest how and where the template should behave

intelligently.

Recall that AI techniques are proposed as performing three

distinct roles—as a planning procedural "moderator," as a

creative stimulant, and as an input burden reducer. The boxes

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51
input, output, and display routines, but clearly states our goal.

Many previous decision aids have placed enormous burdens on their

users, burdens that usually require them to learn a vhole new

methodology and then "feed" the methodology almost continually.

Many of the decision analytic aids, for example, require their

users to input hundreds of numeric values expressed either as

probabilities or utilities. One planning aid, known as CONSCREE:.

(Martin, Esoda, and Gulick, 1983), requires planners to evaluate

courses of action via the use of a multi-attribute utility

structure that requires planners to veight criteria and score

each course of action against each of the criteria in a tedious

process that requires numeric expressions of value along a scale.

While the multi-attribute utility assessment technique is a

poverful one, forcing the planner to make hundreds of

quantitative assessments places far too much burden on him.

The trade-off of course is diagnosticity. Decision analytic

methods can only "guarantee" results if all of their procedures

are carefully followed. If the procedures are amended or shortcircuited, then the results will be undiagnostic. Or will they?

One of the hypotheses we are testing tries to determine the

level, nature, and structure of the expert judgment one needs in

order to be analytically effective. Perhaps a great deal less

information is necessary. Perhaps we have been burdening our

users for a disproportionate return. We plan to test this and

some other hypotheses in an effort to design and integrate

"spreadsheets" that are both analytically powerful and easy to

53
use.

TACPLAN now has a number of spreadsheets/ based upon multiattribute utility assessment» embedded into the flow of the

planning process. While the planner still has to make

assessments, they are high level and basically qualitative.

TACPLAN will convert these aggregate judgments into quantitative

form, perform some calculations, and then rank-order the objects

under evaluation, like adversary courses of action, friendly

courses of action, and relative combat capabilities. If—and

only if—this initial evaluation fails to produce a clear winner,

TACPLA:. will require the planner to perform a i.>ore diagnostic

(and slightly more tedious) assessment. We hope that the more

complicated procedure will take approximately ten minutes, or

roughly tvice as long as the burdenless procedure. Note that

present multi-attribute utility assessments, such as those

required in CONSCREEN, can take several hours or even days to

perfor.-..

Another set of hypotheses we plan to test will measure the

analytical pay-off derived from the use of graphic equivalent

displays. Recall that the Carlisle experiments suggested

conclusively that tactical defensive planning at the Corps level

was substantially graphic and symbolic, that new ideas were

scrutinized via grease-penciled illustrations and old ones

discarded by a quick reference to realities clearly visible on a

map. Aids like CONSCREEN do not display the actual area for

vhich the planner is responsible. He cannot "see" courses of

54

tmmmmmmma
action as they are conceived and evaluated. Aa the evaluation

unfolds he must refer to other maps and alphanumeric information.

This searching can require him to travel through several levels

of information that are usually stored in several different

forms. While using CONSCREEN he must continually stop and refer

to additional pieces of information. He may even stop to draw on

a paper map.

Since planning is a graphic, spatial« and symbolic probler.-

solving process« planning aids should also be graphic« spatial,

and symbolic. Moreover« planners should be able to see their

concepts as they are developed and evaluated. Assessments about

capabilities« routes« courses of action« and area characteristics

can be made more efficiently spatially and graphically than they

can be nade numerically. While ve vill have to wait for the

results of the evaluation« we expect this and related hypothesei

to be confirmed. We also expect the notion to hold up and down

cor.nand levels.

TACPLAN will permit planners to draw plans« move units« designate

courses of action, and even annotate their drawings directly on

an actual terrain map« not on a computer-generated one. The

system will also permit planners to zoom in and out« in much the

same way planners move closer to and stand back from paper maps.

They will be able to scan across their areas of responsibility«

call out terrain characteristics» and even "de-clutter" the video

y

image (see Section 3.1.2 for a more detailed discussion of the

graphic interface to TACPLAN).

55
Our assumption is that a great deal of problem structuring,

alternative hypothesis testing, and simple and complex evaluation

will be accelerated by the use of interactive mapping techniques.

A corollary assumption is that graphic interfaces can be used

to lighten the analytical workload of the planner, that what

previously would be required to determine analytical consistency

and reliability can be reduced significantly if not eliminated

altogether via the use of high resolution graphics.

The use of graphics also supports our use of embedded multiattribute utility assessment, since graphics can communicate more

information to planners, information that can be used to generate

more reliable analytical judgments.

The third major aspect of TACPLAN's concept of operation worth

discussing is its use of rules to represent procedural and

substantive information and Knovledge, respectively, about the

planning process. We plan to develop a set of rules (see

Appendix B) that will help manage the planning process and will

also help the planner formulate an optimal plan. Recall that

our view of the role of Al methods and techniques falls into

three categories. First, AI can contribute to plan managerrient.

Second, it can contribute to plan consistency while

simultaneously stimulating creative planning? and third, it car.

reduce the burden placed upon planners via the application of a

knowledge base about tactical defensive planning at the Corps

level. Procedural rules are straightforward, but no less

important than substantive ones, which will guide, check,

56
accelerate, and—eventually—criticize the planning process. The

same ru!es can reduce the planning workload by embedding

knowledge about the relationships among specific planning

variables» like terrain, trafficability, force structure, orders

of battle, and doctrine.

We do not anticipate a huge rule base for the TACPLAK prototype;

lor do we anticipate a large rule base for the more fully evolve:;

TACPLA!.', as least not as part of the defensive Corps level

ter.plate. As TACPLAN evolves, additional rule sets car. be

integrated depending upon the kind of planning and the level at

which it is occurring. While it is difficult to deterrine the

precise nur.ber of rult'i that might be used in a given template,

it is difficult to imagine rules sets of larger than 100 rules

for any giver, template designed to support the development of nev.

plans (assuring that rules constitute the best way to represer.t

knovledge? see Appendix A). More complicated applicatio.-.s, suor.

as plan execution. —nitoring, and re-planning, may require mere

sophisticated knowledge bases.

"Burdenless analytical spreadsheets," "graphic equivalent

displays," and procedural and substantive rules comprise the

essence of the TACPLAN concept of operation. But the}1 do not

exhaust the contents. There are additional ideas and hypotheses

that will be tested along the way, such as the use of narrative

rationale as a means of tightening decision-making during the

planning process, the use of graphic menus to accelerate the

assessment process, and the use of "logic finders" to let the

57
planner know where he can find information or hov he can recall

and display his rationale and TACPLAN's responses. To a

significant extent, the TACPLAN design process has given us the

opportunity to examine the relationships among cognitive

requirements« analytical methodology, and interface technology--

all vith reference to one goal, to build an interactive plannin:

aid that will lead to the development of better plans faster an:

more efficiently.

56

inTi«n«nirT«niriT ninTit m mint T«i«Mmmiin lannii i nmi^i« iiliiMifin
3.0 PROGRESS ON THE DEVELOPMENT OF THE TACPLAN PROTOTYPE

3.1 Delivery System Configuration

The Carlisle experiments, refined analytical framework, TACPLA*:

functional description, and TACPLAN concept of operation all

determined the TACPLAN "delivery system." There were also a

number of overarching technical and applications guidelines that

determined the configuration. Compatibility across these two

data bases yielded the delivery configuration described belov.

3.1.1 "Personal" Planning Aid - A common technolcjy need

heard over and over again these days calls for the design and

development of "low cost," "portable," and "distributed"

"personal" decision aids. All of these terms are of course

relative. Low-cost generally means less expensive than a

minicomputer. Portable usually means "trans-portable," and

distributed usually means accessible and sometimes "networked."

But "personal" has some interesting connotations beyond the

literal derivatives. Personal should mean controllable and

manageable. It should mean non-threatening and supportive; and

it should mean flexible and adaptive. The planning process is

certainly compatible with these definitions.

Our observations of the planning process clearly suggest the

appropriateness of "personal aiding." There are also no

computational or display requirements that cannot be satisfied

with a personal aid. "Low cost," "portable," "distributed," ar.n

59
"personal" thus suggested to us the use of a microcomputer vith

interactive graphic display capabilities. We selected the IBM

Personal Computer (PC) vith ample storage and memory capabilities

(IBM/PC/XT). The TACPLAN prototype can easily be implemented or.

this microcomputer (though future versions may require the

capabilities of an IBM/PC/AT, vhich has larger memory and storage

capabilities). This system gives us the option of color or blacr*.

and vhite (monochrome) displays as veil as several input options.

The prototype vill permit keyboard and joystick/mouse input» it

vill also be configured to interact naturally and synergistically

vith the interactive graphic interface.

3.1.2 The Graphics Interface - TACPLAN vill have a graphics

interface comprised of an interactive video disc, video disc

player, and video disc/mapping control panel. Video disc-based

maps are made by first filming the area that is to become the

disc-based map. Perceptronics has already filmed the areas

necessary to implement the TACPLAN prototype. The NATO/Pact

areas of Western and Eastern Europe are already on disc at

multiple scales.

The video disc vill display actual maps of the Corps area of

responsibility at near perfect resolution. In fact, the video

disc-based map is clearer and much more flexible than any

conventional paper map. It permits planners to annotate vhat

appears on the the display (vhich vill be located right along

side the IBM/PC/XT display) vith nodes and symbols for later

60
reference. It also enables planners to create their own personal

symbols for annotation purposes. It supports de-cluttering, a

simple operation that removes (stores or erases) annotations.

The video disc-based mapping system also permits planners to

"fly" around a map, which, if laid out, might cover a warehouse

floor. Under joystick control, planners can fly across great

spans of maps, switch off to other kinds of data (photographs,

films, weapons descriptions), and even zoom in on an image or

location of particular interest.

The most important capability of the video disc-based mapping

syster that we plan to link to the (IBM/PC resident) TACPLA::

analytical systeri is its ability to communicate symbolically vit'r.

both the planner and the analytical side of TACPLAN'. When a

planner illustrates an enemy course of action by drawing it on

the video display, TACPLAK will immediately "know" something

about this course of action. It may know, for example, about the

terrain that might constrain or propel the course of action, it

may know about the relationship between the course of action and

force structure, and it may know about the relationships among

doctrine, orders of battle, and trafficability. Coordinates on

the video disc-based map vill be the link to analytical routines

and knowledge bases stored in TACPLAN. When planners illustrate

courses of action, annotate on the disc image, or erase an idea,

TACPLAN will know what has happened and react accordingly. The

/

nature, timeliness, and depth of the reaction will be determined

61
by the analytical routines and knowledge bases resident in

TACPLAN not by the capabilities of the interactive video disc.

In time it should be possible for a planner to draw a complete

route across a map« or deploy several divisions along a border,

and expect TACPLAN to react immediately, consistently, and

substantively about the implications of the route or plan (see

Section 4.1.2 for more information on this capability). Once the

technology that links locations on the video disc to analytical

routines and knowledge bases is fully developed, then the

possibilities are unlimited. Procedural L substantive rules

can be invoked spatially. Alternative plans can be generated

once a planner has entered his first candidate; and narrative

criticisr. and advice can be offered—all fron what the planner

does via TACPLAN" s graphic interface.

3.2 Status of TACPLAK Prototype

The TACPLAK prototype is currently under development. The target

delivery date is February 1985. The progress made to date in the

four primary areas is outlined below.

3.2.1 Menu Development - Nearly all of the menus that

appear in the TACPLAN concept of operation Appendix B) have beer,

programmed. These menus will «licit plans, missions» analytical

judgments, and rationale from planners as they develop a new

plan. This progress has been recently documented (Perccptronics,

1984).

62

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3.2.2 Analytical Routines - Th« many analytical routines

that vill drive the evaluation of terrain opportunities end

constraints, capabilities, and courses of action vill be modified

multi-attribute utility algorithms (Edwards, 1983» Gulick, 1960;

Andriole, 1963} Hill, et al., 1981). The major difference vill

be in the elicitation procedure, not in the calculation of

overall utility. These algorithms have been programmed to work

with the menu structures described above and in Appendix E.

TACPLAN's analytical routines will also include the specificaticr.

of the relationship between unintelligent analytical processing

and the procedural and substantive rule bases. This vrk is

ongoing.

3.2.3 Procedural and Substantive Rule Development -

Progress on the development of procedural and substantive rules

has been steady since the Carlisle experiments were conducted.

Kork has been iterative; a number of rules are included in the

TACPLATC concept of operation in Appendix B. The "depth" of the

rule bases vill be determined by our ability to derive rules fror

the experimental data, field manuals, interviews, and ongoing

discussions with experts at the Army Kar College.

3.2.4 IBM/PCAT Link to Interactive Video Disc - Progress

here has also been steady. As of this date, the IBM/PC/XT

"talks" with the video disc mapping system quite fluently. It is

possible, as it has been for some time now due to applications in

63
other areas« to annotate direcly onto the disc» zoom in and out,

•tore« erase« and retrieve annotations« and pick up and move, or

remove« tactical symbols. Work continues on the linkage between

locations on the map image and the procedural and substantive

knowledge bases.

3.3 The Iterative Strategy

special mention should be made of our progress to implement an

iterative strategy to the design and development of TACPLAK.

From the outset it was clear that given the number« nature« and

complexity of the hypotheses that we hoped to test, and given the

goal of incarnating the results in an interactive computer-based

aid« ve would need a procedure for checking and rechecking our

work. We were fortunate to find a dedicated group of expert

planners at the Arry War College, and have long since developed

an appreciation for codified doctrine and instruction. The

challenge lies in knowing how and when to iterate on research

progress as it is made. Our approach has involved the

development of a wide communications channel with a growing

number of designers« developers« and planners. We hope to wider.

the channel even more as TACPLAN emerges as a real system. In

retrospect« had it not been for the wide communications channel

we would never have thought to ask many of the questions posed

above« let alone ever had an opportunity to answer them.

