Sec. 8 Symbolic P.easoninp Approaches

Appendix B

problem thee lie ahead (lQl1. They identify several serious deficiencfes of

current systems-  For example. termination crrterfa are still poorly understood.
Although INTEEIFIST can diagnose  sinultaneous diseases, it also pursues all
ebbnormal f indir.gs  to completfoc,  even though a  clinician often ignores minor
unexplained abnormali ties  if the rest  of a patient's  clinical status is well

understood.  In addition, although some of these  programs now cleverly mimic
some of the reasoning styles observed in ez.z.erts [143,[48], it is less clear how
to keep the systems from abandoning one hypothesis and turning to another one as

900~ as  new information suggests another possibility. Programs  that operate
this way appear to digress from one topic to another - a characteristic that
decidedly alienates a user regardless of the valic!itp of the final diagnosis or

advice.

9    Conclusicns

   This review has  shown that there sre  two recurring issues to  confront in
considering the field of computer-based clinical decision caking:
(I) Bow can we design systems that reach  better, more reliable decisions in 2
  broad range of applications, and

(2) Bow can we more effectively encourage the use of suth systems by physicians
  or other intended users?

We shall summarize by reviewing these points separately.

    Performance Issues
   Central to assuring a program's  adequate performsace is a matching  of the

nos t  appropriate technique with the problem domain.   I!e have seen that the

structured logic  of clinical algorithms can be  effectively applied  to triage

functions and  other primary care problems,  but they would be less naturally
matched with complex tasks such  as the diagnosis and management of  acute renal

failure.  Good statistical data may support an effective Bayesien program in
setrings where diagnostfc categories  are smll in number,  non-overlapping, and
well-defined,  but the lack of  higher level docain knowledge limits the
effectiveness of the Rayesian  approach in more  complex patient  management or
diagnostic environments.  A mathematical approach may support decision making in
certain veil-described fields in wb%Ch  observations are  typically quantified,
and related by functional. expressions.  These examples, and  others, demonstrate
the the need for thoughtful. consideration of the technique most  appropriate for
managing a clinical problem.   In general the sirplest effective methodology Js


Sec.  9 Conclusions

to be preferred, but acceptability  issues must also be considered  as discussed

belcv.

   It is also always appropriate to ask whether computer-based approaches are

needed at  all for a given decision making task. The clinical algorithm
developers,  for example, have almost uniformly discarded the machine, and
Schwartz et al. pointed   out  that a  useful decision analysis can often be
accomplished la a qualitative manner using paper and pencil [87].

   Finally,  it is important to consider the extent to which a program's
"understanding" of its task  domain will heighten its performance, particularly
in settings where knowledge of  the field tends to be highly judgmental and
poorly quantified.  We use the term "understanding" here to refer to  the degree
of judgmental or structural knowledge (as opposed to data) that is   contained in

the program.  Analyses of human clinical decfsion making [141, [4g? suggest that
as decisions move from simple to complex, a physician's reasoning  style becomes
less algorithmic and more heuristic, with qualitative judgmental  knowledge and
the conditions for invoking it coming increasingly into play.  It is likely that

medical  computing  researchers   will  similarly have to   become  "!;nowledge
engineers" ill   the sense that  they will look for effective ways to natcb the
knowledge  structures that  they use  to the  cOmplexity of  the tasks  they are
undertaking.

   Accentability ISSueS
        .
   A recurring  observation as  one reviews  the literature  of computer-based

medical decision  making is  that  essentially none of  the  systems  has been

effectively  utilized  outside of a  research  environment,   even   when
                                                                    --  i_cs
performance has been  shown to be  excellent!
             --P-b          This suggests that it may be an

error to   concentrate our  research effort primarily on improving  the decision

making performance of  computers  when there is evidently much more required
before these systems will have clinical impact.  It iS tempting to conclude that

the biases  of medica!. personnel  against computers are  so strong  that systems
will inevitably be rejected, regardless of performance,  and in fact  there are

some data to support this view (991.  However, we are beginning to  see examples

of  applications in  which  initizl  resistance to  automated  techniques has

gradually been  overcome through the  incorporation of adequate  syster! benefits

[113].
   Perhaps one of the most revealing lessons on this subject is an observation


Sec. 9 Conclusions Appendix t)

regarding the system  of ?fesel et al.  that tre described earlier  (641.  Despite
documented physician resistance to  clinical algorithms in other  settings [?&I,

the physicians in  Yesel's study  eccepted the guidance of protocols for the
management of chemotherapy in their cancer patients-  It is likely that the key
to acceptance in this instance is the fact that these physicians  had previously

had no  choice but  to refer  their patients  with cancer  to the  tertiary care

center  in Birmingham where all complex chemotherapy w2s  administered. The

introduction of the protocols permitted these physicians to undertake tasks that
thev had previouslv been unable to do, and it simultaneously allowed maintenance
--             ----

of close doctor-  patient relationships and helped the patients avoid frequent
long trips to the center.  The motiv2tion for the physician to use the system is

clear in  this case-  It is reminiscent of Rosati's assertion  that physicians

will fir st welcome ccmputer decision aids when they become aware that colleagues
who are using the machine have 2 clear advantage in their practice [811.

    A  heightened awareness of "human engineering"   issues   among  medical

computing researchers  is also apt  to help improve  acceptance of  computers by
physicians.   Fox has  recently reviewed :tis field  in..detail [ ie].   The issues

rtnge  from the  mechanics  of interaction at  a computer   terminal  to program
charscteristics designed to make the  system appeer as a tool for  the physician
rather then a dogmatic advice-giving machine.
   Adequate  attention  must also  be given to  the   severe  time constraints

perceived by physicians.  Ideally they would like programs to take no  more t'ne
                                                                           4.

than they currently spend when  accomplishing the same task on their own.   Time

and schedule pressures are similarly likely to explain the greater resistance to
automation among interns and residents than among medical students or przcticing
physicians in Startsman's study [99].
  Finally it must be noted  that acceptability issues should generally be

considered from  the outset in  2 system's design because they nay dictate the

choice of  methodology as  much 2s  the tzsk domain itself does. The role of

formal knoeledge structures to facilitate expl2nation capabilities, for example,
fmy argue in favor of  using symbolic re2soning techniques even when  a somewhat

less complex methodology might have been adequate for the decision task.

    In  summary,   the  trend towards  increased   use  of knowledge engineering
tech.niques for clinical  decision programs has been in response to desires for

both improved performance and  improved acceptance of such systems.   As greater


Sec.  0 Conclusions

evner:cnce 1s  gained v!ith  these  techniques   and  they become better known
throughout  the medical  coo~uting  ccmmunity, it  is likely thet   we  will see

increasingly powerful  unions between symbolic reasoning and   the alternate

nethodolcgies we have dFscusaed.  Cne lesson to be drawn lies in the recognition
that there is basic computer  science research to be done in  medical computing,

and  that  the field is more  than the  application of  established computing
techniques in medical domains.

Ackncwledsments

Fe wish to thank R. Blum, L. Fagan, J. King, J. Yunz, 6. Sax, and G. Wiederhold.
for their thoughtful advice in reviewing earlier drafts of this paper.

179


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Schwartz,   W.B.,   Gorry ,   G.A.,  Kassif;er, J.P.,    and
'$a~l;;ion analysis and clinical judpent.   her. J.     Es~ff~~,/,$~
                                  yed 55, pp.
                                                -- -
  _ .

Scott,    >..c.    Clancev
"Exolaration laoabilitie; of w.,   Davis, R.,   and
                         knowle+oe-besed
                   lS79.    Shortliffe,,l  E.H.
J. Cotzputational Lineuistics, >!ccroficKe 62,     reduction  system.   Pmer.