We are also dedicated to the applications prototyping strategy.

Fuzzy analytical problems cannot be structured overnight or

64
straightforwardly. A great many requirements will only emerge

over time. Our expectation is that the TACPLAN prototype will

uncover even more requirements and, as a prototype, accelerate

the requirements definition and problem structuring processes.

It will also help institutionalize our iterative strategy.

65
4.0 PHASE TWO RESEARCH, DESIGN, AND DEVELOPMENT PLAN

Following the development of the TACPLAN prototype we hope to

continue our research and development along four distinct lines.

The first will involve the development of yet another analytical

framework. The second will be devoted to enhancing the TACPLA.*:

aid. The third will be devoted to testing and evaluation, while

the fourth to documentation.

4.1 Iterative Analytical Framework

Research progress thusfar has suggested a number of potentially

profitable directions in which we might move. They include the

integration of situational or "environmental" variables into the

planning process, the development of an embedded plan

"challenger," the continued development of procedural and

substantive rules (as well as other forms of knowledge

representation), and the expansion of the master planning

template to include additional command levels and different

planning problems, such as plan execution, plan monitoring, and

re-planning.

4.1.1 Environmental Variables - The Carlisle experiments

suggested that the planning process is susceptible to a variet}

of perturbations from the environment. The amount of time

available, for example, changes the way the process is

implemented. Similarly, differences in planning "style" can

v

66

JWAJWVWTMlllAMUMIAjlAa^«JTMrUU\*AJUl*/1*riJkniLAMAI^
affect the process, just as the psychophysical condition of the

planner can dramatically accelerate or retard the process.

Perhaps the most important environmental variable is time.

Research suggests that time can affect the problem-solving

process in some strange, counterintuitive vays (Nisbett and Ross,

1980} Nisbett, et al., 19B2). There is no correlation, for

example, between the amount of time available and the quality of

an analytical solution, given a bare minumum or benchmark

allotment of time. After a few hours, a problem that should have

taken a few minutes to solve is no easier to solve, and may in

fact be impossible to handle because of the extra time (Nisbett

and Ross, 1980). Additional time also provides an opportunity to

gather more information, information that presumably would

contribute to a better solution. But here too we find no

correlation between the amount of information available to a

problem-solver and the quality of the solution (Heuer, 1980»

Tversky and Kahneman, 1982} Nisbett, et al., 1982). Additional

time and information can complicate an otherwise straightforvari

problem-solving process.

The presence or absence of time should be treated as ar.

independent variable that may or may not impact upon quality,

specificity, consistency, reliability, and accuracy. If research

could confirm or reject these hypotheses then it would be

possible to re-design TACPLAN t£ accommodate the findings. It IF

interesting that the TACPLAN prototype will ask the planner about

his time constraints. While the prototype will only respond to

67
the 72 hour option, how might it respond to much shorter or

longer time constraints? Should it become more abbreviated in

dialogue and interaction, or should it become much more

deliberate? Notice that no suggestion was made regarding

possible relationships. Intuitively, one might assume that the

shorter the available time, the more abbreviated TACP1.A.. should

behave, and the longer the time, the more deliberate it should

be. But these relationships may indeed be backward. I_t may be

that TACPLAN should slow down—but attempt to do a little less--

vhen tine is short, and speed up—and attempt to do a^ little

more—when a lot of time is available.

The amount of time available to a planner will also affect the

amount of information brought to bear on the planning probier.

But hov? Ke know ^hat more information does not predict to

higher quality plans. But we also know that information paucity

predicts to bad plans. Where is the balance? What is the rigr.t

mix? Is it linked to the individual problem-solving style of the

planner? These are research questions for phase two.

The literature on cognitive style is inconsistent where its

application to computer-based problem-solving is concerned. Ir.

non-computer-based contexts there is evidence to suggest that

people learn and remember in some very different ways. Pagan,

Back, Stansell, Ausburn, Ausburn, Butler, and Huckaby (1979) have

identified ten cognitive styles appropos to the study of learning

systems. They include:

68

utuflitauflMMaMaukuai
• Field Dependent-Field Independent Styles

- An analytic as opposed to global

manner of perceiving. Field independent thinkers reflect the

ability to perceive visual stimuli

as separate from an embedded context}

e Impulsive-Reflective Styles

- Individual differences in speed

and errors vhen faced vith response uncertainty. Reflective

thinkers study hypotheses longer

and are usually correct vhen

choosing a response, vhile impulsive thinkers tend to select

the first response and are usually wrong)

e Visual-Haptic Styles

- Visual thinkers use their eyes

as the primary sensory intermediary, vhile haptic thinkers

rely much more on kinesthetic

and body orientation;

e Leveling-Sharpening Styles

- Levelers tend to incorporate

nev ideas vith old memories,

blurring the original image,

vhile sharpeners can add nev

ideas without distorting the

old;

e Constricted-Flexible Styles

- Constricted thinkers are very

susceptible to distraction

vhile flexible ones ate not;

e Broad-Narrow Categorization Styles

- Broad thinkers prefer broad

categorization, vhile narrow

thinkers prefer narrower categorizations;

69
• Scanning Styles

Differences among thinkers in

their extensiveness and intensity of attention!

e Tolerant-Intolerant Styles

Tolerant thinkers can accept

perceptions that are at variance with normal experiences,

while intolerant thinkers cannot}

e Cognitively Complex-Simple Styles

Complex thinkers see the world

multi-dimensionally and in a

discriminating manner« while

simple thinkers do not} and

e Conceptualizing Styles

- Different thinkers categorize

stimuli with perceived similarities and differences very differently} they also utilize different

approaches to conceptualization.

These styles suggest ways to present computer-generated

inform.ation and enhance man-computer interaction. For example•

visual perceptual types would benefit from the use of graphical

output (so long as the application called for it) just as

"scanners" would feel comfortable with abbreviated display

formats (so long as the impact of available time and inforr.atlo:

was well understood).

In addition to cognitive style» experience is part and parcel c:

the overall cognitive profile hypothesized here as integral to

enhanced computer-based problem-solving. Has the planner dealt

with similar problems before? How familiar is he with planning

70
•ids? What level "user" is he?

Cognitive variables are important because behavioral decisionmaking research suggests that decision-makers (and planners) tend

to underutilize appropriate inferencial strategies and

overutilize more primitive ones. The typical decison-maker, for

example, often fails or performs suboptimally when he undertakes

the inferencial tasks of covariation assessment, causal

explanation, and prediction. Significantly, all of these—and

many other mediational and perceptual—tasks are regularly

performed by planners. Suboptimal performance can be traced to a

%hole host of cognitive, personality, and perceptual phenomena

which are only partially understood by behavioral scientists. Ke

should thus focus upon the cognitive strengths and weaxnesses of

planners. Some relevant questions here concern the limits of

cognitive information processing in tactical planning, the

mechanics of problem-structuring in tactical planninc, the

identification and description of planning heuristics, and the

larger categorization of cr-nitive planning styles. All of these

are phase two research questions.

The last set of environmental variables to be examined are the

psychophysiological ones. At some point we must develop

verifiable generalizations about the psychophysiology of

decision-making and its subset activities, like planning. The

current state of this research includes some indicators which art

more or less useful for detecting emotional states in general and

especially for identifying peaks of arousal. Of great potential

71

^L.WLA.-WW^ d«'v»/w'\AftAÄ*.'vu\*A*Ajir.A-.»rji "jw^r
is the array of nonverbal» physical, physiological, and

especially psychophysiological indicators now beginning to

receive serious attention (Hopple, 1980) Druckman and Slater,

1979) Andreassi, 1980). It is not at all clear how such

indicators can be linked vith interactive computing, but they do

respresent a source of information that has to date been ignored

by all but a handful of systems designers (Donchin, 1979)

Andriole, 1982) Andriole and Hopple, 1982).

While it is easy to speculate about just how psychophysiological

indicators might be used to alter man-system interaction (lots of

mistakes make the system slov down) delayed responses change the

interaction mode, and the like), it is not clear which

combination of indicators and system reponses will enhance

performance. These are core phase two research questions.

Finally, the integration of environmental research findings into

TACPLAN has implications for the nature, size, and depth of

TACPLAN's knowledge bases (see Section 4.1.4 below). Should

environmental research findings be flexible and dynamic and

expressed as networks and scripts, or should they be rule-based

and relatively inflexibly invoked? Our limited research to date

suggests that rules would capture the essence of environmental

findings accurately and efficiently.

4.1.2 The Embedded "Challenger" - Perhapr . * greater

challenge in interactive systems design exists than one that

requires on-line system introspection. We intend to respond tc

72
the challenge in the context of tactical planning in a number of

ways. First, we intend to explore, via our expert planners at

the Army War College and elsewhere, the appropriate role of a

challenger or plan critic. We need to understand the potential

contributions that such a function can make, as veil as the

threats it may pose to the planning process. In preliminary

discussions with Army War College planners held on October A,

1984, it was suggested that a challenger—.if, informed by analoov

(Silvernan, 1983), precedent, experience, and doctrine--could be

an invaluable planning assistant. It could stimulate thought and

discussion and, when available time was short, help the planner

develop a plan through a set of iterative steps and judgments

made vis-a-vis a series of increasingly specifc and relevant

stravr.en. Imagine, then, a Corps commander with awesome

requirements and but t few hours time asking TACPLAN *.o respond

to his mission guidance with several concepts of operation that

the commander could massage until he felt as though he had hit

upon the optimal plan (which would then still be subjected to

TACPLAN's same critical personality).

In order for a challenger to perform credibly it must have

intelligence in a number of areas. First, it must have the

capability to develop a plan based upon incomplete, or "fuzzy"

information. It must be able to suggest alternatives to existing

concepts of operation, and it should be able to distinguish

between, compare, and contrast existing plans and alternatives

with plans that may have been developed six months ago. One vs73
to accomplish this is to store a limited but representative

number of alternative concepts of operation. These plans could

I •

be stored as complete concepts of operation that could be

altered by the planner as the situation required. Stored plans

•

are not unliice stored templates. They could be used to stimulate

the planner or accelerate the planning process.

Stored plans could be stored narratively and graphically. If a

planner asked TACPLAN to provide an alternative, TACPLAN would

respond by describing the plan narratively and by drawing it

directly onto the video disc-based map. The difficulty with this

approach is to pre-select a representative enough sample of plans

to be of real value to the full range of Corps (or whatever)

level planning problems.

This is an interesting problem. On the one hand it is easy to

find experienced tactical planners convinced that the number of

plannii g solutions for a given theater on a given command level

is relatively few; on the other hand, there are some that are

just as convinced that the number of optimal solutions is

infinite. As always, the "truth" lies somewhere in the middle.

It's discovery remains a research question for phase two.

If plans cannot be stored in toto, then the means by which

they can be quickly produced must be created. One approach might

involve the development of tactical planning scripts for multiple

command levels. Scripts are flexible data bases that permit the

creation of solutions from a set of variables that a planner

74
might provide. But scripts are not capable of producing infinite

numbers of plans» either. Deep scripts could probably produce

many plans that could be efficiently stored _in toto (or generated

on-line), but shallow scripts vould probably perform no better

than a reasonably large library of tactical plans.

Inference networks represent another strategy, as do the use cf

simple and complex rules, that would create plans fror,

information about the planning problem at hand. A rule-based

challenger would be fast and flexible, but perhaps not as expert

as a script- or inference network-based challenger.

The selection of one (or more) knowledge representation

strategies will depend upon a requirements/task analysis, an

assessment of the strengths and weaknesses of each strategy, ar.r.

and assessnent of the costs and benefits of integrating the

strategy into our delivery system.

4.1.3 Additional Command Levels and Planning Functions -

The TACrLA.\' prototype will only permit the development of a nev.

plan. It will not permit re-planning based upon adversary

movement and the evolution of battle. In other words, it will

not permit the planner to execute his plan and monitor its status

in much the same way a war game proceeds. TACPLAN also only

permits the development of Corps level plans. It does not perr.it

planning» except at a crude level, at the Division» Battalion,

Brigade, or Platoon command levels.

75
There are a number of reasons why widening the scope of TACPLAK's

applicability make sense. First# plan execution and monitoring

(and the re-planning that then occurs) are natural extensions of

the development of new plans, rep ess of command level. It is

difficult to determine the success >r failure of a plan, except

against an absract "ground truth," unless it is implemented. The

feedback planners get from battle, adversary responses, and

unanticipated events is the data used to re-plan, adapt, and

learn.

An argument can thus be made that ve have truncated the tactical

planning process by limiting TACPLAN's capabilities to plan

development. One solution is to build a gaming capability into,

TACPLAN that would permit planners to execute and monitor their

plans, and then re-plan as the need arises.

Simulated battle systems are numerous and efficient, but they

also have serious limitations. First, they have almost, all beer,

designed for war gaming/training purposes. They are therefore

instructional, not operational, in nature. Second, they do not

have interactive spatial capabilities. Third, thty are not

"intelligent." While phase two research will have implications

for the design and development of more sophisticated war games

and simulations, the primary focus will involve the merger of

three distinct technologies: simulation models, artificial

intelligence/knowledge representation techniques, and interactive

graphic interface technology. The work will be informed by

previous work in simulation, war gaming, and intelligent

76
adversary behavior (Lehner, 1983), but will go beyond this wor;.,

integrating new ideas and capabilities vithin the TACPLAN'

structure.

As with the development of the TACPLAN new plan challenger, a

variety of techniques will be explored. Should re-planning

direction be stored as alternative re-plans? Should they be regenerated each time Red moves, Blue fails, and unanticipated

events occur? Or should they emerge from the firing of rules?

Once a plan is executed, should infert.ices be made about its

likely success or failure? Would it be feasible and/or desirable

to shov planners ahead of time where and now their plans might

fail?

Khat about additional command levels? How does planning and

computer-aided planning change as one moves up or down the

comr.and level? Beyond the development of different templates

(comprised of different systems options, different evaluative

criteria, and different displays), hov should intangibles like

ris>: be handled? Hov should situational and cognitive style

variables be integrated (if at all)? How should the challenger

behave?

We propose in phase two to identify two additional command levels

and to develop two additional templates. The two levels will be

the Division and Platoon levels. The Division level will be

templated because it is important to test the synergism betweeen

two contiguous command levels, to determine the compatibility

between TACPLAN-developed Corps and Division plans. The Platoon

77
level will be templated because of the dramatic contrast between

Corps/Division and Platoon level plans, and because of the

hypotheses that Platoon problems will allow us to test. The

development of new templates will require us to reconstruct

planning at the two new levels, run some experiments (at the Army

War College and perhaps elsewhere), identify Division and Platoon

tasks, match the tasks to the best methods, determine the best

way to represent knowledge, and modify TACPLAN to accommodate the

new templates. Given that TACPLAN will be of modular design, ve

do not anticipate major changes to the program? the real

challenge lies in the development of the substantive templates.

The new templates will also be developed with reference to

research findings in the environmental and plan challenger areas.

We hope to expand the number of templates so that commanders at

all three levels can build, execute, and monitor plans against

different environmental backdrops with and without the aid of ?.

plan challenger.

In order to accomplish these additional command level and

functional tasks we plan to use the same geographical area and

similar scenarios (perhaps simulating a TACPLAN network). This

will facilitate testing across levels and guarantee a costeffective research approach.

4.1.4 Multiple Knowledge Representation Strategies -

Reference has been made throughout this report to procedural and

substantive rules. While the TACPLAN prototype will use rules tu

78

uajf
represent knowledge about terrain, trafficability, and the like,

phase two research may well suggest the need to represent

knowledge in some other ways. Alternatives include the use of

inference networks, frames, scripts, and a variety of hybird

techniques. The knowledge representation approach will be

determined by the task analyses that will be conducted for the

additional (Division and Platoon) command levels and additional

(execution and monitoring) functions.

The approach to problem structuring and interactive systems

design and development applied to the development of the TACPLA::

prototype will be applied to the design and development of the

proposed enhancements. We are committed to the systematic

analysis of military planning requirements and the use of

analytical methods and techniques that satisfy those

requirements. The iterative strategy with operational planners

will thus also be maintained.

4,2 Testing and Evaluation

The number of evaluations of interactive computer-based decisior.

support, information management, and forecasting systems is far

less than the number of systems that have been sponsored by the

U.S. government. In fact, reliable evaluations are conducted of

Jess than one in ten systems. There are a variety of reasons why

decision (and related) aids are seldom evaluated. Some are

bureaucratic and some are technical.

79
We plan to evaluate each iteration of TACPLAM in a number of ways

that we hope will provide us vith good feedback about vhat is

right and what is wrong with TACPLAN.

4.2.1 Testing and Evaluation at the Army War College - V.e

plan to test and evaluate TACPLAN at the Army War College. V.'e

plan to structure the evaluation around so-called "internal

criteria" and "external criteria." Internal criteria measure

planning performance defined in terms of planning thoroughness,

diagnosticity, and consistency. How many planning options are

considered? How many dimensions of value are used to determine

the likelihood of adversary courses of action? How consistent is

a planner from problem to problem? Answers to questjrns such as

these will permit us to measure hov well a planner structures a

plan. But what about the quality of the plan? External criteria

measure the extent to which the planning solution approximates

what is regarded as the correct solution to the planning probler,

or "ground truth." Ground truth is usually determined by a group

of independent planners who do not subsequently participate in the

planning evaluation.

A unique opportunity presents itself at the Army War College not

only because of the critical mass of expert planners that can be

found at Carlisle, but because the College has sponsored sone

planning research of its own. In several instances this research

has resulted in the development of an interactive planning aid.

One, known as CONSCREEN (Martin, Esoda, and Gulick, 1983), which

80
uses a decision theoretic framevork, can be evaluated against

TACPLAN to determine which aid best supports the tactical

planning process and why. The proposed plan calls for the

evaluation of three groups of expert planners: one with

CONSCREEN, one with TACPLAN, and one with neither aid. We may

also vary the situations (wartime versus peacetime), the command

levels, and planning functions to learn as much as we can about

the effect of alternative methods and displays on planning

performance.

4.2.2 Testing and Evaluation in Operational Environments -

If TACPLAN "passes" some important tests at the Army War College

we plan to transfer it to at least one operational or quasioperational setting for further testing and evaluation. This

plan is consistent with the overall applications prototyping

approach to interactive systems design and development. If the

early prototypes are successful—and successfully iterated upon--

then the last prototype should "graduate" to a more challenging

environment.

4.3 TACPLAN' Documentation

Upon completion of phase two of the project TACPLAN will be

documented according to conventional documentation procedures.

But in addition to the usual functional description, system

specification, and user manual ACPLAN's knowledge bases will be

documented. The evolving proble structuring methodology will

81
also be documented, especially as it has contributed to the

design and development of TACPLAN.

82
5.0 CONCLUSION

This report has documented progress made on the design and

development of an intelligent tactical planning aid. It is

important to note that the above reports on a planning project,

not on an artificial intelligence project, an interface project,

or a project on analytical methodology. The processes of

tactical planning have determined the research questions. Our

vork began vith an analysis of the planning process, not with an

assessnent of some interesting approaches to modeling or vith the

programming of a planning aid. As the research unfolded it

became clear that the only way the tactical planning process

could be supported by yet another aid is if that aid vas the

product of an interdisciplinary effort. We thus ended up

integrating a top-dovn, hierarchical, goal-directed model of the

planning process with some multi-attribute utility assessment

capabilities and knowledge bases, and packaged it all in a

computer-based aid with an intelligent graphic interface. Had vc

left any one of the ingredients out the aid would have had no

chance at all to succeed.

We have come full circle. We began with the notion that tactical

planning was a soft problem domain, unwilling to yield to any

single analytical perspective. Our research thusfar has

convinced us that we were right, and that there is much left to

do.

83

. A.WL'<A. rf^yL-WtrWUL J-U WL r^ lA. W <- TJ •
NOTES

International Information Systems, Inc., under sub-contract

to Perceptronies.

84
REFERENCES

ANDREASSI, L. (1980) Psychophysioloqy; Human Behavior and Hurar.

Response. New York: Oxford University Press.

ANDRIOLE, S.J., ed. (1985) Microcomputer Decision Support

Systems. Wellesley, Massachusetts: O.E.D. Information

Sciences.

_, ed. (1984) Applications in Artificial

Intelligence. Princeton, New Jersey: Petrocelli Books,

Inc.

(198 3) Interactive Computer-Based Systems Design

and Development. Princeton, New Jersey: Petrocelli Books,

Inc.

(1983) The Handbook of Probler-'-Solvinq; An

Analytical Methodology. Princeton, New Jersey: Petrocelli

Books, Inc.

(1982) "The Design of Microcomputer-Based Personal

Decision-Aiding Systems." IEEE Transactions on Systems,

Kan, and Cybernetics. Volume 12, Number A, July/August, pp.

463-469.

(1981) "Another Side to Command, Control, and

Communications: Computer-Based Information, Decision, and

Forecasting Systems." Signal: The Journal of the Armed

Forces Communications and Electronics Association. Volume

34, Number 6, March, pp. 15-22.

and G.W. HOPPLE (1982) "They're Only Human

Decision-Makers in Command and Control." Signal. Volume

36, Number 7, March, pp. 45-49,

BENNET, C.A. (1971) "Toward Empirical, Practicable,

Comprehensive Task Taxonomy." Human Factors. 13, pp. 229-

235.

BOAR, B.B. (1984) Application Prototyping. New York: WileyInterscience.

85
BROWN, R.V., A. KAHR, and C.P. PETERSON (1974) Decision Analysis

for the Manager. New York: Holt, Rinehart, and Winston.

CROLOTTE, A. and J. SALEH (1979) Development of JS Decision

Taxonomy for the Marine Command and Control Environment.

Woodland Hills, California: Perceptronics, Inc.

DEWEY, J. (1910) How We Think. New York: Macmillan.

DONCHIN, E. (1979) Command Psychophysiology. Proposal to the

Defense Advanced Research Projects Agency. CharraignUrbana, Illinois: The University of Illinois.

DRUCKMAN, D. and P.O. SLATER (1979) Nonverbal Communication:

Information Processing and Impression Management. Bethesda,

Maryland: Mathtech, Inc.

EDWARDS, W. Multiattribute Utility Assessment. Beveily Hills,

California: Sage Professional Papers.

FIEGENBAUM, E.A., A. BARR, and P.P. COHEN, eds. (1981, 1982,

1983) The Handbook of Artificial Intelligence. Los Altos,

California: William Kaufman.

GALIT2, W.O. (1980) Human Factors in Office Automation.

Atlanta, Georgia: Life Office Management Association.

GULICK, P.M. (1981) RSCREEN User's Manual. McLean, Virginia

Decisions and Designs, Inc.

(1980) Documentation of Decision Aiding Software

Introductory Guide. McLean, Virginia: Decisions and

Designs, Inc.

HAYES-ROTH, B. and F. HAYES-ROTH (1978) Cognitive Processes in

Planning. Santa Monica, California: The Rand Corporatio:

et al. (1980) Human Planning Processes. Santa

Monica, California: The Rand Corporation,

HAYES-ROTH, F., D.A. WATERMAN, AND D.B. LENAT (1983) Building

86
T

'-»-»- "»-» »-w-w-i

^y Expert Systems. Reading, Massachusetts: Addison-Kesley.

HILL, P.H., et al. (1980) Making Decisions. Reading,

Massachusetts: Addison-Wesley.

HOPPLE, G.W. (1980) Political Psychology and Biopolitics:

Assessing and Predicting Elite Behavior in Foreign Policy

Crises. Boulder, Colorado: Westview Press.

HUNTER, D.E. (1983) Political/Military Applications of bavesiar.

Analysis: Methodological Issues. Boulder, Colorado:

Kestview Press.

KELLY, O.K. and R.R. STEWART (1980) The Decision Template

Concept. McLean, Virginia: Decisions and Designs, Inc.

(1981 ) Program Completion Report: Advanced Decisior.

Technology Procram. McLean, Virginia: Decisions and

Designs, Inc.

and J. WEISS (1980) RSCREEN and OPGEN; Two Probler

Structuring Decision Aids Which Employ Decision Templates.

McLean, Virginia: Decisions and Designs, Inc.

LEH.MEP, P. (1983) Planning Under Adversity; The Development of

a Contingency Planner. McLean, Virginia: PAR Technology

Corporation, as noted in Basic Research in Behavioral and

Social Sciences. Alexandria, Virginia: Army Research

Institute.

MARTIN, A., P.M. ESODA, and P.M. GULICK (1983) CON'SCREEN'. McLean,

Virginia: Decisions and Designs, Inc.

MEISTER, D. (1976) Behavioral Foundations of System Develor-ent.

New York: John Wiley.

MUMFORD, E. and D. HENSHALL (1979) A Participatory Approach to

Computer Systems Design. New York: John Wiley.

NISBETT, R.E. and L. ROSS (1980) Human Inference: Strategies

and Shortcomings of Social Judgment. Englewood Cliffs, Nev

Jersey: Prentice-Hall.

87
NISBETT, R.E., et al. (1982) "Popular Induction: Infornation Is

Not Necessarily Informative," in D. KAHNEMAN, P. SLOVIC, and

A. TVERSKY, eds.. Judgment Under Uncertainty; Heuristics

and Biases. Cambridge, Massachusetts: Cambridge University

Press.

PEARL, J., A. LEAL, and J. SALEH (1982) "GODDESS: A GoalDirected Decision Structuring System." IEEE Transactions or.

Pattern Analysis and Machine Intelligence. Volume A,

Number 3, May.

PERCEPTROMCS, INC. (1984) TACPLAN Prototype Software

Documentation. Woodland Kills, California: Perceptronics,

Inc.

RAGAN, T.J., K.T. BACK, V. STANSELL, L.J. AUSBURN, F.B. AUSBUR:.,

P.A. BUTLER, and K. HUCKABY (1979) Cognitive Stvles: A

Review of the Literature. Norman, Oklahoma: University of

Oklahoma.

RAIFFA, H. (1968) Decision Analysis: Introductory Lectures or.

Choices Under Uncertainty. Reading, Massachusetts:

Addison-Kesley.

RAMSEY, K.R. and M.E. ATWOOD (1979) Human Factors in Cor.cuter

Systems: A Reviev of the Literature. Englewood, Colorado;

Science Applications, Inc.

SACERDOTI, E.D. (1974) "Planning in a Hierarchy of Abstraction

Spaces." Artificial Intelligence. Volume 5, Number 2, pc,

115-135.

(1977) A Structure for Plans and Behavior. Nev

York: American Elsevier.

SCHÄNK, R.C. (1978) Scripts, Plans, Goals, and Understanding

Hilsdale, N.-w Jersey: Earlbaum.

SILVERMAN, E.G. (1983) "Analogy in Systems Management: A

Theoretical Inquiry." IEEE Transactions on Systerrs, Man,

and Cybernetics. Volume 13, Number 6, November-December,

pp. 1049-1075.