Sheiner, L.R.,  Halkin, E., Peck, C., et
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Sherman, K., Xeiffen, E., and Komoroff, A.L.  "Ambulatory care systems." In
Problee-Directed and yedical Information Svstemso$:.F.
or .: Intercontznental lVec!lcal i%ooK Lorporatlon, f.     Driegs, ed.), New
                                            9 PP.  143-li!.

Sbimur2, X.
and sune' .I'
pp. 125-h.

Shortliffe,
    . .

"Learnin
Proc.   P
   Int,. procedures in pattern classifiers - introducrioa
       Joint Conf. on Pattern Recognition, Kyoto, 1978,
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E.H:, Axline, S.G., Buchanan, B-G., and- Cohen, S.N. "Design
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conslaer2tlons   zor
therapeutics."  Proc. a &"~::"   to provide   consultations   in clir.?cal
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Diego, Calif., F?%?Xruarym4- -          Cv~~osiu~,  311-310, Sen

Chortlifqe  E.E. and Davis R.
Gf knowie?ge-based exper;    "Some consideretfons for the iaplemental:j.on.
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December 1975.                       XeWsletter, No. 55,  9-12,

Shortliffe, E-E., and  Buchanan, E.G.  "A node1 of inexact reasoning in
medicine." >?ath. Biosci. 23, pp. 251-379 (1975).

Shortliffe, E.P.  Copauter-??ased
ElsevierfSorth Polland, icfib.    ?!edical Consultations: ?!YCTy,  New York:


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      e

88.  Sridharan,  V.S.   Guest  editorial.  Artificial Ictellizence
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O?.  Startsmen, T-S.,  and   Robinson, R.F.   "The   attitudes      medical
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lo@. Stead, W.V., Brane, R-G., Hammond U.E., et al.  "A computerized obstetric
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101. Szolovits, P. and Pauker, S.G.
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102. Taylor, T.R.
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187


Appendix C

TUE ART OF ARTIFICIAL INTELLIGEYCE:

X. Themes and caee studies of knowledge eneineering

Edward A. Feigcnbaum

Department of Computer Science,
   Stanford University,
Scanford, California, 94305.

Abstract

   The knowledge engineer practices the art of
bringing the principles  and tools of AL research
CO   bear on   difficult   applications problems
requiring experts'  knowledge for  their solution.
The technical issues of acquiring this knowledge,
representing it.  and using  it appropriately to
construct  and  explain   lines-of-reasoning, are
important problems in the design of knovledge-
based systems. Various systems that have achieved
expert level performance fn scientific and medical
inference   illuminate   the   art   of knowledge
engineering  and  its parent  science, Artificial
Intelligence.

INTRODUCTION: AN EXlUlPLE

    This is the first  of a pair of papers that
vi11   exnnlne   emerging   themes   of knovledge
engineering,  illustrate  them with case studies
dravn from  the vork of  the Stanford Heuristic
Programming Project. and discuss general issues of
knovledge engineering art and practice.

    Ler me begin  vith an example  nev  to our
workbench: a system  called PUFF. the early fruit
of a collaboration betvean our project and a group
dC  the Pacific Medical Center  WC)   in San
Francisco.

   A physician refers a patient   to PnC's
pulmonary function testing lab for diagnosis of
posstble pulmonary function disorder.  For one of
the tests, the  pacienr inhales and exbalcs a fev
t imea    in a    tube    connected     to an
instrument/computer combination.   The instrument
acquires data on flow rates and volumea, the so-
called flov-volume loop of the patient's lungs and
airvays. The coapucer maasures cettaln parameters
Of   the   CUNC   and   presents   them to  the
diagnostician   (physician or    WFF)    for
interpretation. The diagnosis is made along these
1 ines :  normal or diseased; restricted lung disease
or obstructive airvays disease or a combination of
both; rhe severity; the likely disease type(s)
(e.g.  emphysema,  bronchitis. etc.); and ocher
factors important for diagnosis.

   PUFF is given not only the measured data but
also certain items of information from the pattent
record, e.g. sex.  age.  number  of pack-years of
cigarette smoking. The task of the PUFF system is
to infer a diagnosis  and print it out in English
in the norma   medical  summary tform of  the
interpretation   expected by    the   referri-rg
physician.

   Everything   PUFF   knovs   about  pulmonary
function diagnosis is contained in (currently) 55
rules of  the IF...THEN... form. No textbook of
medicine  current 1 y  records  these   rules.  They
constitute   ehe   partly-pub] ic,   partly-private
knovledge of `* an expert pulmonary physiologist at
PMC. and vere extracted  and polished by project
engineers vorking intensively vitb the expert over
a period  of time.  Here is an example of a PUFF
rule (the unexplained acronym refer to vorfous
data measurements):

-------------------------------------------

RULE 31

SF:

1) The severity of obstructive airways
disease of the patient is greater than or
equal to mild, and
2) The degree of diffusion defect of the
patient is greater than or equal to mild.
and
3) The tlc(body box)observed/predicted of
the patient is greater than or equal to 110
and

4) The observed-predlctcd difference in
rv/tlc of the patient is greater than or
equal to 10

THEN:

1) There is strongly suggestive evidence
(.9) that the subtype of obstructive airways
disease is emphysema. and
21 It is definite (1.0) that "CAD.
Diffusion Defect. elevated TLC, and elevaLed
RV together indicate emphysema."  is one of
the findings.

-----------------------------------------------

188


Appendix C

   Onr hundred cases. carefully chosen  co span
thr  Klriccy  of disease scotes oith sufflcienc
rxrnplary tnformatton for each, were used to
exccacc the 55 rules. As the knowledge emerged, it
was represented in rule form. added co  the system
anti  rested by running additional cases.   The
expert   was   sometimes   surprised,   sometimes
frustrated, by    the   occasional    gaps   and
inconsistencies in   the   knowledge.   and  the
incorrect diagnoses chat were logical consequences
of the extstlng  rule set.  The interplay between
knowledge engineer  and expert gradually expanded
rhe set of rules to remove most of these problems.

    As cumulation of  techniques in the art
demands and allows. a new tool was not invented
when m old one vuuld do. The  knowledge engineers
puIIed out of their toolkit a version of the M'YCIN
system (to be discussed later). with the rules
about infectious diseases removed. and used  it as
the inference engine for the PUFF diagnoses. Thus
PUFF, like HYCIH. fs a relatively simple backuard-
chaining inference system.  It seeks a valid line-
of-reasanfng based on its rules and rooted  in the
tnstrumenc and patient data. With a little more
work at fitting some existing tools together, PUFF
will be able co explain this line-of-reasoning.
juac as NYCIN does.

    As it is,  PUFF only prints out  the final
tncerprecation.  of which the following is an
example:

PAT I ENT DATA :
The degree of dyspnea: HODERATELY-SEVERE
The severity of coughing: MILD
Sputum production MODERATELY-SEVERE
ihe number of pack-years of smoking: 4
referral diagnosis:  BRONCHITIS
KVC/IVC-predicted: 80
RVJRV-predicccd:   191
FVC/FVC-predicted: 87
TLC(body box)observed/predicted:   127
Predicted FEVl/FVC: 83
TLC(DLCO)observed/predicted: 83
FEVllFVC ratio: 50
RV/TLC Observed-Predicted: 21
HHF/hHF-predicted: 19
the DLCOIDLCO-predicted: 48
The slope (FSO-obs-F25-obs)/FVC-obs: 19

DECREE OF OBSfRUCTIVE AIRWAYS DISUSE:

om degree by SLOPE: (~~DE~ATELY-SEVERE
700)
OAD degree by MMF:   (S&WE 900)
OAD degree by FEV1:  (MODERATELY-SEVERE
700 1

Ft!lhL OAD DECREE:    (MODERATELY-SEVERE
910) (SEVERE 900)
so conflfcc.  Final degree:
(YODEPATELY-SEVERE 910)

Obstruction fs indicacrd by curvature of
the flow-volume loop.
Forced Vital Capacity is normal and peak
flow rates are reduced. suggesting
airway obstruction.
Flow race from 25-75 of expired volume is
reduced, indfcating severe airway
obacruccion.