88
TVERSKY, A. and D. KAHNEMAN (1982) "Judgment Under Uncertainty:

Heuristics and Biases," in D. KAHNEMAN, P. SLOVIC, and A.

TVERSKY, eds.. Judgment Under Uncertainty; Heuristics and

Biases. Cambridge, Massachusetts: Cambridge University

Press.

U.S. ARMY (1983) Staff Officer's Handbook (RB 101-999)

89
APPENDIX A

PROGRESS ON THE DEVELOPMENT OF A

GENERIC PROBLEM STRUCTURING/SYSTEMS DESIGN METHODOLOGY

90
A GENERIC PROBLEM STRUCTURING METHODOLOGY

FOR INTERACTIVE COMPUTER-BASED SYSTEMS DESIGN & DEVELOPMENT

Conventional computer-based systems design and development

directs us to first look at the requirements that the syster, is

intended to satisfy. It then suggests that some kind of modeling

of the mock system can redefine our requirements which, in turn,

will permit us to develop yet another functional description of

the system, and so forth until the model accurately represents

and satisfies the requirements. Then, and only then, we're tola,

should we turn to software design and engineering, and that these

steps should in turn determine our hardware configuration.

Bringing up the rear are "packaging" tasks, such as the

preparation of users' manuals, snd "technology transfer" tasks,

such as the introduction of the system into the targe*-,

environment.

Systf?r,s analysts differ on the role of the user in this process

but more and more we hear cries about the neglected user who nust

become involved in the systems design process if the system is tc

have any chance at all of satisfying user needs.

Finally, there are debates about the way the first iteration of

the system itself should be developed. Some hold that a thorough

requirements analysis will assure the development of a responsive

system, while others feel just as certain about the wisdom of

some kind of "prototyping" strategy. Because of the complexity

of the tasks that interactive systems are expected to perforr,

91
the prototyping strategy is becoming more and more popular.

The process itself is anchored in the quality of the requirements

analysis. If the analysis is conducted poorly then the system

vill fail. If it is conducted well then the system has a better

chance of succeeding. What does this mean? First it means that

without a structured, quantitative, verifiable requirements

analysis the system cannot possibly fulfill user needs. On the

other hand, a well conducted requirements analysis by no means

guarantees success.

Where is the problem here? Requirements analysis techniques are

more than fallible, and the techniques that we use to ..onvert

requirements to systems functions are only barely devM-^ped

This appendix presents some ideas about hov to identify and

structure analytical problems and hov to convert the proclers

into representative and responsive computer-based systems.

The Requirements Dilemma

Requirements analysis is boring, time-consuming, and expensive.

Unless conducted by professionals it is also often a waste of

time. Yet, without some formal "definition of requirerer.ts" man;

managers will not permit systems development, and the systems

that do get developed usually fall far short of expectations.

Budgetary constraints also assault the requirements analysis

92
process. It is usually very difficult to convince managers and

sponsors that a large front end investment will pay huge

analytical dividends during the entire length of the systems

design and development process.

Finally, because requirements analyses are often poorly conducted

a lot of managers and sponsors have a negative view of their

utility.

The Road to Systems Tarqetinc

Systems targeting consists of two assessments. One has to do

with the users and one with the tasks. "User profiling" is easy

compared with task analysis. There are several typologies of

user characterisitics, but a limited number of typological

criteria. For example, users can be categorized according to

their experience with interactive computing, according to their

job description, and/or according to how frequently they are

likely to use the system. There are also more exotic criteria,

like cognitive problem-solving style, that, on some occasions,

may be used to categorize users. One simple typology might thus

recognize "naive," "managerial," and "scientific technical"

users. Another might just recognize "frequent" and "infrequent"

users," while yet another might categorize users according to

their attitudes toward interactive computing. The key is

criteria-based classification that is consistent and appropriate

to your application. With regard to interactive tactical

computing for example, familiarity with computing, rank, and

93
training might be used to classify users.

The very first step should involve an assessment about user

characteristics. This assessment should not be static, but

should assume that the system will be successful in time and that

users will "graduate" from class to class.

The second step should turn to requirements definition. What

will the users be expected to do on the system? How will they be

expected to do it?

"Task profiling" requires an identification and assessment of the

tasks chat will convert into the funucional essence of the

systerr,. How should tasks be identified. Classic requirements

analysis tells us thac the best way to find out what someone does

for a living manually is to ask ther. to describe exactly what it

is that they do. Interviews and ad hoc working groups are very

popular techniques for identifying requirements, as are the use

of structured questionnaires and surveys. Simulations are alsc

popular, especially for very large and complex probler domains.

Conventional wisdom suggests using at least one of these

techniques to identify requirements. But which ones? Research

after the fact suggests that some techniques are better at

identifying certain requirements, but since you don't know the

requirements how can you know which technique to select?

The appraoch recommended here is to employ a generic task

taxonomy (or two) to identify the general class of requirements

94
you will have to satisfy. Two taxonomies appear below. The

first belongs to Wilbert 0. Galitz (1980), while the second to

r

Crolotte and Saleh (1979).

GALITZ' GENERIC TASK/BEHAVIOR TAXONOMY

• Cognitive Behavior

Information development/gathering

Information storage and retrieval

Reading and proofreading

Data analysis and calculation

Planning and scheduling

Problem-solving and decision-making

• "Social" Behavior

Telephoning

Dictating

Conferring

Meeting

• Procedural Behavior

Completing forms

Checking documents

• "Physical" Behavior

Filing and retrieving

Writing

Mail handling

Traveling

Copying and reproducing

Collating and sorting

Pickup and delivery

Typing and keying

Keeping calenders

Accessing equipment

THE CR0L0TTE/SALEH GENERIC TASK/BEHAVIOR TAXONOMY

• Perceptual Behavior

Searching for and Receiving Information

Detects

Inspects

95
f ^i ^1 ^F1

^' ^•"

Observes

Reads

Receives

— Scans

Surveys

Identifying Objects, Actions, and Events

Discriminates

Identifies

Locates

Mediational Behavior

Information Processing

Categorizes

Calculates

Codes

Computes

Interpolates

Itemizes

Tabulates

Translates

Problem-Solving and Decision Making

Analyzes

Calculates

Chhoses

Compares

Computes

Estimates

Plans

Communication Behavior

Advises

Answers

- Communicates

Directs

Indicates

Informs

Instructs

Requests

Transmits

Motor Behavior

Simple/Discrete

— Activates

— Closes

/

96
r>

Connects

Disconnects

Joins

Moves

Presses

— Sets

- Complex/Continuous

Adjusts

Aligns

Regulates

Synchronizes

— Tracks

These two taxonomies suggest a number of strategies. First, they

suggest a way to get an immediate handle on the nature of the

tasks that have to be modeled. They also suggest how to select

the "best" requirements analysis technique.

Before a preliminary requirements methods assessment takes place,

however, a general user/task matrix, like the one below, should

be constructed. The user and task categories in the figure are

not important. Thp key is to identify those general user and

task categories most likely to inform the selection of a.-i

appropriate requirements analysis method.

Research reported in Ramsey and Atwood (1979) suggests that

techniques have certain known strengths and weaknesses. For

example, all survey, questionnaire, and interview techniques are

notoriously inaccurate because they more often than not measure

users' perceptions of what they do »s veil as their biases and

oversights. As Ramsey and Atwood suggest, "users are expert at

doing jobs, not describing them." Ad hoc working groups also

suffer from these and related problems. Job analysis techniques

97

>:-. ^/^^^^.. *^^-:y^^
TASKS

COGNITIVE SOCIAL PROCEDURAL "PHYSICAL"

N

A

1

V

E

TASK/USER COMBINATIONS

CAN SUBSEQUENTLY BE USED

TO MODEL AND THEN DEVELOP

ACCURATELY TARGETED

COMPUTER-BASED PROBLEM

SOLVING SYSTEMS

A

E

N

R

A'

A

L

ST T N

C 1 El

IF C C

El HA

N C - L

. / 1

EXEMPLAR USER/TASK MATRIX

are best suited to the identification of clerical type tasks, not

cognitive ones. Simulation and gaming techniques can be'very

insightful. So-called "paper simulations," where prospective

users perform tasks similar to the target ones, can yield a great

deal of useful information in cognitive areas, as can the use of

formal "protocol analysis," a technique the_ requires prospective

users to comment extensively on tasks performed during a

simulated problem-solving session. This technique is especially

powerful for identifying cognitive and mediational tasks.

Finally, all-machine simulations, while expensive, may be the

only way to capture a variety of complex distributed tasks.

98
Our tactical planning problem fell primarily into the mediational

and cognitive task categories, but we didn't know exactly what

sub-tasks comprised the domain until we conducted the

requirements analysis. We used several techniques. First, we

structured a number of questions that we intended to ask at some

point during the analysis. These were critical questions

designed to measure the primary components of the planning

process, like probability assessment, criteria-based utility

assessment, and "payoff." Based upon an initial screening of our

taxonomies, we decided to conduct a paper simulation of the

planning process. Ke also decided to introduce aspects of the

protocol collection technique, aspects that we hoped would not ce

too intrusive. As a precaution, we decided to implement Dr.

Robert M. Sasmor's suggestion to videotape the

simulation/protocol collection procedure.

Videotaping yielded enormous dividends. Initially we decided tc

videotape because we felt that if protocol collection became tec

intrusive we could back off and still capture a great deal of

information. Videotape, we reasoned, would permit us to alter

the requirements analysis on-line. Dr. Sasmor assumed that

videotaping would yield insight into some tasks and requirements

that might otherwise go unstated or unobserved. All of his

assumptions were validated. Due to the constraints imposed by

the simulation, and the intrusions necessary to collect protocol

data, some tasks simply went unreferenced. When we observed the

videotape after the simulation we noticed that at times the words

99
—^ . • -F

were out of sync with what was happening during the simulation,

not because of any technical difficulty, but because our expert

planners found it easier to simply proceed with the planning

process than to describe it at every turn. Videotape fills in

the gaps in the "thinking aloud" and "participant observation"

process.

Videotape also documents the simulation in a way that audiotaping

simply cannot match. The recollections of the participant

observer are nearly as faulty as many of the utterances of the

subjects. Videotape helps validate the data immediately and long

after the experiments are completed. They also contribute to the

development of a task/requirements analysis data base that car.

be used by other investigators to study identical or similar

behavior.

After your general user/task matrix has suggested the

appropriateness of a particular requirements analysis technique

(or techniques), and you have implemented the methodis), then

you can redevelop your user/task matrix, as suggested below. lr.

the example, the matrix has been prepared for naive users

performing cognitive (planning) tasks.

The importance of structured requirements analysis cannot be

overstated. The approach outlined above will yield reliable task

definitions, but sometimes the process must be repeated several

times. Depending on the application, one or two requirements

analysis techniques may yield more than enough diagnostic data;

100

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sometimes, however/ several techniques must be applied several

times.

TASKS

COGNITIVE SOCIAL PROCEDURAL "PHYSICAL"

N

i A

1

1

V

TASK/USER COMBINATIONS

CAN SUBSEQUENTLY BE USED

TO MODEL AND THEN DEVELOP

ACCURATELY TARGETED

COMPUTER-BASED PROBLEM

SOLVING SYSTEMS

A

1

A

L

ST T N

C 1 El

IF C C

El HA

N C - L

• /

"NAIVE" USER/COGNITIVE TASK MATRIX

Tasks/Methods Matching

There are a variety of techniques for identifying just the right

combination of tasks and analytical methods. The method used for

our tactical planning work, as well as a method that can be

applied to many matching problems, involves the use of generic

methods inventories, such as the one below from Crolotte and

Saleh (1979).

101

'. -A . T. '. •.<.!*« *
AN ANALYTICAL METHODS INVENTORY

Decision Analytic Methods

- Value Models

Utility Assessment Techniques

Multi-Attribute Utility Analysis

Cost-Benefit Analysis

Discounting Models

Monte Carlo Methods

Group Utility Aggregation

Probability Models

Probability Elicitation

Bayesian Updating

Sensitivity Analysis

Group Probability Aggregation

Probability and Value Models

Subjective Expected Utility

Probabilistic Multi-Attribute Utility Analysis

Risk-Benefit Analysis

Decision Tree Structuring

Group Decision Analysis

Partial Information Based Decision Analysis

Fuzzy Decision Analysis

Operations Research Methods

- Analytic Models

Warfare Area Models

Lanchester's Theory of Combat

Game Theory

Detection and Search Techniques

Time Invariant Statistical Detection

Signal Detection

Search Modeling

Optimization Methods

— Simulation and War Gaming

Scheduling

Mat/.ematical Programming

Facilitation Techniques

Tactical Simulation

102

v-yj^ '." r'j"".' "-• V "." V V V V V '«• 'J" V V '«*.'•
Coverage Templates

Time/Distance Algorithms

• Computer Science Methods

Information Management Techniques

— .Data Base Organization

— Man-Machine Communication

— Message Processing

Pattern Kecognition Techniques

Clustering

Classification

Information and Discrimination Measures

Linear Discriminant Functions •

Artificial Intelligence Techniques

Problem Representation

Problem Solving

Learning Systems

Pattern-Directed Inference Systems

Planning Mechanisms

• Advanced Man-Machine Interface/Graphic Methods

Situation Displays

Dynamic Displays

Geographic Highlighting

Alternative Backgrounds

Automated Overlays

Selective Callup Techniques

Innovative Symbology

Pattern Analysis

— Center-of-Mass Computations

Movement Trend Analysis

Terrain Models

Graphic Summarization

Zooming/Windowing

Planning Tools

"What If" Projections

— Sketch Models

Optimal Pouting

Target Aggregation

103

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s

There are other useful inventories. The key is to use an

inventory to determine the range of methods that can be

incarnated into software to satisfy the tasks identified in the

requirements analysis. At a general level the following

relationships between behavior types and methods can be

specified:

SOME BEHAVIOR/METHODS RELATIONSHIPS

Perceptual Behavior > Computer Science & Operations Methods

Mediational Behavior > Decision Analytic/ Computer Science, &

Operations Research Methods

Communication Behavior > Computer Science &. Advanced >:ar.-

Machine Interface Methods

Motor Behavior -e> Computer Science & Advanced ManMachine Interface Methods

These relationships are general; before proceeding with i»

specific matching each sub-task should be compared with each submethod. After our plam.ing requirements analysis we compared as

many tasks and requircrreits as we could to methods and submethods in our generic ..iventory and identified what ve believes

were good matches. Subsequent testing ani evaluation will

determine if we were right and if our matching procedure should

be altered.