MD. Diffusion Defect, elevated TLC, and
elevated RV together indicate emphysema.
OAD. Dfffusion Defect. and elevated RV
indicate emphysema.
Change in expired flow rates following
bronchodllation shows chat there is
reversibility of airway obstruction.
The presence of a productive cough is an
indication that the MD is of the
bronchitic type.
Elevated lung volumes indicate
overinflation.

Air trapping is indicated by the elevated
difference between observed and predicted
RV/TLC ratios.

Improvement in airway resistance indicates
some reversibility of airvay
Airway obstruction'ls consistent virh the
patient's smoking history.
The airway obstruction accounts for the
patient's dyspnea.
Although bronchodilators were not
useful fn this one case, prolonged use may
prove to be beneftcial to the patient.
The reduced diffusion capacfcy indicates
atrvay obstruction of the mixed
bronchitic and emphysematous types.
Low diffusing capacity indicates loss of
alveolar capillary surface.
Obstructive Airvays Disease of mixed types

    150 cases  not studied during  the knowledge
acquisition process were used for a test and
validation of  the  ruIe set.  PUFF inferred a
diagnosis for each. PUFF-produced and expert-
produced   interpretations    were    coded   for
sracistical  analysis  to discover  the degree of
agreement. Over various types of disease states,
and for two conditions of match between  human and
computer dfagnoses ("same degree of  severity" and
"vfthin one degree of severity"), agreement ranged
between approximately 902 and IOOI.

   The PUFF story fs just beginning and 0111 be
cold perhaps at  the next  IJCAI.  The surprising
punchline to my synopsis is that the current state
of the PUFF system as described above was achieved
in less than 50 hours of interaction with the
expert and less than 10 -n-weeks of effort by the
knowledge engineers.  We have learned much  in the


Appendix C

p.lac decade of the art of engineering knouledgc-
based incelligcnt agenta!

   In the raataindct of this essay, I vould like
to discuss the route that one research group, the
Stanford Heuristic Programming Project, hns taken,
illustrating progress vith case   o tudiea,  and
discusming tbemea of the uork.

2 ARTIFICIAL INTELLICXNCE b KNOULEDC& EXINEEIIINC~

   The dichotomy that vsa used to classify rhe
collected      vwr=      la     the     volume
Computers and Thought still characterizes veil the
motlvetlons  end   research efforts of   the AI
community. First, there are some vho work toward
the construction of intelligent artifacts, or seek
to  uucover principles, methods. end techniques
useful in  such construction.  Second,  there are
chose who view artificieI intelligence ea  (to use
Newell's phrsaa) "theoretical psychology." sacking
explicit and  valid informscion prOCSSSing models
of human thought.

   For purposes of this essay. I wish to focus
on the motivations of the first group,  these days
by far  the larger of  the two.   I label these
motivations "the lots: ligent agent  vievpoint" end
here is my understandlug of thet vlevpolot:

   "The potential wea of computers by
people  to accomplish rash can be `one-
dlmenaion.alised'    into     a    apec trum
representing  the nature of  instruction
that must ba given the computer to  do its
job.  Call it the URAL-TO-HOW spectrum.
At one extreme  of the spectrum.  the user
supplies his lntelllgmce to  instruct the
-china vith precision axacrly XOY to do
hie Job,  step-by-step.   Progress in
Computer Science ceo be seen ss atepa svsy
from the extreme `HOW point   on the
spectrum: the familiar peooply ?? o aaembly
languagea (      subroutine    librarice,
compilers, extensible languagea,  etc. At
the other extreme of the spectrum la the
user vlth his real problem (WUT he wishes
the computer. ee him instrument. to do for
him).  He aspires to colmmfcetc UllAT he
veota   dooe   In a  langusge   that ia
comfortable to him (parhepa English); vie
cmicatfon modes that ere coovenleot
for bin (lncludlng perheps. aperch or
pieturen);  with some  gemerallty, some
vagueness.   imprecision.    even   error ;`
without having  to lay out lo det'ail all
necessary    subgoals     for   adequate
performance -  with reasonable ssaurence
that he is addressing en InteIIlgent agent
that La using knowledge of hia vorld to
understand his intent. to fill 10 his
vagueneaa,    to    sake   spaciflc   his
abstractions,  to correct his errors, LO
discover   appropriate   suhgoela.   and

ultimately to  translate WHAT ha really
wants done into processing steps that
define HOU it  shall be done by .a real
computer.  The research activity aired at
cresting  computer programs that act iia
"intelllgeot egentsv nesr the MlAT end of
the UDAT-To-HOW spectrum COO be viewed ss
the long-range goal of   AI research."
(Felgenbaua, 1974)

   Our young acieoce 1s still more art than
science. Art: "the principlea or methods governing
soy craft  or branch of learning."  Art: "skilled
uorkmsnship,  execution. or  agency."  These the
dictionary teaches us. Knuth tells us that the
endesvor  of computer programming is an arc. in
Just   these   vays. The   art  of constructing
lntelllgent agents is  both pert of   and an
o xtenalon of the programming art. It la the art of
building complex computer programs that represent
and reason with knowledge of the world.   Our art
therefore lives in symbiosis with   the other
uorldly arts,  whose practitioners -- axparts of
their srt - hold   the knowledge ve   need to
construct fntelllgent  agents. In moat "crafts or
branches of lesroing" what us cell "expertise" is
the essence  ot the art. And for the domains of
knowledge that  w touch with our art. it is the
"rulea of  expertise" or  the rules of  "good
Judgment" of the expert practitioners of that
domaio chat we seek to transfer to our programs.

2.1 Lessons of the Past

    TWO   insights   from   previous   vork  are
pertinent to this essay.

   The first concerna the quest for generality
and power of the inference engine used fn the
perfornsnce of intelligent acte (what Nlnsky and
Papert  [see Goldstein and Paperr,  19771 have
labeled "the paver strategy"). UC must hypothesize
from our  experience  to date that  the problem
aolvviog paver exhibited In an  lntelllgent agent's
perfomnce is primarily a consequence of the
specialist's koovledge employed by the agent. and
only very secondarily related to  the generality
end power  of the inference aethod employed. Our
agents oust ba bowledge-rich, even if they are
methods-poor. In 1970. reporting the firet msfor
sunnary-of-results of  the DWDBAL program (to be
dlacuaaad lster). we addressed thia i.aa~e as
folloua :

   "...general problereolvera  are too
weak to be used as the bash for buiIding
high-perfornsnca systems. The behavior of
the best general problem-solvers  ve know,
human problem-solvers, Is observed to be
nak and shallow,  except in the  area* in
which the  human problan-solver   is a
specialist.  And it la observed  that the
transfer of  expertise becveen specialty

190


Appendix C

areas is  slight.   A chess   master 1s
unlikely to be an expert algebraist  or P"
experr  mass  spectrum  analyst,  etc. In
this view.  the expert Is  the specialist,
with a specialist's knowledge of  his sees
rnd      specialfst's
heurist:c5." (Feigenbauo.  methods    and
                       Buchanan and
Lederberg. 1971. p. 187)

   Subsequent evidence from our laboratory and
aI others has only confirmed this bclfef.