What about prospective users in this process? Users should be

104

fl^fl^^^C^
involved in both the task requirements analysis and the matching

procedure, not as members of the systems analysis or design

teams, but as the intended users. Their views should be

solicited, and their insights should be fed into the development

of a requirements definition and the tasks/methods matching

procedure. Systems designers cannot completely interpret

requirements data, nor can they always make completely perfect

tasks/methods matches. Users can help in both of these areas.

.Modeling

Probler. structuring for interactive systems design and

development is not complete until the matching process is

functionally modeled. The model itself can take many forms. It

can be narrative, in a flow-chart, mathematical, and/or in a

"storyboard." The storyboard approach to modeling is new and

powerful. Succinctly, storyboarding requires designers to design

every single display of the system in sequence with explanations

and descriptions of each display. All methods (and algorithms)

should be explained, and users should be able to get a solid feel

for hov the eventual system will function just be thumbing

through the pages of the storyboard.

Storyboards can be developed as strawmen or as blueprints for

programmers, or both. The sensible approach involves soliciting

help from the users to develop the storyboard; the "final" board

can then be treated as a programming agenda.

105
It is important to remember that the development of a storyboard

assumes that the developer(s) know something about the principles

of software design. This assumes an understanding of the range

of available input and output technology, display devices, and

the human factors connected with interactive systems operation.

Without this "knowledge base," the system cannot hope to satisfy

user requirements.

Models provide an opportunity to check and re-check your work.

The TACPLAN functional description went through several

iterations until we felt comfortable with its contents.

Prototypina

Problem domains that are especially resistent to requirements

analysis and tasks/methods matching make bad interactive syster

candidates. Over the years we have determined that the number of

domains that lend themselves well to computerization is

relatively small. This is because much of our past work has bee;,

in the cognitive and mediational areas, areas that are extremely

difficult to decompose and translate into analytical software.

The inherent difficulties connected with cognitive and

mediational modeling suggest the need for iteration.

Applications prototyping (Boar, 1984), the strategy that assumes

that several iterations of an interactive system are necessary

and desirable, has become very popular over the past few years.

Among other advantages, prototyping supports modular software

106

A;...^^:;^^^ ^
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engineering, permits user participation in the design process,

and protects project resources from the jaws of tunnel

programming. Most importantly, the applications prototyping

strategy permits you to keep the requirements analysis process

alive during the critical conversion process.

Prototyping assumes thavthe first version of your interactive

system will be rejected and modified. It assumes that users and

designers will have some difficulty identifying and defining

critical system functions, and that a limited amount of money

should be spent on each prototype until a durable system

definition emerges. The money saved should be plowed back into

"v* requirements defintion, tasks/methods matching, and rodeiir.c.

$

V.e have adopted the prototyping strategy to the design and

development of TACPLAK. The February 1985 version will be

evaluated by our expert planners from the Army War College as

well as by a team of independent systems designers. It will the:

be modified to accommodate their recommendations.

Guidelines

>

The structuring process is not nearly as straightforward as the

above implies. One must be flexible and adpative. Sometimes,

for example, just the right method cannot be found on any lists.

Sometimes a hybrid method must be developed. Sometimes it is

just impossible to define all of the tasks that the system should

perform. Problem structuring is an art, not necessarily a

107
science, even though many structuring steps are "quantitative"

and "verifiable." The real challenge lies in the combination of

steps. Often requirements analyses go well enough, only to be

followed b> a frustrating search for the right method.

The outcome is also never assured. Just as it is possible to

make a " .ood" decision that results in a "bad" outcome, so too is

it possible to carefully execute all of the structuring steps and

produce a really terrible interactive system. Hence, the

prototyping strategy, which can help mediate the impact of

unpredictable and imponderable events.

In the near future we hope to refine this whole approach even

more, especially by developing some exrlicit tasks/methods

matching procedures. We also hope to develop a generic r.ilitarv

task/behavior taxonorv and a generic military probler-solvir.s

methods inventory. We thus hope to develop and refine a r.ilitEr'

probier, structuring methodology comprised of task/behavior and

methods taxonomies, assessments and recommendations about

requirements analysis techniques, and algorithms for integrating

requirements methods, tasks, and analytical methods into

functional descriptions. There is also no reason to believe why

such a structuring methodology could not be incarnated in a

problem structuring "expert system."

108
APPENDIX B

TACPLAN CONCEPT OF OPERATION

109

^^^^:v:^v^-^^^:^-v x
TACPLAN CONCEPT OF OPERATION

Stephen J. Andriole

INTERNATIONAL INFORMATION SYSTEMS, INC,

802 WOODWARD ROAD

MARSHALL, VIRGINIA 22115

(703) 364-1504

July 1984

110

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• I M

.•.-

?

NOTE

This concept of operation was prepared to communicate TACPLAN's

design capabilities and goals to the systems analysis/programming

team. It represents the first strawman concept of operation that

has already undergone extensive changes.

This concept of operation also fails to present displays regarding

the operation of the interactive video disc-based interface. This

is because it was prepared prior to the completion of the IBM PC/XTbased tactical vidßo mapping (TVM) system that will be linked to

TACPLAN. References are made throughout this concept of operation

to "graphic equivalent displays," "annotations to the video disc,"

"drawing," and "illustrations," among other spatial and graphic

references. These reflect TACPLAN's dual screen configuration

which will permit planners to analytically construct a plan and then

see it unfold directly on a video disc-based map image.

Finally, this version of the concept of operation has been reviewed

by several expert planners and experts in the design and development

of interactive computer-based decision aids, as well as human factors experts and experts in the psychology of analytical computing.

These and other comments have been integrated into subsequent

concepts of operation.

111

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TACPLAN PROCESSOR ACTIVIATED

TACPLAN is a tactical planning processor that permits

commanders to develop, display, and test tactical plans under

conditions of uncertainty, threat, and peacetime. TACPLAN is

interactive and flexible; it is also knowledgeable uout tactical planning.

This first TACPLAN prototype permits users to develop,

display, and test tactical defensive plans in a NATO/PACT

threat scenario developed by the tactical planning experts at

the Army War College, Carlisle Barracks, Pennsylvania.

:

This prototype demonstrates a number of interactive problem structuring and problem solving approaches, including the

use of "burdenless" analytical spreadsheets, "graphic equivalent

displays," and "rules" expressed both as constraints on the

planning process and positive guidelines to rapid yet diagnostic tactical planning.

'.v

Future research will see the development of additional

(tactical and strategic) templates, situational flexibility,

automated "challenges" to the planner, additional rules, and

and the integration of an advanced situational display system

112

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TACPLAN

Options

[ 1 ) Develop a New Tactical Plan

\T\ Display Plan(s)

[T] Modify Plan(s)

Select An Option By Touching the Appropriate Function

Key (or By Moving the Cursor into the Option Box and

Pressing 'RETURN': 1 (2 or 3)

NOTE: I have assumed the use of the above input/dialogue

techniques. Your hardware and software may give you other

options, i.e., windows, other kinds of menus, and the like.

So long as the interaction is graceful and consistent across

option selection, then feel free to use whatever technique

makes the most development sense.

Also note that these are but the options for the first prototype. As the systems grows, so too will the initiul menu of

options.

113

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NEW PLAN

m

m

Options

Profile Tactical Planning Problem

State Mission Goals

Assess Area Characteristics

Assess Combat Capabilities

Determine Adversary Courses of Action

Determine Friendly Courses of Action

Develop a Concept of Operations

Select One Option by Inputting the Appropriate

Number (or by Moving the Cursor to the Box . .

* i £ i 31 • • •

114
PLANNING PROFILE

Nature of Planning Problem (Select One) :

[ 1] Defensive Operations

I 2 ] Offensive Operations

|3 | Movement of Military Units

Planning Problem #?: 1, 2, or 3

Command Level & Available Time (Select One Combination)

£

1^^ Time

Level\.

24 hrs. 48 hrs. 72 hrs. 72+ hrs.

Corps

Division

Brigade

Battalion

Platoon

Select One Combination By Inputting the Appropriate

Number (or By Moving the Cursor . . .): 1, 2, 3 .

Available Templa es (Select One):

[T] Western Europe/NATO/PACT

{2 ) Middle East/Lebanon

[3 I Middle East/Persian Gulf . . .

Select One Temp.late by . . .: 1,2, 3

115

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NOTE TO 'PLANNING PROFILE' DISPLAY: The prototype will only

be capable of assisting the planner in the development of a

defenseive plan at the Corps level for the Western European

Theater. The assumption, based on the scenario and requirements analysis, is that 72 hrs. are available for planning.

The PLANNING PROFILE display suggests that the PLAN PROCESSOR

can grow over time via the additional of templates for additional command levels and different geographical locations.

Templates include "knowledge" about command level responsibilities (within the context of the nature of the offensive/defensive planning problem), about the characteristics of the geographical area (terrain, weater, and the like), and information

about force strengths. In addition, each level/area template

combination will contain rules about the planning process, rules

expressed as constraints and rules expressed as positive guidelines about how to accelerate and improve the planning process.

Note that information pertaining to the prototype scenario

(defensive operations/corps level/Western Europe) is. traceable

to the requirements analysis/filming that took place at the

Army War College in January, 1984, the US Corps/Soviet Combined

Arms Army Command Pest Exercise (LETORT 8 4")") and the Army fFTs

that pertain to command decision-making, planning, and operations, especially the Staff Officers' Handbook, RB 101-999.

116

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MISSION STATEMENT 6 DESCRIPTION

Profile;

Defensive Operations/Corps Level/72 hrs./Western Europe

-•.

S

Specify & Illustrate Responsibilities t Goals

Describe Primary Goals: Graphically Illustrate Primary & Secondary Goals:

Designate Corps Areas of Interest & Influence

Designate Corps Rear Area &

Communications Zone

Designate Covering Force Area,

Forward Edge of the Battle Area

(FEBA), the Main Battle Area (MBA),

and

Describe Secondary Goals:

Designate the Areas of Divisior.

Interest & Influence in the Ccr^s

Areas of Influence & Interest

.'.

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NOTE: The designation of the corps areas of

interest (as well as the other designations)

accroding to Army conventions as illustrated

influence and

shouldbe made

below:

CORPS DEFENSE

- • — 1 UIM

The designations should be overlayed onto the video disc image

of Western Europe (the NATO/PACT boundary). Note also that the

scenario calls for the IX Corps (comprised of three divisions)

to occupy the MIDAG (the Middle Army Group's porition). All of

this is described in the scenario (LETORT, p. 2). The composition of IX Corps is a given that should be input by the hypothetical planner.

It is also important for force strengths to be designated. Rath,

than have the planner locate all of the 19 Soviet divisions, as

well as the NATO forces (all spelled out in LETORT), they should

be on the disc when the template is selected. The assumption

should be that intel has located ahd described adversary force

strength and that friendly force strength is known.

Finally, no instructions have been provided on the above display

for working with the graphic (video disc) image. The planner

will build the display according to the interface requirements

of the system; precise instructions can be added after the interface in completed.

118
RULES FOR MISSION STATEMENT & DESCRIPTION

1. The first "rule" for mission statement & description should

be model based. A model of the corps areas of interest and

influence (as presented above and as discussed in LETORT

and the Staff Officers' Handbook) should determine if

the planner has designated all of the requirements and if

the designations make ratio sense. Remember that IX corps

has three divisions, and that the divisions should be lined

up side by side, and so forth.

2. If possible, there should be synergism between the narrative

description of the goals and the graphic illustration of

same. A narrative out of sync with what the planner builds

on the video disc should flagged, for example. Narrative

should also be checked against the following doctrine:

Defensive Operations Must Be organized Around One or More Goals:

a. Cause adversary attack to fail;

b. Gain time;

c. Concentrate forces elsewhere;

d. Wear down adversary forces as prelude to offersive

operrtions;

e. Control essential terrain;

f. Retain tactical, strategic, or political objectives; and

g. Create opportunities to return to the offensive.

If these goals are not present in the narrrative of primary

and secondary goals the planner should be quizzed further

about what the defensive mission is all about . . .

119

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7*

AREA CHARACTERISTICS ASSESMENT

Defensive Operations/Corps Level/72 hrs./Western Europe

Ps. Forces

CharacN.

keristicsS.