   AI researchers have dramatically shffted
their vfew on generality ahd  power in the psar
decade. la 1967, the canonical question about the
PENDW   program was:   "It sounds   like good
chemistry. but what does it have to do with AI?"
In 1977, Goldacain and Paperr write of a paradigm
shtft in AI:

   "Today  there has been a shift in
paradigm.   The fundamental   problem of
understanding  Intelligence is   OOC  the
identification of a    fcv  powerful
techniques, but rather the question of bov
to represent large amounts of knouledge in
a fashion that permit5 their effective use
and  interaction." (Goldstein and Papert,
19771

    The second  insight from past  work concerns
the nature of the knowledge that an  expert brings
to  the performance of a task.   Experience has
shown us that this knowledge is  largely heuristic
kmnrledge. experiential. uncertain - mstly "good
guesses" and "good practice," in lieu of facts and
rtg0r.  Experience has also taught us that much of
this knowledge fs private to the export. not
because he is unvillLng CO share publicly how he
performs. but because he is unable. He know more
tbsn he is avare of knoving. Imy else is the
Pha. or    the   Internship a   gufld-like
apprenticeship co  a presumed "master of the
craft?" What the masters really knov is not
written in the textbooks of the masters.]  But we
have learned also that this private knowledge can
be uncovered by the careful, painstaking analysis
of a second  p=rty v  or sometimes by  the expert
himself,  operating in the context of a large
number of highly specific performance problems.
Finally, we have learned that expertise is multi-
faceted, that the expert brings to bear  many and
varied  sources of knowledge in performance. The
approach to  capturing his expertise  must proceed
on many fronts 5imultaneousiy.

2.2 The Knowledge Eoqineer

   The knowledge engineer is chat second party
jurt discussed.   [An historical note about the
tern. In the mid-60s. John McCarthy, for reasons
obvious from   his work. had   been descrfbing
Artificial Intelligence as "Applied Epistemology."
`&en I first described the DENDRAL program to
Donald Hichle in 1968, he remarked that it was
"epistemological  engineering," a   clever but
ponderous aad unpronounceable turn-of-phrase that
I slmplffled into  "knovledge engineering."] She
(in deference  to my favorite knowledge engineer)
works intensively with an  expert to  acquire
domain-specific knovlcdgc and organize It for use
by a program.  Simultaneously she is matching the
tools of the AI workbench to the task at hand --
program  organi2atlon8,   methods of   symbolic
inference,  techniques for the   structuring of
symbolic information,  and the like. If the tooi
fits, or nearly fits, she uses it.   If not,
necessity mothers AI invention, and a oew tool
gets created. She builds the early versions of the
intelligent agent,  guided always by her intent
chat the program eventually achieve  expert Leveis
of performance in the task. She   refines or
reconceptualiaes  the system 5s  the tncreasing
amount of acquired ,\novledge eau5es the AI tool to
"break" or slow down intolerably. She 0180 raffnes
the human interface to the intelligent  agent with
several   aims: co   make   the   system  appear
"comfortable" to the human user in his linguistfc
transactions  vith it; to  make  the system's
inference processes understandable to the user;
and  to make  the assistance controllable by the
user vhen,  in the context of a real problem, he
has an insight that previously was not elicited
and therefore not incorporated.

   In the next section, I vish to explore (in
summary form) some case studies of  the knovledge
engineer'5 art.

3 CASES FROH THE KNOWLEDGE ENGINEER'S WORKSHOP

    I will draw material for this section from
the work of my group at Stanford.   Much exciting
work   la knovledge  engineering is   gofng on
elsewhere.   Since my intent is not to survey
literature but to illustrate themes, at the risk
of appearing parochial I have used as ca5e studies
the wrk I knov bert.

   w  collaborators (Professors  Lederllerg and
Buchanan) and I began a series of projects,
initially the development of the DENDRAL program,
in 1965.  We had dual motives: first, TV study
scientific   problem   solving    and   discovery,
particularly  the processes  scientists do  use or
should use in fnferring hypotheses and theories
from empirical evidence; and second, to conduct
this study in  such a  way that  our experimental
programs would  one day be of use to working,
scientists.  providing  Lntelligent  assistance on
feportant and difficult problems.  By 1970. we and


Appendix C

our co-workers had gafned enough experience that
we felt comfortable in laying out a  program of
research encompassing work on  theory formation.
knowledge   ucllFratton.   knowledge  acqutoltion,
explanation, and knowledge engineering techniques.
Although there were  some surprises along  the way
(lfke the All program). the general lines of the
research sre proceeding as o nvfsioned.

THEMES

   As a road map  to these case studies,  it is
useful to keep in mind certain major themes:

Cenerstion-and-test:    Omnipresent     tn    our
exneriments is the  "classical" neneration-and-

test framework that has been the hallmark of AI
programs for tvo   decades.   This is   not a
consequence of s doctrinatre attitude on our part
about heuristic   search,  but rather of the
usefulness and sufficiency of the concept.

Sftuation -> Action Rules:   Ue have   chosen to
represent the kno-o,ledge of experts in  this form.
Making  no doctrinaire  claims for  the universal
applicability of   this   representation, we
nonetheless point to the demonstrated  utility of
the   rule-based   representation.    From   this
representation flov  rather directly msny  of the
characteristics  of our programs:  for example.
ease of  modification of the knowledge,  case of
explanation.  The essence of our approach is that
a rule must capture s "chunk" of domain knowledge
char is meaningful,  in and of itself, to the
domain specialist.   Tbucr our  rules bear  only a
historical relationship  to the production rules
used by Newell and Simon (1972) vhich we view as
"machine-language    programming"     of     a
recognize -> act machine.

The Domain-Specific Knowledge: It plays s critical
role in organizing and constraining  sesrch. The
theme is that in the knowledge is the power. The
interesting  action srises from the knowledge
base, not the inference engine. We use knowledge
in rule form (discussed above), in the form of
inferentially-rich models based on theory, and in
the form of  tableaus of  symbolic   data and
relationships   (i.e.   f rawlike   structures).
System processes are made to confons to nstural
and  convenient  representations of  the domain-
specific knowledge.

Flexibility to aodifv the knovledKe base:  If the
so-es1 led  "grain   sire" of   the  knowledge
representation is chosen properly (i.e. small
enough to be  comprehensible but large  enough to
be mesningful to the domain epecislist), then the
rule-based approach allows great  flexibility for
adding.  removing. or  changing knowledge in the
system.

Line-of-reasoning: A csncrsl organizing principle
in  the design  of knovledge-based intelligent
agents is the naintenance of  a line-of-reasoning
that is comprehensible to the  domain specialist.

This principle is,  of course,  not  a logical
necessity, but seems to us to be an engineering
principle of major importance.

Multinle Sources of Knowledge: The forwcion and
maincensnce (support) of the line-of-reasoning
usually require the integration of many disparate
sources of knowledge.  The representational and
inferential problems in achieving s smooth and
effective integration are formidable engineering
problems.

Explanation: The ability to explain the line-of-
reasoning in a language convenient to the user is
necessary   for   application   sad   for system
development (e.g. for debugging and for extending
the knowledge base). Once again, this is an
engineering principle, but very important. Uhat
constftutcs  "an  explanation"  is not  a simple
concept,  and  considerable thought  needs  to be
given,  in each case, to the structuring of
explanations.

CASE STUDIES

   In this section I vi11 cry to illustrate
these themes with various csse studies.

3.1 DENDRAL: Infertine Chemical Structures

3.1.1 Historical Note

   &gun in 1965.  this  collaborative project
with the Stanford Hass Spectrometry Laboratory has
become one of the longest-lived continuous efforts
in the history of AI (a fact that la no spa11 way
has contributed  to irs success).    The basic
framwrk of generation-and-test and rule-based
representation has proved rugged and extendable.
For us the DENDRAL system has been a fountain of
ideas. many of which have found their way, highly
metamorphosed,  into our other   projects.   For
exsmple.  our long-standing commitment to rule-
based   represenrstioas    srose   0oC    of   our
(successful) attempt to head off the imminent
ossification of  DHNDRAL caused by   the  rapid
accumulation of new knowledge in the system around
1967.

3.1.2 Task

   To enumerate plausible structures (atom-bond
graphs) for organic molecules, given two kinds of
information: analytic instrument data from  a mass
spectrometer  and a  nuclear  magnetic  resonance
spectrometer; and user-supplied constraints on the
answers,   derived from   any  other   source of
knowledge (instrumental or contextual) available
to the user.