NATO CORPS PACT

OpportunitiesConstraintsOpportunitiesConstraints Rationale 1

pranportakion/Telecommunica-

[tions Networks

1 2 3

•

4

[Topography 5 6 7 8

Wydrogrphy 9 10 11 12

Climate/

keather 13 14 15 16

'

urbanization 17 18 19 20 i1

Other

(Specify) 21 22 23 24

Designate the Opportunities, Constraints, & Rationale for Each

Force By Inputting the Appropriate Numbers (and Narrative) . . . Then ITj Stop & Store Assessments, [2j Return to Higher Menu, o:

TU"TT CCntii.nue Planning Process

NOTE: The information about terrain that should be embedded into

the system (for this scenario) is attached. It is extracted from

the LETORT scenario. The information about the % of urbanizatior.

and the location and nature of mountain ranges and rivers, for

example, can be on the disc image when it is displayed. On the

120

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other hand, it is not necessary to embed <»tl of this data. The

planner should be able to demonstrate knowledge of the terrain

by judging whether NATO or PACT has the advantage or disadvantage

in a given category and by providing rationale for the judgments.

For the demonstration of the TACPLAN system the terrain features

should probably be designated on the disc or embedded in the software.

RLLES FOR AREA CHARACTERISTICS ASSESSMENT

1. Can be gleaned from the attached.

2. Judgments and rationale regarding the opportunities and

constraints vis-a-vis area characterisitics will be used

subsequently to determine the prudence of judgments about

enemy courses of action and friendly courses of action, and,

ultimately, the concept of operations. That is, if judgments

are made regarding constraints at this point in the process,

and then forces are deployed inconsistently with these

judgments then the planner should be alerted to the inconsistency.

3. All area characterisitics must be specified; default should

not be permitted here (as it will in subsequent analytical

operations).

121

Uki&tiiti&&&&&^^
TEkKAIN ÜVtKVlEW

"Knowledge of Ihc country ib to a R«rierAl what u rlflt' 1H to an

infantryman and what rules of iritlunc-t ic arc to a geometrician.

If he does not know Che countryt he will do nothing but sake gross

mistakes. Without this knowledge hib other projects, be they

otherwise admirable, become ridiculous and often impracticHble."

Fredeiick tht> Great, l/V)

- To his generals -

The terrain within the XI Corps arc« ut Initrest within West Cciwany i» dlvliied

into two conirastirg parts for which the Mittelland Canal can gHtierally be

depicted as the demarcation line. in toe üouth are the Central Uplands with tie.

Harz Mountains, the Lower Saxon Hills, the Weser Hills and the Wcsrphaiian basin

being the major land forms. To the north is a sandy northern luwlünd stretching

to the west as far as the coastal morsiies. between these two par's, the Cential

Uplands and the northern lowland, runs the narrow fertile loess heit of the

Hcrcynian Foreland.

Northern Lowland - The fundamental physical dividing line runs just north 01 Inamschweig, Hannover and Osnabrück. Going north, there is a change from loess to

sand, from wheat to rye growing, from dense agricultural populations and

industrial towns to relatively unpopulated heaths. Although the area had bern

glaciated, it lay outside the limits of the latest glaciation an: the drifts nit

substantially leached, and the few remnants of terminal Moraines are greatly

eroded. Large sheets of sand and gravel aic predominant and these are divided

into a number of separate sandy block« (Ceesi) by cnannels cut by mtrlting glaciers. The growth of vast peat bogs, especially in ill drained depressions, was

favored by the oceanic climate, in the coastal areas and by the estuaries,

however, fertile marshes are found. The largest of the Geest blocks is the

Luneberg Heath (Luneburger Heide), in general overview, the area between Celle

and Hamburg, with the Weser drainage basin (glacial spillway) foraing Ch«

western boundary of the Geest block. The poorest land has been planted with

spruce. The sandy soil grows rye and potatoes and all of the valleys and swampy

depressions provide pasture for cattle. Although well north of the XI Corps

sector, the Worthe terminal Moralrie, which uses to over SOO feet and forms a

hilly spine from northwest to southeast across the Lüneburg Heath, is a noticeable terrain feature in the area. Tie Western Geest, to the vest of the Aller

and Weser Rivers, is lower and mote level than the Lüneburg Heath, and is split

into many separate blocks by poorly diaineil depressions. The whole region lacks

industry and the towns are small, with the exception of Oldenbutg.

llercynian Foreland loess belt - Th-'. Mil tel land Canal links the West and Kast

German Canal system along the liiu 01 ;ht Ht-rcynlan Foreland. This dry loess

terrain was used by the great Mdieval highway following the Hcrcynian Foreland

to the Elbe and beyond. Where routes out of the hills to the south emerged to

intersect the highway, towns such as Hennoser and Braunschweig, grew up. The highway, railway and canal routes fron, the Ruhr to East Germany and Berlin today

follow auch of the route of the estliei highway.

Portions of the lower Saxon Hills to the south intrude into the loess belt üS

widely spaced escarpments standing as wuodeo islands in wide stretches of loesscovered plain.

r 122

^tittfr^^ m^Mtä^^
The harz - This massif stretches some 100 Km from southeast to northwest. The

slates and granites of this terrain feature form a ml lire surface at IhUO to

2iU)U feet which in turn is overlooked by rhr K\rv, wlndswcpi granlli' rurkH nl

Liu- Kroi-kcn (J7A7 teet) Junt cant ol the intcriwil ftHial bound.ity. Kl roaawi 11 om

the Harz have cut narrow and steep-sided valleys. The ralnlnli and steep sJupcM

have encouraged the preservation of forests with beech and oak below 1300 feet

and spruce reaching up as far as the Brocken.

The Lower Saxon Hills - These hills are the outcrops of secondary rocks which

extend to the north, west and southwest from the Hart. Towards the south the

rocks are horizontal or gently domed, but in the north they are folded into a

series of southeast to northwest-trending anticlines end synclines. In the

south, where the hills extend a short distance to the south of Cottingen, the

rivers Leine and Weser have quite contrasting courses. Although the Leine is

the smaller stream, the nature of the land has allowed it, using a rift valley,

to open an 8 Km wide passage. This is the route followed oy the main northsouth railway and autobahn between southern Germany and the ports to the north.

The Weser, on the other hand, has cut across sandstone. The narrow, winding

valley created has traditionally not been used at a thoroughfare and no important towns are found along the Weser in that area. In the north the towns of

Helmstadt, Salzgitter and Hildesheim generally define the transition to the

loess belt. The folded rocks of the northern portion of the Lower Saxon Hills

have eroded into escarpments of sandstones and limestones, with alternating

small valleys of less resistant materials. The ridges are wooded with forest ol

beech or spruce and fruit trees cover the lower slopes. The loess-covered sm^ll

valleys contain large villages of half-timbered houses set among the open farn

fields. The Lower Saxon Hills project westward to, and in some cases beyond,

the Weser River and adjoin the Weser Hills.

The Weser Hills - These hills to the west of the Weser River are essentially «

westward extension of the Lower Saxon Hills and have many of the same cliariuteristics. Two notable features are two southwest facing escarpments, the

Wiehen Hills and tb ' Teutobruger Wald, which define the northern and western

boundaries of the Weser Hills. These hills push to the northwest, separatinr

the Westphalian Basin from the Northern Lowland.

The Westphalian Basin - The Kuhr, the industrial heart of North

Khlne-Westphalia, is probably the best known feature of the Westphalian Basir.

The heart of the basin, the area around Monster, has overlying cloys which fern:

the damp lowland of the Munsterland, which is broken onlv by orcasfonal low

tidges of sandstone or limestone. The basin's western outlook results in higl

rainfall and ideal grazing country. The countryside has what has been cnlled a

very "English" appearance with cows grazing in hedged fields and large isolated

tarmhouses set in clumps of trees.

Urbanization - The XI Corps area of interest rovers portions of three West

German states. The percent of built-up area in each of these is shown below

Hesse 10:

Lower Saxony 9.52

North Rhine-Westphalia 152

123

?>:v>v:^M:>M>fr^.:-^
Of particular Interest are Che Ruhr (U5S aq KB) and the Hannover area. The

urbanization in Che Hannover area ha* taken Che form of growth along the E-8

autobahn toward Brunswick In Che ea»t and Buckenburg in the weat. Smaller

cities such as Celle, Munster and Kassel also are growing in area and population. The Rhine-Ruhr area ia converging wich the Dutch Kandstand. When thib

convergence occurs, a single gigantic urban barrier 300 Kilometers long,

atretching down Che Rhine fro« Bonn Co Che Hook of Holland, will be formed.

Rivers - The Weser River first Cakes its name at Che confluence of the Uerra and

Fulda Rivera at Munden. Flowing generally north, Che shallow stream follows a

winding cou.se through hilly countryside until ic pierces the Wiehen HI 11B

escarpment at the Porta Westfallca at Minden and enters the northern lowland.

The river has maximum flow in the winter, at a time of least evaporation, anil a

period of summer low wster, and associated navigation difficulties. Drifting,

or continuous ice, appears on the Weser at Minden for fourteen days a year on

the average. North of Minden, the river is deep enough to provide a 1350 ton

water route to the Weser ports.

The Rhine River in its northward passage attains a width of 3000 feet as it

flows past Bonn. At Duisburg, in the Ruhr, there is one of Che world's largest

inland harbors. This harbor is ehe head of deep-sea navigation on the Rhine.

Almost immediately after crossing Che Dutch frontier at Emmerich, the Rhine

divides into two parallel streams, Che norchern being called by Che Dutch the

Neder Kyn and later the Lek, and Che southern, Che Waal. As ic flows through

the Dutch lowlands, the Rhine splits up again i&to the many arms of its delta, a

network of rivers and canals chat give access Co the great ports of Rotterdam,

Amsterdam and Antwerp, and finally, at Che Hook of Holland Co Che North Sea.

124

kittStiiMrt^*^^
•y

COMBAT CAPABILITY ASSESSMENTS

^^ssessment

Element^v.

Favor's

NATO

Favors

PACT

Rough

Equivalency Rationale

Composition 1 2 3

Numeric

Strength 4 5 6

Types & 1

of Weapons 7 8 9

C2 10 11 12

Location &

Disposition 13 14 15

Logistics 16 17 18

Reinforcements

19 20 21

Other

(Specify)

22 23 24

fe

Designate the Combat Elements that Favor NATO-or PACT Forces By

Inputting the Appropriate Numbers . . . Then Qj Stop & Store

Judgments, QQ Return to Higher Menu, or [3]" Continu«

Process

ie Planning

NOTE: This display/operation assumes that NATO & PACT forces

will be displayed on the video disc image with all of the proper

tactical symbology. The planner should be able to look at the

display and determine where the advantages/disadvantages are.

Rationale needs to be collected to check for consistency along

the way and, as always, to store the reasoning that went into

the formulation of the plan, reasoning that can be called upon

later for "audit" purposes or to refresh the memory of the

planner if the planning process is interrupted.

Also note that all assessment about combat capabilities are

necessarily relative to the sector and mission in question.

125

fr^^x:^^^ ^

v.y. L.-_. . _,.;j .v.,
RULES FOR COMBAT CAPABILITY ASSESSMENTS

There are a thousand rules regarding force mixes; we

cannot identify, develop, or include them all in the

prototype.

If NATO or PACT forces are adjudged to be vastly superior

to the other (by "winning" five or more elements) then:

If NATO forces win heavily then offensive operations

should be considered; and

If PACT forces win heavily then the planner (as corps

commander) should reformulate primary and secondary

goals.

Only two additional combat elements should be permitted ;

there are already eight "accepted" elements, sc we are

already over-extending the plus-or-minus . . . rule. At

the same time, at least but no more than two should provide

the planner with all of the flexibility needed to customize

his/her assessment.

126

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ADVERSARY COAs

Specify (Up to Five) Adversary Courses of Action (COAs)

1.

2.

3.

4.

5.

Designate Each COA Graphically By Symbol, Number, and/or Name:

NOTE: Instructions will have to be provided to the planner regarding how to annotate the video disc according to TVM or other

specs and symbology.

Evaluate Each COA With Reference to Explicit Criteria:

^^^COAs

Criteri'Ä»^

Favors

COA #1

Favors

COA #2

Favors

COA #3

Favors

COA #4

Favors

COA *5 Rationale j

Military

Intentions 1 2 3 4 5

Area Characteristics 6 7 8 9 10

Combat

Capability 11 12 13 14 15

Doctrine 16 1

17 18 19 20

Other(S)

Specify 21 22 23 24 25

Evalauate the COAs By Inputting the Appropriate Numbers . . .

Then fl) Stop & Store Judgments, ^2} Return to Higher Menu, [

Continue Planning Process

127

."*• k» k •

^: I^:^:^^
NOTE: TACPLAN should determine the obvious "winner" by stating

the COA that has been adjudged the most likely by the tactical

planner. It is important that the planner be made aware that

his/her judgments are with reference to a likelihood, that the

results of the judgmental analysis should produce a ranked list

of the most-to-least likely adversary COA:

***COA #3 IS THE MOST LIKELY COA***

***COA #2 IS THE LEAST LIKELY***

***COAs #1, 4, & 5 ARE RELATIVELY UNLIKELY***

However, if rough parity exists, then the system should require

the planner to implement the next, more diagnostic, method for

evaluating COAs. This method, a derivative of the multi-attribute

utility (MAU) approach, requires the planner to be more explicit

and quantitative in his/her evaluation(s), though only a little

more burdensome. In fact, the version of the MAU technique

described below is much less burdensome then existing MAU techniques, though no less diagnostic (assuming that the tactical

planner is not a novice planner).

At the same time, the planner should have the option of implemer.tir.j

the second, more diagnostic, technique if he or she so desires. The

second analysis might be conducted when there is some doubt about

the initial judgmental rankings or when the rankings produce no

clear "winner."