Appendix C

3.1.3 ReprCSCntaLions

   Chemical structures are represented as nodr
link graphs  of atoms (nodes) and bonds (links).
Constraints on search are represented as subgraphs
(acomic configurations) to be denied or preferred.
The empirical theory of mass spectronecry is
represented by a set of rules of the general form:

Sttwtfoa: Parricular atomic
      configuration
      (subgraph)

( Probability, P,
I  of occurring

V

Action:  Fragmentation of the
     particular configuration
     (breaking links)

Rules of this forn are natural and expressive to
mess speccrometrists.

3.1.6 Sketch of Hethod

   DENDRAL's inference procedure 1s a heuristic
search that takes place in three 5cages. without
feedback: plan-generate-test.

  "CenVate"  (a program called CONCEN) is a
generation process for plausible structures.  ICS
foundation is a combinatorial algortthm (vith
aathemaeically proven properties  of complecene55
and non-redundant generation) that can produce all
the topological ly legal   candidace  5trUCturss.
Constraints supplied by the user or by the "Plan"
process prune and seer the geaeracion to produce
the plausible set  (i.e.  chose satfafyiag  the
constraints) and not the enormous legal set.

    "Test"    ref fnes    the    o ??o?????*    of
plausibility. discardfng less worthy candidates
and rank-ordering the remainder for examination by
the user.  "Test" first produces a "predicted" set
of instrument data for each plausible candldete.
ustng the rules described.  It then evaluates the
wrrh of each candidate by comparing its predicted
data vtth the actual input data.   The evaluation
is based on heuristic criterfa of goodness-of-fit.
Thus,  "test" selects  the "best"  explanation5 of
the data.

   "Plan" produces direct (i.e. not chained)
tnf o ??*??   about like Ly   subrcructure   In the
molecule from patterns in the data chat are
indicative of the  presence of  the substructure.
(Patterns in ehe data trigger the left-hand-sides

of substructure  rules).  Though composed of many
atoms  whose   interconnections are   given. the
substructure  can be  manipulated as  atom-like by
"generate." Aggregating many units entering tnto a
combinatorial  process into   fewer htgher-level
units reduces the size of the combinarorial search
space. "Plan" sets up the search space so aa to be
relevent co  the input data.   "Generate is the
inferencs  cacticlan;  `Plan" is   the inference
strategist.  There is a separate "Plan" package
for each type of instrument daca, but each package
passes  substructures (subgraphs) to "Generate "
                                       .
Thus, there is 5 uniform interface between "Plan"
and  "Gearrace."  User-supplied constraints enter
this fnterface.  directly or   from user-assist
packages. tn the form of substructures.

3.1.5 Sources of Knovledse

   The various sources of knowledge used by the
DENDRAL system are:

    Valences   (legal connections   of atoms);
stable and uastable configurations of atoms; rules
for m5ss speccrometry fragmencacions; rules for
NMR shifts; expert's rules for   planning and
evaluation:      user-8uppL ied      constraints
(contextual). `*

3. X.6 Results

   DgNDRAL's structure elucidation abilftles
are.  paradoxically.  both very general and very
narrow. Ia general, DENDRAL handles ail molecules,
cyclic   and   tree-like.    In   pure   3tructure
elucidation under constraints  (without instrument
data).CONCgN is unrivaled by human performance. In
structure   elucidation   with   inacrumenc data,
DLNDRAL's  performance   rivals    expert   human
performance only for a smail number of molecular
families for which the program has been given
5pecialisc's knowledge. namely the families of
interest to our chsmfsc collaborators.   I will
spare this computer  science audience the list of
name5  of these families. Uithia these areas of
knovlsdge-intensive   specialization,   DENDRAL -s
perfomnce is usually not only much faster but
also more accurate than expert human performance.

   The statement just made summarlses thousands
of runs of DENDRAL on problems of interest  to our
experta, their colleagues, and their studencs. The
results  obtained. along  with the knowledge chat
had to be given  to DERDRAL to obtain  them, are
published in major journals of chemistry. To date,
25 papers have been published there, under a
aerie    title  "Applications of   Artificial
Intelligence for Chemical Inference:  <spaTif ic
subject>" (see references).

   The DKNDRAL system fs in everyday use by
Stanford  chemists,  their collaborators  at ocher
universities   and   collaborating or  otherwise
interested chemists in fndustry. Users outside


Appendix C

I;t:rnford   BCL'PS.s  the   syacrn   ""CT  rommerr i 3 1
compuc*r/commcinicacions  network.    Thr  prob I c-es
chry are  solving are  often dlff lcul t  and novrl .
The  Pri tfsh  povcr"mmc  is currently supporting
work at Edinburgh aimed at transferring DENDRAL to
induscrlal user cnmmuntttes in the UK.

3. I. 7 Discussion

    Representaclon   and   extensibility.   The
wpresentatlon    chosen For    the   molecules,
ronscraincs,   and   rules of   instrument data
interpretation Is sufficiently claw CO  that used
hY   chemists In   thinking    about   scructurc
elucidation  that  the knowledge base has been
extended smoothly  and easily. mostly by chemists
themselves in   recent years. Only   one  major
reprogramming  effort  took place In the last 9
years -- when a new generator was created  to deal
vlth cyclic ~cr~~c~re~.

    Representation   and   the   Integration of
multiple  sources  of knowledge.   The generally
difficult problem of fnteRratinR various sources
of knowledge has been made easy in DENDRAL by
careful  engineerinK of  the  representations of
objects, constraints, and rules.  We insisted on a
C"lll?O"   language of   compatlblIity of   the
representations  vfth  each  other  and  with  the
1"ference processes:  the language of molecular
structure expressed  as graphs.   This leads to a
stralqhtforward procedure for addlng a  new source
of  knowledge,  say.  for example,  the know1 edge
associated with a new type of instrument data. The
procedure is this:  write rulea that describe the
effect of the physical processes of the Instrument
0"  no1 ecu1 es using  the situation -> action form
with molecular graphs  on both sides:  any special
Inference process ustng these rules must pass its
results  to the generator only(!)   in the common
graph language.

   It is today vldely believed in AI that the
use of  many diverse sources of knowledge In
problem solving  and data interpretation has a
strong  effect  on quality of performance.   HOW
stronq Is. of course, domain-dependent. but the
impact of bringlnq just one additional source of
knowledge to bear on a problem can be startling.
In one difficult (but  not unusually difficult)
mass spectrum analysis problem*, the program using
its mass  spectrometry knovledge alone  would have
generated  an impossibly large set of plausible
candidates (over I.25 million! 1. Our engineering
response to this was to add another source of data
and  knowledge,  proton NHMR.   The addition on a
simple interpretive theory of this !DfR data, from
which  the proDram  could infer a  few additional
ronstraints.   reduced   the   set of  olauslble
candidates to one, the riRht structure!   This was
not  an isolated  result but  shoved up dozens of
times In subsequent analyses.

------------------
o the  analysis of an  acyclic amine  vith formula
C2OKLW.

    DENDRAL and data.  DEKDRAL's robust mode1 s
(topologlca1. chemical. instrumental) permit a
strategy of   finding  solutions  by generating
hypothetical "correct answers" and  choosing among
these  with  critical  tests.  ThlS   strategy 1s
opposite to   that of   piecing   together   the
Implications of each   data  point to   form a
hypothesis.  UC call DENDRAL's strstegy largely
model-driven,  and  the other data-drive".   The
consequence  of having  enough knowledge to do
model-driven analysis is a large reduction  in the
amount of data that must be examined since data fs
being used mostly for verification of possible
answers.   In a  typical DENDRAL mass spectrum
analysis,  usually no  more  than  about 25 data
points out of a cyptcal  total of 250 points are
processed.   This   important point   about data
reduction   and   focus-of-attention    has   been
discussed before by Gregory (1968) and by the
vision  and  speech research Rroups,  but Is not
widely understood.

    Conclusion.  DERDRAL was an early herald of
AI's shift to the knovledge-based paradigm. It
demonstrated the point of the primacy of domaln-
specific knowledge in achieving expert lavels of
performance.   Its   developmew t brought to   the
surface   important   problems of   knowlcdee
representatiofl.  acquisition. and  USC.   It shoved
that,  by and large, the AI tools of  the first
decade were sufficient to cope wfth the demands of
a complex scientific problem-solving  task,Or were
readily extended to handle unforsecn difflcultles.
It   demonstrated   that   AI's   conceptual   and
programming tools   were  capable   of producing
programs of  applications  interest,   albeit in
narrow  special ties.   Such a   demonstration of
competence and  sufficiency was Important  for the
credibility of the AI field at a critical juncture
in its history.

3.2 HETA-DENDRAL: inferring rules of mass
    snectrometry

3.2. I Historical note

   The META-DERDRAL program is a case study tn
automatic  acquisition  of domain knowledge. It
arose  out of  our DENDRAL vork for  two reasons:
first, a decision  that vlth DENDRAL we  had a
sufficiently firm foundatlon on which to pursue
our   lonR-standing interest in   processes of
scientific   theory   formation;   second,   by a
recbgnltion   that  the   acquisition   of domain
knovledRc was  the bottleneck problem In  the
building of   applications-orirnted  Intelligent
agents.

X2.2 Task

   MS'TA-DEDDRAL's job  is LO  infer  rule? of
fragmentation of molecules in a mass spectrometer
for possible later use by the DE?IT)RAI. performance


Appendix C

proqram. The inference  is co be made from acrusl
spoccra recorded from know  molecular structures.
The   oucpuc of   the  SYSLWI   is the   set of
frn~mcntation  rules discovered, summary of the
evidence tuppartlnR  each rule,  and a  summery of
contra-indicating     widener.     User-supplied
constraints cm also be input to force the form of
rules along desired lines.

3.2.3 Represencactons

   The rules arc, of course, of the same form
as used by DENDRAL char was described earlier.

3.2.4 Sketch of Vethod

    URA-DEBDRAL.   like   DEh3RAL.   uses   the
rcnrration-and-test framework. The   process is
orA.xnited fn chrre staRes: Reinterpret the data
an-'   summari*e   evidence    (lrrSuM) ;   generace
plausible candidates for rules WJLECEN); test and
refine the set of plausible rules (RUtMOD).

   INTSLM: gives wery data point in wery
spectrum an interpretation as a possible (highly
specfficl   fraReentacion. It   then  sumaarizcs
stntiscically   the  "ueiRht of   evidence"  for
fragmentations and for atomic configurationa that
cause  these fragmentations. fhue. the job of
INTSLD4 is to  translate data to  DENDRAL subgraphs
and bond-breaks, and co summarize the evidence
JccordlnRly.

    RULECEN: conducts a heuristic search of the
space of  all  rules  chat  are legal under the
DENDBAL   rule   syntax   and   the  user-supplied
constraints.  Xc searches for plausible rules, i.e.
those for which posirlve evidence exists. A search
path is pruned when there is no evidence for rules
of  the  class just qeneraccd.  The search tree
begins vi th   the (single1 most   general rule
(loosely    put,   "anything"   fragment9   from
"anything")  and proceeds level-by-level toward
more detailed specifications of  the "anything."
The heuristic stopping criterion measures whether
a rule being generated has become too specific. in
particular  vhccher tt is applicable to too few
molecules of the input set.  Slmi1arly there is a
criterion for decidinR vhecher an emerRing rule is
too Rsncral. Thus. the output of RULECEN Is a set
of  candidate rules for which there is positive
evidence.

    RULE%OD: tests the candidate rule  set using
more complex  criteria. includlnR the presence of
neRatfve evidence. It removes redundancies in the
candidat*   rule  set ;   merges  rules   that  are
supported by the same evidence:  tries further
special izacton  of candidates to  remove negative
iCVl4CnCe;  and tries further Reneralizstion chat
preserves posirivc evidence.

3.2.5 Results

    HFTA-DENDRAL produces  rule sets  chat rival
in quality  those produced by our collaborating
experts.  In some rests,  HETA-DENDRAL recreated
rule sets that ve had previously acquired from our
experts during the DENDRAL project.   In d more
stringent test  involving members of a  family of
complex  r ingcd  molecules for vhich   the mass
specrral theory had not been completely worked out
by chemists, META-DENDRAL discovered rule sets for
each subfamlly. The rules were judged by experts
to be excellent and a papcr describing them vaa
recenciy published   in a  maj or  chemical journal
(Buchanan. Smith, et al. 1976).

   In a test of the generality of the approach,
s version of the META-DENDRAL program 1s currently
being applied  to the discovery  of rules for the
analysis of nuclear mognecic resonance data,

3.3 WCIN and TEIRESIAS: Medical Diagnosis

3.3.1 Htstorfcal not;

   HYCIN orlglnated in the Ph.D. thesis of E.
Shortliffe (now Shortlfffc. M.D. as well). Ln
collaboration vith the Infectious Disease group at
the Stanford Medfcal School (Shorcliffe, 1976).
TEIRESIAS,  the Ph.D. thesis vork of  R. Davis.,
arose from issues and problems indicated by the
MYCIN project but generalized by Davis beyond chr
bounds of
1976).  medicaln~~~~n~:::ee~pl~~ea:f~ns W:vls,
        Ocher                            In
progress.

3.3.2 raJks

   The WCIN performance cask is diagnosis of
blood infections and meningitis tnfectlons and the
recommendation of drug treatment.   !iYCIN conducts
a coneultacion (in English) with  a physic ian-user
about  a patient case,   constructing lines-of-
reasoning leading  to the diagnosis  and treatment
plan.

   The TEIRESIAS knovledge acquisition task can
be described as follow:

    In rhe context of a particular consultation.
confront the expcrc vith a diagnosis vith which he
does not agree.  Lead him systematicaLly back
through  the line-of-reasoning  that  produced the
diagnosis CO tho point ac vhich he indicates the
analysis wane  awry. Incerncc with the expert co
modify offending  rules or  to acquire  new ruies.
Rerun the  consul tat ion to   test the  soIution and
gain the expert's concurrence.

195


Appendix C

3.3.3

IF

Rcpresentattons:

NYCIN's rules are of the form:

<coajuoctlve clauses> THEN <implication>

Here is an example of a HYCIN rule for blood

fnfections.

RULE 85

IF:

1) The rice of the culture is blood. and
2) The gram acain of the organism is

gramncg , and

3) The morphology of the organism ia

rod, and

4) The patient is a compromised host

THEN:

There is suggestive evidence (-6) that
the identity of the organism is
pseudomonas-aeruginose

    THIRESIAS   allow  the   representation of
HYCIN-like rules governing  the use   of ocher
ru1es.i.e.  rule-baaed  strategies.   Aa example
follows.

3.3.5 Language of Intetac tion

   The language used looks like it raight be
English but is actually the dialect "DocLor-ese"
ured by members of the tribe of healing arts
practitioners.   Reasonably   simple   I anguage
processing  methods suffice.  When ambiguities of
interpretation are encountered, they are Ted back
to the user for decisions about meaning.

3.3.4 Sketch of method

    MYCIN   employs    a    generation-and-test
procedure of a familiar sort. Tbe generation of
steps in the line-of-reasoning is  accomplished by
backward chaining of the rules. An IF-side clause
is either immediately true or false (as determined
by patient or  test data entered by  the physician
in the consultation); or is to be decided by
subgoaling. Thu..  "test" ia   interleaved with
"generation" and serves to prune out incorrect
lines-of-reasoning.