Like all of the assessments

area characterisitics, adve

the results will be used to

concept of operation—if th

her own judgments throughou

certain that the graphic re

will become part of the con

in this case, to lower leve

sistent with what TACPLAN a

general and the planner's j

the "rules" which govern th

be implemented when the con

will be true for both posit

made during the planning process (like

rsary COAs, friendly COAs, and the like),

determine—during formulation of the

e planner has been consistent with his/

t the planning process and to make

presentations of the plan--which

cept of operations distributed,

1 (division) commanders, is conIready knows about planning in

udgments in particular. Many of

e operation of TACPLAN will thus

cept of operation is input. This

ive and negative (constraint) rules.

128

iä££ä&ä^^ ,.w:.
ADVERSARY COAs

:-.'

Specify (Up to Five) Adversary Courses of Action;

1.

2.

3.

4.

5.

Evaluate & Score Each COA With Reference to Each Criterion

! ^^-QOAs

Criteri^t^^ COA #1 COA «2 COA #3 COA M COA *5

Military

Intentions

Area Characterisitics

Combat

Capability

Doctrine

Other(s)

Specify

TOTALS

Score the COAs eith reference' to the criteria by inputting a scale

of your choice (1 « best, 5 » worst; 1 « best, 10 « worst; etc.);

TACPLAN will total your judgments i prepare a ranked listing.

Then jT) Stop & Store Judgments, [^ Return to Higher Menu, or [T)

Continue Planning Process

129

•ViAa-Vj^wC^^wl
NOTE: The planner should be able to weight the criteria as

well. This can be either a requirement or only suggested as

yet another level of diagnosis. Weighting is a natural companion

of scoring, but scoring should always be done first and before

and totals are displayed; ideallly the user would score the

criteria and then on another VDT page, the weights should be

elicited. This guards against biasing.

The weighting procedure is as attached. All the planner has to

do is determine which of the criterion is the most important,

second most important, third most important, . . . and least

important; alternatively, the planner can input percentages

about the "worth" of the criteria, percentages that may or

may not total to 100% (TACPLAN can normalize more or less than

100% judgments).

130

XXXi^^
HANDBOOK

OF

PROBLEM

SOLVING

An Analytical Methodoloöy

Stephen J. Andriole

•P»•van«

(•wy«A/*>4nt««an

131

tä^teä^^ «JJSS
PRESCRIPTION IIS

ods permit decision-makers to »elect a specific option without regard to any uncertain events or conditions that night mrt an impact upon the selection process.

Other methods enable decision-makers to deal with uncertainty in a manner which

permits them to maximize the value connected with individual options.

Option Selection Under Conditions ofRelative Certainty

Imagine a scenario whe/e you have decided to purchase a new car. You have

the money, and cars are available, so there is no uncertainty connected with the decision. But you have looked at and test-driven no less than seven cars and cannot

make up your mind.

The first step toward a quick selection involves making a list of the factors (or

selection criteria) important to your choice. They might very well include cost, gas

mileage, comfort, appearance, and overall quality of construction. The next step is

to define these criteria so they will be consistently used when you perform the next

step, scoring (on a scale of 1 to 10), as suggested in Figure 7.1.

The scoring itself can be done in absolute terms, quickly, and extremely easily,

as Figure 7.1 suggests. Note also that car #1, with a score of 31 is the winner and

car #4 a close runner-up with a score of 30. But note that #1 is extremely expensive

and that #4 is extremely inexpensive. One ariation to the process, then, might involve weighting the criteria in terms of thei; overall importance to the decision. In

the new car selection example, if cost were l.eavily weighted in terms ofimportance,

then car #1 could not possible "win."

j -^^^ Car»

• 1 • 2 • 3 • 4 • S • 6 • 7

| COST 7

GAS MILEAGE 1

COMFORT 3

APPEARANCE 7

OUALITY 6

TOTAL 27 25 22 24 ii 1

FIGURE 7.1

QUICK (UNWEIGHTED) NEW CAR SELECTION

132

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rw 116 HANDBOOK OF PROBLEM-SOLVING: An Amlytical Mcthodoln|y

The steps necessary to conduct a weighted attribute analysis, as suggested in

Percy Hill, et. tVt Making Decisions (Reading, MA: Addison-Wesley, 1980), are as

follows:

1. Identify decision alternatives.

2. Define decision alternatives.

3. Identify evaluation selection criteria.

4. Define evaluation selection criteria.

5. Rank the criteria according to their relative importance.

6. Determine criteria weighting factors according to the formula

below:

Evaluative Rank/ Weighting

Criteria Points Factors

Cost 1/5 5/15-0.33+

Mileage 2/4 4/15-0.26+

Appearance 3/3 3/15-0.20+

Quality 4/2 2/15-0.13+

Comfort 5/1 1/15-0.05+

15 15/15-1.0

7. Develop a decision matrix, as suggested in Figure 7.2.

8. Score the alternatives against the criteria, one criteria at a time,

on a 10-to-l (highest-to-lowest) scale.

9. Multiply the scores X the weighting factors.

10. Select the alternative with the highest total/weighted score,

as suggested in Figure 7.3.

The above unweighted and weighted decision option selection methods are useful

when the alternatives are relatively few in number (ten or less), when the criteria

are few and one dimensional, and when speed is required. But they are not the most

diagnostic methods available.

Another "multi-attribute utility" evaluation method formalizes and extends

the above procedures. Developed by Ward Edwards in his "How to Use Multi-Attri133

^vi^^^^
PRESCRimON 117

Criterii ^s. c

Can # 1 «2 «3 «4 • 5 «6 m 7

Cost 0.33

////// /

Gas Mileage 0.26

/

/

////

/

Appearance 0.20

///////

Quality 0.13

/

//A///

Comfort 0.06

/////

//

TOTAL 1.00

FIGURE 7.2

WEIGHTED SELECTION MATRIX

134

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RULES FOR ADVERSARY COA EVALUATION

1. The number of COAs considered should be limited to five.

2. The number of criteria should be limited to seven, which

means that the planner can add only three additional

criteria.

3. The planner must consistently implement one scoring scale,

a scale that should be used throughout the planning process.

For example, if the planner selects a one to five scale, the;.

when friendly COAs are evaluated the 1 to 5 scale should be

treated by TACPLAN as a given.

4. It is not necessary to have the planner define the criteria

in detail, at least not for this initial version of the

system. The hypothesis is that far too much detail has

been required of the planner/decision-maker in the past

because of the desire to forcefit preferred methods onto

the analytical problem-solving process. We shall try to

maintain diagnosticity and drastically reduce the amount

of work the planner has to do.

5. it should be possible to equally weight up to three criteria

but no more than three. Diagnosticity suffers when more thar.

three (out of a posssible seven) are equally weighted. If,

however, no additional criteria are added then the planner

should only be allowed to equally weight two (out of

the four) criteria.

6. After the COAs have been ranked according to their likelihood

(by whatever means) then TACPLAN should automatically display

the rankings by means of an annotation on the video disc.

As the rankings change, becajse of modifications to the

process or sensitivity analyses, then the video disc

annotations should also change.

7. In keeping with the burdenless concept, rationale should

only re required if the the results of the COA evaluation

are not clear-cut, regardless of the evaluation method

selected. If there is little distance between the likelihood of three or more COAs, then rationale should be

required; if there is a clear-cut winner then the planner

should not have to labor over long narrative input. The

idea is to require the planner to input just enough information to make diagnostic evaluations and plans and to have

enough information for later study. While rationale would

always be nice in principle, at this point, given the user

load, it can be skipped if the results of the evaluation

are clear.

136

a^^is^rf^^^^sM^^; &&££&V.v; ;<&. .i»:tt^^M^^^^M»»ß:^
8. The COA evaluation should be guided with judgments

previously made about area characteristics and combat

capabilities. The most likely COA should be consistent

with judgments made about adversary terrain constraints

and combat capabilities, for example. If a COA is

determined to be highly likely, but conflicts with judgments about enemy terrain constraints and capabilities

then the planner should be made aware of the discrepency

137

mti^rtis^^^^
."

DEFENSIVE DEPLOYMENT/COAs

NOTE: This procedure will be exactly the saine as the evaluation

of adversary COAs except that the criteria tr be used are as

follows:

1. Adherance to Goals

2. Combat Capability

3. Area Characteristics

V

4. Adversary COAs

5. Adversary Combat Capabilities

6. Adversary Doctrine

7. Other(s) (Specify)

The two level procedure (favor/no favor and numeric scoring)

procedures should both be available, just as they are for the

evaluation of adversary COAs. Only five COAs shoulu be allowed

and the planner should have the option of implementing the more

sophisticated evaluation procedure if he or she wants, so long

as none of the rules are violated.

Again, the planner will have to designate COAs by working with

the video disc. The attached provides some examples of what

visual/graphic courses of action look like. They should be

used for both adversary and friendly COAs annotations. Here

is an example of graphic equivalency in the system, where the

planner thinks verbally about a COA and then sees it unfold

on the screen.

138

V.

ü&täti^^
RULES FOR FRIENDLY COA EVALUATION

1. The number oT COAs considered should be limited to five.

2. The number of criteria should be limited to nine, permitting

the user to add only two additional criteria.

3. The planner must consistently implement one scoring scale,

as suggested in the rules for the evaluation of adversary

COAs.

4. Rules 4-8 for adversary COA evaluation should be applied

faithfully to the evaluation of friendly COAs.

139

S^m&fami^umm^ :&&& ^vj
CORPS COMMANDER'S CONCEPT OF OPERATION

Steps

S Distribute Forces Graphically Along Primary

Adversary COA, including reinforcements

0 Designate Priority of Deployment; Sequence the

Movement of Adversary & Friendly (Defensive)

Units

H Formulate, if D«sired, a Contingency Plan, Based

Upon the Next Most Likely COA, or in the Event of

a Failed Plan, by Re-taking Steps 1, 2, and 3 . .

H Develop a narrative of Primary & Continger-ry Plans

NOTE: The planner

to the evaluations

most likely advers

in response to the

into the concept o

friendly and adver

and the like. The

combination should

completes the eval

of his/her concept

forces in detail a

can also locate th

the adversary COA,

will take the above steps with reference

of friendly and adversary COAs above. The

ary COA and the optimal friendly COA (conce

most likely adversary COA) should feed dir

f operation. The graphic illustrations of

sary COAs will be general, with sweeping ar

most likely/optimal adversary/friendly COA

be displayed on the video disc when the pi

uation phases and moves on to the formulati

of operation. Then the planner can distn

long the adversary and friendly COAs. He/s

e specific adversary combat capabilities al

and so forth.

i vec

ectl\

rows ,

ar.r.c ;

or.

butc

he

one:

The planner should also designate graphically the phasing or »equer.c\

of the attack and the defensive operations. The attached should

help conceive of how this should be done on the video disc, but

conceptually it is straightforward.

The planner can then retake the steps to formulate a contingency

plan based upon the next most likely COA, and so forth, by recycling through the steps in the concept of operation phase of

the planning process.

140

iaii^^r: N>> . ••"-->;-- iV A*. a\ j^_ M".

\ <.'
I

and Doctrine Command, NATO «rmlet

committed to Forward Defense on the

Central Front agreed to standardize the

general principles of a tactic described as

Activt Dt/tnst. In terms of genealogy,

this was a tactic whose design from the

outset was predicated on the context of

Forward Defense. The originality of the

Active Defense is not in th« invention of

radically new methods of fighting but.

rather, in the «ay that it borrows concepts from established tactics, combines

them in a unique sequence, and applies

them at a lower level of organizancn*

implementation.

The first phase in the active defer.st

sequence begins immediately at the in

ter-bloc border with a eovtring /o'C(

Traditional Defensive Tactics

Mobile Defense Delay/Screening Defense

* Acjrcsslve; seeks Initiative - Takes high risks

+ Sets up opponent's _ Divides for«

ovtr-cxlcnsion

- Vulnerable to multiple

penetration 4 Lars« Counterattack

Arca/Hcdgchog Defense

4 Screens broad frontage - Gaps; multiple pcnetraiiors

Repeated ambush slows - Rapidly-declining combat

attack power

4 Avoids decisive engagement - Danger of dlsorganizailon

Positional Defense

* Slows down high-speed - Easily enveloped; units Ipst

* '

k

- Passive; no counleratia'ck

•^ Takes advaniace of cxisiinE „ . . . .

terrain Piecemeal defeat

* Needs minimal

command/control

4- Maximum preparation of - Passive; waits for attack

,erT',n

- Vulnerable to massed

•f Attacker must suffer heavy echelon breakthrou£h

•"ri,l0n- Brillle; difficult to disengage

4- No gaps for penetration

Armad t ••! JOURNAL Intamational/May 19B4

141

i&tt^X^ VJ^: .-. . _ ^. .-.v..-. .-. f-• -"- ^"-^frää&BS WASk.tV.VJ
SÄ

nificant »mitank btrrier, most of the terrain it broken by large ttretchei of forest

(the hilly woods of central and southern

Germany) or »oft toil (the marshes of

northern Germany). While the density

of roads, from Autobahn to logging trail,

makes every kilometer accessible to

tracked vehicles, this terrain is nevertheless not particularly conducive to a highspeed advance by massed armor. From

the perspective of trafTicability, as opposed to topography, the infamous

North German Plain is not an area like

the Russian Steppe where entire armored divisions, not to mention tank

The originality of the Active

Defense Is not In the

Invention of radically new

methods of fighting but,

rather, In the way that It

borrows concepts from

established tactics,

combines them In a unique

sequence and applies them

at a lower level of

organizational

Implementation.

armies, can meander at will. Rather, an

attacker must repeatedly subdivide units

on multiple axes and proceed through

constricted passages—often in march

formation. Likewise, one looks in vain

for the Fulda gap, which in reality is

merely a series of narrow mini-gaps with

relatively higher viscosity than tome

other sectors of the front. Scattered

across the breadth of Germany are large

built-up urban areas which, while they

permit high offensive trafTicability, also

give the defender unusually favriabie

points of observation, fields of fire, and

protective cover. Even where open terrain is prevalent, it is dotted by a town.

village, or hamlet an average of once

every six kilometers.