    Each  rule supplied   by an   expert has
o ssocieted with   it a  "degree  of certainty"
representing  the  expert's  confidence   in the
validity of the rule (a  number from I to LO).
HYCIB usea a particular ad-hoc but simple model of
inexact  reasoning  to cumulate  the degrees of
certainty of the rules used In an  tnference chain
(Shortliffe and Buchanan, 1975).

    It follows that  there may be a number of
"aomevhat    true"    lines-of-reasoning -- some
indicating one diagnosis, some indicating another.
All (above a threshold) are used by the system as
sources of knovledge indicating plausible lines-
of -reasoning.

   TEIRESIAS' rule acquisition process is based
on a record of tWCIN's search. Rule acquisition is
guided by a set of  rule models that dictate the
form and indicate the likely content of new rules.
Rule models are not given in advance, but are
inferred from the knowledge base of existing
rules.

IF:

1) the patient is a compromised host, and
2) there are ruler vbich mentioa in their
premise pseudomonas
3) there are rules vhich mention in their
preoiee klebsiellra

THEN:

There is suggestive evidence (.4) that the
former should be done before the latter.

3.3.6 Example diagnosis and treatment plan

   The folloving is an example of a diagnosis
reported by RYCIN to one of its users  foll,ovlng a
consultation. An example consultation vi11 not be
ahovm. but one  can be found in Shortliffe's book
(Shortllffe, 1976).

---------            m-s-

Fly therapy recommendations vi11 be designed
to treat for organisms that are either very
likely or, although less likely, would have
a significant effect on therapy selection
if they were present.  It is important to


Appendix C

cover for chr followtnR probable
infection(s) and associated organism(s):

INFECTION-l is CYSTITIS
<Item I> PSEUDOIYONAS-CEPACIA [ORCANISl#-51
<Item 2, CIIROBACTER-OIVRRSUS [ORGANISMS]
<Item 3s E.COLI [ORCANISt!-31

MFETXON-2 la UPPER-RESPIRATORY-INFZTION
<Item 4> LISTERIA [ORGANISM-21

INFECTION-3 is RACTgRDfIA
<Item 5> WTEROCCCCUS [ORGANI%!- 1

[REC-II tly preferred therapy recommendation
is as folloue:

In order to cover for Items ~1 2 3 4 5X
Give the following in combination:
I ) KANAMYCIN
Dose: 7.50 mg (7.5 =&kg) q12h IF! (or IV)
   for 28 days

Comments: Modify dose in renal failure
2) PENICILLIN
Dose: 2,500.OOO units (25000 unitm/kg)
   q4h IV for 28 days

--e-------w--

3.3.7 Results

    MYCIN ts   currently bcfng   prepared for
clinical evaluation.  In a prellmtnary evaluation.
done outside of  the clfaical setting, o  panel of
expert   judges   uas   asked to   rate MYCIN's
perfomance along a number of dimensions (e.g.
infecting   organism   ldentif ication,   organism
siRniflcance1.  In 90X of the casem submitted CO
the judges, a majority of the judgca raid thet the
program's decisions uere the-some-as or as-good-as
the decisiona they would have made.

3.3.8 Exulaaacfon

   MYCIN can expose its line of reasoning by
offering explanattons of various kinda.  `Why" and
"How" questions can be asked by the user during
the consultation.  "my"  is the user's usy of
inquiring. `Why do you vaac to know that?" "Row"
is his  uay of asking,  "Hou did you conclude
that...?" In a post-consultation, he can similarly
inquire about how the diagnostic conclusiona uere
reached, and inquire about the reasons why certain
ocher conclusions verc ruled out.  Examples of the
XYCIN-TEKRESIAS explanation capabilities follow:

IlYCXN:     Is the patient's fllness with
ORGANISM-I a hospital-acquired infection?
USER: UKY

TEIRFSIAS: (i.e. WRY is it imporcrnt to
determine whether or not the infection with
ORGANISM-l uas acquired while the patient
uaa hospitalized?)

Il.01 This ~111 aid in determining the
category of ORGANISM-I.

It has already been o acablished that

Il.11 the gram stain of ORGANISM-1 is
gtameg. and
Il.21 the morphology of ORCANISR-1 is rod.
cud

[I.31 the aerobicity of ORCANISH-I is
facultatlve

Therefore. if

11.41 the infection with ORGANISM-I was not
acquired while the patient was hospitalized

then

there is strongly suggestive evidence (.8:
that the category of ORGANISM-1 is
enterobscterirceae

Also:  there is weakly suggestive evidence
(. 1) that the identity of
ORGANISU-1 is pseudomonas-acruginosa
[RULE 371

USERR:  KOU 1.3

TEIRESIAS:   [i.e. HOU vas it established
that the aeroblcfty of ORCXNISH-I ts
facultative?I

The following uere wed:

f3.11 RULE 27 indicated there is strongly
suggestive evidence (.8) that rhe
aerobicity of ORGANISR-1 1s facultativc

13.2) RULE 27 indicated there is weakly
suggestive evidence (-2) that the
rerobicity of ORCANISX-I is anaerobic

Since this gave a cumularive CF of c.8) for
facuitaclve, and c.2) for anaerobic, i.t has
been established that the aerobicity of
ORGARISH- is faculcative.


Appendix C

USER:   WHY DIDN'T YOU CONSIDER
STREPTOCOCCUS AS A WSSIBILITY FOR
ORGANISM-1 7

TEIRESIAS:   The following rule could have
been used CO detetmiue that the fdentlfy of
ORCANISM-1 was strepcococcw: RULE 33

But claue 2 ("the morphology of the
organlao IS coccus") WAS already know
co be false for ORCANISP!-I, so the rule ves
never tried.

3.3.9 Discussion

   A fev short comments aimed nt knowledge
engineering are in order.

   English is the language moat natural snd
comfortable for out experts and users. and van
therefore  the language  chosen for interactive
consultation,    explan8tion.     and    external
representation of  the rules (the internal format
is INTERLISP).  This situation is not peculiar to
doctors; in   most  mew of  applfcat1on of
intelligent agents I believe that Eaglieh (i.e.
natural langusge) vi11 be the language  of choice.
Programming an English language proceesor aad
front-end to   such  SyBtcw  is not . sc4ry
enterprise because:

    a) the domain  is specialized. so that
possible interpretations are constralned.

   b) specialist-talk is replete with stsndard
jargon   and  stereotyped veye  of expressing
knowledge and queries - just right   for text
templates,  simple  gr-rs and  other simple
processing schemes.

   cl the ambiguity of interpretation resulting
from simpple schemes caz~ be dealt with easily by
feeding back Cntcrpretatlous for confirmation. If
this is done tith s pleasant "I dido't quite
understand you..." tone.  it is not irritating to
the user.

   English msy be exactly the wrong language
for   representation  and   interrctioo   in 80-e
dorrfns. It would be svkvard. to sey the lust. to
represent   DE24DUAL's   chemical   atructuren  and
knovledfle of MAW apectronetry in English, or to
interact about these vlth a user.

   Staple crplsnation schenee hsve been .s part
of the AI scene for a number of years and  4r'c not
hard to implement. Rcaily good models of what
o xplanstion is as  a traauction between  user and
agent,  vf th programs  to implement  these models,
vi11 be the subject (I predict) of much future
research in AI.

    Without   the   o xpLanaCion   capability, I
asert, user acceptance  of WIN would have been
nil.  and   there  would   have been  a greatly
diminished effectiveness and contribution of our
experts.

   HYCIN wss the first of our progrsos that
forced ua  to deal with uhst ve  bad always
understood: thst  experts' knovledge is uucertafn
and that our Inference  engines had to be  msde to
reason titb this uncertainty. It is leas importaot
that the inexact  reasoning  scheme be formal,
rigorous, md uniform thro it is for the scheme to
be natural to  and eaolly underataudable by the
experts and users.