For the defender, this terrain is best

tuited to the antitank ambush, where,

from under cover and from h.ill defilade,

the lead elements of an armored column

are suddenly engaged from multiple directions. By the time the attacker recovers from the surprise and prepares for an

organized assault with heavy tuppressive

artillery support, the defender his covertly withdrawn to the next ambush

tite several kilometers down the corridor—leaving behind a favorable I -6 loss

exchange rate. This tactic, repeated over

and over, can slow the most aggressive

armored advance to a craw] and drain

the attacker of his quantitative superiority.

Nevertheless, the very aspects of West

German terrain which can permit a de04

fender to fight outnumbered and win,

can also work against him. The absence

of "big" gaps means that the defender

cannot concentrate his forces In a few

high-threat areas, but must cover all the

routes of potential Ingress. The terrain ia

only of advantage to the defender if be

occupies it; otherwise it permits the attacker to maneuver large numbers of

small formations very deeply, very

quickly. The high interConnectivity of

these many corridors means that the defender cannot merely focus on east/west

routes—lest an attacker veer into the

flank or rear of an unsuspecting adjacent

defender. To realize the full advantage of

these terrain compartments requires the

defender to be in position long enough to

undertake considerable preparation (demolition and emplacement of obstacles

to channelize movement; mining of

high-speed approaches; construction of

alternate hull defilade firing positions for

vehicles; entrenchment, overhead cover,

and barbed wire for infantry).

While defended forests and urban areas

make rapid mechanized assault difficult

for the ofTensive, they are particularly

vulnerable to traditional dismounted infantry infiltration. To truly hold this

terrain for any length of time requires a

high concentration of defenders—indeed

the larger urban areas are manpowei

"black holes" capable of consuming multiple divisions.

The most significant aspect of West

German terrain is where the poor trafTicability areas are located with respect i

operational depth. Within the 150 kilometer band discussed earlier, the most

defensible terrain for virtually the entire

breadth of the front is near the inter-blo:-

border. Behind the first SO kilometers.

the terrain tends to open up—corridors

become wider, with a higher degree o

r

intersection, and north/south movemeris easier—none of which facilitates a stable defense. Beyond 100 kilometers there

are large areas of concentrated urbanization and forest, but their isolation and

sheer size do not provide the interlocking

defensibility available near the border

Density: The Ratio of Force to

Space

One of the major diflicult.es of selecting where to deferd is correlating the

position to the quantity of forces needed

to hold it successfully. For over a generIncreased Density from

Defending Forward

Fonvard Defense

• Uti Ihm 40 km

from Oordcr

• Frenlitc - 240 km

• Ottemlblc Terrain

• Stable Inlcrlocklni

Dcftme

• Allick Contained

• Dcnilly and Rttcrvti

Reanvard Defense

• 120 km from

Border

• rrenla|( » 3(0 km

• Open Terrain

• Lou of Key

Clilct and Major

LOCi

• Offenaivt

Momentum /

• Reduced Oeirtlty

•r Na Retervei

Source: Charles T. Kampa. Jr.. Her Ctp: Tht Nurnbtrg Plnetr. SPI. \Y. 19"

142 Arm«d Foreaa JOURNAL Intornat.onai/Me,

i:^:^ ^
--•

vulnenble reir area that • Forward Defense provides meant tht*, unless the

Soviets can cITect a large number of timulttneoui deep penetrations all across

the front, the few that do get through

can be sealed off and destroyed by reserves The more mobilization time

NATO has: the greater the difficulty the

Warsaw Pact first echelon will have in

penetrating the Forward Defense line;

the stronger the rear area security will be

to contain raiding forces; and the larger

the tactical and operational reserves that

will be available to destroy an OMG.

The ultimate success of a Forward

Defense Is less dependent upon the relative margin of superiority of the aggres

tor than on the amount of clear and

unambiguous warning that an attack is

imminent, and the strength ofthe political will of NATO to respond before it

ocean.

p

1

i

Di

High

- i

I

e

m

2

Low

Types of |

sfensive Tactics !

Screen j

i Mobile * !

S |

Active

| • |

! Positional

Area * i

1 * \

i

De,p ' Depth "Sh"low

Tactical Development

There is no aspect of Forward Defense that has produced more controversy than the discussion of which tactics

are best There arc virtually at many

variants at there are commentators. Nor

is it uncommon for critics to describe a

certain "straw man" tactic at endemic to

Forward Defense and then attack it in

detail.

In fact, the strategy of Forward Defense could be implemented by any number of different tactics, and at the time

NATO adopted it, each national army

tended to have its own preference (refleeting historical experience, level of

technological modernization, limitations

of force structure, and the terrain it

would have to defend). Thus, the British

tended to prefer the security ofthe traditional positional de/tnse with dug-in infantry maximizing the ute of prepared

terrain and natural obttades, with armor dispersed to support them. The

Germans were attracted to the flexibility

of the mobile de/tnse (at least at the

brigade/battalion level), with infantry

deployed in ttrong points and a heavy

42

armored force withheld in reserve for

local counterattacks. The French, with

only light units and a broad frontage,

were the most experimental—refining

the delay or screen;ng action into a form

of antitank guerrilla warfare. The US

Army reflected all three types of tactics,

depending on the branch of the unit:

delay for cavalry regiments; positional

defense for infantry divisions; and mobile defense for armored brigades.

Each of these tactics has its inherent

strengths and weaknesses, and their difference are illustrated on page 4S by

contrasting the way they employ maneuverability and depth. In terms of Forward Defense, the positional tactic

makes the best use ofterrain preparation

and density of fire, offers the maximum

initial resistance, but also takes the longest time to set up. Given the passivity

of the positional defense, an attacker

with superior forces can ultimately concentrate against a narrow sector, and

once penetrated, the static nature of this

tactic means it can be rolled up by shallow envelopment. The mobile defense is

most dangerous to an attacker because it

threatens him with decisive engagement

(rather than incremental losses) when he

is overextended—with open flanks—and

least prepared for it.

However, unless the mobile tactic hat

a tufllciently strong defensive line in

front of it to channelize the route of

attack and prevent multiple simultaneous penetrations, it will not have time

to take advantage of one opposing formation's vulnerability before being bypassed by another. The deeper (rearward) a mobile defense is executed, the

more catastrophic the results if its drive

for decisive engagement fails.

At first impression the delay tactic,

with its heavy emphasis on maneuver,

would appear to offer considerable depth

to the defense. But in practice, a screening force which actually attempts to

slow an attacker (rather than merely reconnoiter his approach) can only engage

in several skirmishes in rapid succession

over a shallow area before it either becomes decisively engaged (without benefit of terrain preparation, secure flanks,

and a cohesive reserve) or lost in a fog of

disorganized retreat.

A tactic with recurring popularity developed by European neutrals with nigged geography and territorial armies is

the ana or hedgehog defense, which uses

terrain choke points and towns which sit

astride the axit of advance as bastions to

protect local militia from being swept

away in an armored avalanche. As a

pure form of defense this tactic has little

to recommend it—ether than peacetime

cost savings—since defeat in detail it inevitable. But when employed at a supplement to a forward strategy, a rapidly

mobilizable area of defense can make the

sting of the screening forces more virulent, can secure the rear area of prepared

defense positions and provide a series of

stabilizing pivots around which a mobile

defense can maneuver with elan Tc

date, however, no NATO army has

adopted the area defense, even for tern

tonal forces, but it continues to be ■■

tactic generating considerable discussioi

in Europe.

In the mid-1970s, after considerable

innovation by the US Army's Trainir.£

Sequencing of

Active Defense

Covering Force

;

. ^ -4- L ) <

^

) <—

0 H ) -4

D

\

? ^I

Dciense In occtor

Counterattack

143 Armad Foreai JOURNAL Mamationai/Ma,

^^^^Iv^v^^vCv v^-.-TfJT/« ^^^^<^^:^^^;:^v^^^^yj^^4^;^M^
nificant antitank barrier, most of the terrain it broken by Urge stretches or forest

(the hilly woods of central and southern

Germany) or soft soil (the marshes of

northern Germany). While the density

of roadi, from Autobahn to logging trail,

makes every kilometer accessible to

tracked vehicles, this terrain is nevertheless not particularly conducive to a highspeed advance by massed armor. From

the perspective of trafTicability, as opposed to topography, the infamous

North German Plain is not an area like

the Russian Steppe where entire armored divisions, rot to mention tank

The originality of the Active

Defense Is not In the

Invention of radically new

methods of fighting but,

rather, In the way that It

borrows concepts from

established tactics,

combines them in a unique

sequence and applies them

at a lower level of

organizational

Implementation.

armies, can meander at will. Rather, an

attacker must repeatedly subdivide units

on multiple axes and proceed through

constricted passages—often in march

formation. Likewise, one looks in vain

for the Fulda gap, which in reality is

merely a series of narrow mini-gaps with

relatively higher viscosity than tome

other lectors of the front. Scattered

across the breadth of Germany are large

built-up urban areas which, while they

permit high offensive tramcability, also

give the defender unusually favorable

points of observation, fields of fire, and

protective cover. Even where open terrain is prevalent, it is dotted by a town,

village, or hamlet an average of once

every six kilometers.

For the defender, this terrain is best

auited to the antitank ambush, where,

from under cover and from hull defilade,

the lead elements of an armored column

are suddenly engaged from multiple directions. By the time the attacker reeovera from the surprise and prepares for an

organized assault with heavy supprsssive

artillery support, the defender has covertly withdrawn to the next ambush

lite several kilometers down the corridor—leaving behind a favorable 1:6 loss

exchange rate. This tactic, repeated over

and over, can slow the most aggressive

armored advance to a crawl and drain

the attacker of his quantitative superiority.

Nevertheless, the very aspects of West

German terrain which can permit a de34

fender to fight outnumbered and win,

can also work against him. The absence

of "big" gops means that the defender

cannot concentrate his forces in a few

high-threat areas, but roust cover all the

routes of potential ingress. The terrain is

only of advantage to the defender if he

occupies it; otherwise it permits the attacker to maneuver large numbers of

imall formatiom very deeply, very

quickly. The high interConnectivity of

these many corridors means that the defender cannot merely focus on east/west

routes—lest an attacker veer into the

flank or rear of an unsuspecting adjacent

defender. To realize the full ad* antage of

these terrain compartments require* the

defender to be in position long enough to

undertake considerable preparation (demolition and emplacement of obstacles

to channelize movement; mining of

high-speed approaches; construc'.ion of

alternate hull defilade firing positions for

vehicles; entrenchment, overheid cover,

and barbed wire for infantry).

While defended forests and urban areas

m»ke rapid mechanized assau!: difficult

for the offensive, they are particularly

vulnerable to traditional dismounted infantry infiltration. To truly hold this

terrain for any length of time requires a

high concentration of defenders—indeed

the larger urban areas are manpower

"black holes" capable of consuming multiple divisions.

The most significant aspect of Wes:

German terrain is where the poor trafficability areas are located with respect to

operational depth. Within the 150 kilometer band discussed earlier, the mos:

defensible terrain for virtually the entire

breadth ofthe front is near the inter-blo:

border. Behind the first SO kilometers,

the terrain tends to open up—corridors

become wider, with a higher degree of

intersection, and north/south movement

is easier—none of which facilitates a stable defense. Beyond 100 kilometers there

are large areas of concentrated urbanization and forest, but their isolation ana

sheer size do not provide the interlocks,j

defensibility available near the border

Density: The Ratio of Force to

Space

One of the major difficulties of se'-. .-

ing where to defend is correlating the

position to the quantity of forces needt J

to hold it successfully. For over a ger.er

Increased Density from

Defending Forward

Fonvard Defen??

• L*H Ihm 40 km

from Dordcr

• fronligi • 240 km

• Dcfcniibl« Terrain

• Stibtt Inlcrlocklnj

Dt rente

• Allick Cenlilned

• Ocmlly and Rcttrvet 0 50

1 . i I i

km

Rearward Defense

• 120 km from

Border

• Fronliie - 360 km

• Opea Ttmln

• Leu of Key

Clllei and Major

LOCi

• Offenilve

Momtnlum

• Reduced Dcniliy

or No Reurvei

Source: Charlci T. Kampi. Jr., Ho/ Cap; Tht Nunbtrg Pinctr. SPI. NY, 197|.

144 Armad Foreot JOURNAL lniarnaiiona:/Ma

'J.'-J.V.*r^^^^
RULES FOR FORMULATION OF CONCEPT OF OPERATION

1. A number of rules converge on the formulation of the concept

of operation. Judgments made about terrain and area characterstics, for example, should be checked against the concept

of operation (as well as with the evaluation of enemy and

friendly COAs). In many respects, the tactical planning

process at the Coprs level is iterative, building upon successive steps in a logical and consistent manner.

2. If judgments about terrain/area characterisitics conflict

with the concept of operation then the planner should be

quizzed about his/her COO.

3. If judgments about combat capabilities conflict with the

COO (by, for example, massing troops at the least likely

location) then the planner should be quizzed about his/her

rationale.

4. The concept of operations must be consistent with the

evaluations of likely adversary COAs and frienily responses.

If the planner distributes forces that are inconsistent with

the COAs then the system should constrain the planner or

else require the planner to rethink the evaluations of

the most likely/optimal adversary/friendly COAs.

145

^^:^:

-•:•:,•>:--->.: ^v^

.V-KV^ 'L**