   All of cheat points can be summarized by
saying that HYCIN snd its TEIRESIAS sdjuact are
exper1mmts in the design of a see-through system,
whose  represeatatioas  and processes are almost
transparently clear to  the domain  specialist.
"Almost" here is equivalent to "with a few minutes
of introductory description." The various pieces
of MTCIN - the b&ward  chaining,  the English
traiasac tious ,  the explanations.  etc.  - are each
simple fn concept  and realization. But there are
great virtues to simplicity in syetcr design; and
vieved as s  total intelligent   Agent  system.
HYCIN/TZIRESIAS'is one of the best engineered.

3.4 SU/X: signal understanding

3.4.1 Historical note

   su/x ia a  system design that vas  tested in
00  application vhose details   arc classified.
&cause ot thin.  the easuiug discussion vi11
appear  considerably less  concrete and tangible
thro the preceding  CS.C  studies.  This system
design vss  done by H.P. Nii and PC, and vss
strongly influenced by the QLU Hesrmy  II system
design.

3.4.2 Task

   SU/X'U task la the formation and continual
updatf ng , over long periods of time, of hypotheses
shout the identity,  location, and velocity of
objects in s physical apace. The output desired is
a display of the  "curreoc best hypotheses" with
full explanation of the support for each. There
are two types of input data: tha primary signal
(to be understood); and suxiliary symbolic data
(to supply context for the understanding). The
primary signals   are  spectra,   represented as
descriptions of  the spec:ral lines.   The various
spectra cover the physics1 space vith some spatial
overlap.


Appendix C

3.4.3 Represencactons

   The rules give" by the expert about objects.
their behavior.  and the interpretation  Of SfgM1
data from them   are  all  rcpreaented   in the
situation -> action    form. The   "slt"atiom3"
constitute 1,WOktng  conditions and  the "actiona"
are processes that modify the current hypotheses,
P-t  unresolved issue5,  recompute  evaluations.
etc.  The expert's knowledge of how to do analyafs
in the task is also represented  in rule form.
There strategy rules replace the normal exccutfve
program.

   The situation-hypochaeis fa repreaaoted a8 a
node-link graph.  tree-like in chat it has distinct
"levels,"   each   representing a   degree of
abstraction  (Or Za88reg~CiOll)  that is natural to
the expert in his understanding of the domaio. A
node represents an hypothesis; a link to that node
represents  support for that hypothesis (as in
HEARSAY 11. "support from above" or  "support f roa
below").  "tower" levels art  concerned with the
specif its of  the  signal data. "Higher" levels
represent symbolic abstractions.

3.4-h Sketch of method

    The   altuation-hypothesis     15    formed
incrementally. As the situation unfolds over time,
the  triggering of  rules  modifiet or discards
existing hypotheses, adds new ones, or changes
support  values.   The situation-hypothesis is a
common wrkspace ("blackboard." in HEARSAY jargon)
for all the rules.

    In general,  the incremental steps  toward a
more complece and refined o ituacioa-hypothceis can
be viewed as a sequence of local generate-and-test
activities. Some of the rules are plausible move
generators.  geoeracing  either nodes or links.
Other rules are evaluators. testing  and modifying
node descriptiona.

   In typical operation. aev data  is submit ted
for processing (say. N tint-units of neu data).
Thfs inftiaces a flurry of rule-triggerlogs and
consequently rule-actions (called "events"). Some
tvencs are direct consequences of the data; other
avents arise io a cascade-like fashion from the
triggering of rules.  huxflisry symbolic data also
cause events, usually affecting the higher levels
of  the hypothesis.  As a consequence, aupport-
fro-above for the lower level ptoceaaes is made
available;  aad  expecrrtioar of  possible lower
level events can be formed. Rventually all the
relevant  rules have their say aod the system
becomes quiescent, thereby triggering the input of
new data to cc-energize the inference activity.

   The ayatem uses the almplifying strategy of
Mincainfag  only oae  `*best" situation-hypothesis
PC  any  moment,  modifying it incrementally as
required by the  changing data.  Thls approach is
made feasible by  several characteristics  of the

dcmain.   First,  there is  the  strong conclnuity
over   time of  objects   and   their behaviors
,(specifically, they do not change radically over
time. or behave radically differently over short
periods).   Second, a  single problem (identity,
location and velocity of a ptrtlcular aat of
obj ecca) persists over  numerous data gathering
periods.  (Coapere this to speech understanding in
which each sentence is spoken just once,  and each
presents  a neu and different problem.) Finally.
the  syscen's hypothesis is  typically "IIlEJosc
right." in   part becsuse it   gets  numerous
opportunities  to refine chc solution (i.e. the
numerous data gathering periods), and la part
because the availability of many knouledga sources
tends to over-derernine the solution.  As a result
of  all of  thatt,  rhc current best hypothesis
changes only slwly vith time, and  hence keeping
only the current best is a feasible approach.

    Of latereat are the time-based events. These
rule-like expressions,  created by  certain rules,
trigger upon the paasaqs of specified amounts of
time.   may   implement   various  "wait-and-see"
strategies of  anelysis that  are  uaaful in the
domaia.

3.4.5 Results

   In the teat application. using signal data
generated by a simulation program because real
data uas not available, the program achieved
expert level6 of performance over a *pan  of test
problems.   Some problems wre difficult because
there vao  very little primary signal  to auppor t
inference. Others were difficult because  too much
signal induced a plethora of alternatives with
much ambiguity.

   A difitd SU/X design is currently being
used as  the basis for an application to the
interprctatlon of x-ray crystallographic data, the
CRYSALIS program mentioned later.

3.4.6 Discussloo

   The role of the auxtliary symbolic sources
of data is of critical importance.  They supply a
symbolic model of  the erlstlng situation that Is
used  to generate txpactationt of events  to be
observed in the data stream. This allovs  flow of
inferences from higher levels of abstraction to
lover.   Such a   process,  50 familiar   to AL
researchera.  apparently is al?ESt unrecognized
0-s   5igTlal   processing   engineers.   In the
application task, the expectation-driven analysis
is essential in controlling the combinatorial
procesaiog explosion at the lover levels,exactly
the explosion  chat forces the traditional afgM1
processing angineers to aeek out  the largest
possible number-cruncher for their vork.

   The de8lgU  of appropriate  explanations for
the user takes  an interesting twist in SU/X. The


Appendix C

situation-hypothcals unfolds piecemeal  over time,,
but rhe " appropriate" explanation for the  user is
one chat focueea on individual objects  over time.
Thus   the   appropriate   o xpIenatinn muet be
synthesized from a history ol all the events that
led up to the current hypothesis.  Contrast this
virh   the HYCIN-TEIRESIAS   reporting of rule
invocatioee in the eonetructioa of a reeeoning
chain.

   Since its knowledge beee and its auxiliary
eymbolic data give ?? o model-of-the-eituetion
that  StrO&y  coastreins interpretation of the
primary   data   Stream.   swx Is  relarivelp
unperturbad by o rrorful or missing date. These
data conditions  merely cause fluctuations in the
credibility of individuel hypotheses  and/or the
creation of the `*"aIt-end-see" events. SU/X can be
(but has not yet been) ueed to control o enaore.
Since its rulce  specify what types end  vel"ee of
evidence ere nece8eary to establish support, nnd
since it Is constantly processing a complete
hypothesis structure. It can request "critical
readings" from the sensors. In general, this
a1 lows an  efficient   use of   Umited sensor
bandwidth   and   data   acquieirion   processing
capability.

3.5 OTHER CASE STUDIES

   Space does not ellov more than just e brief
sketch of other interesting projects  that have
been completed or are in progress.

3.5.1 A?+: mathematical discovery

    AM is o   knowledge-based   system  thet
conjectures interesting concepts in elemantary
mathematics.  It is a diecoverer of interesting
theorem8 to prove, not a theorem  proving program.
It was conceivad and executed by D. Lenat for his
Ph.D.  thesis, and Is reported by him in these
proceedings ("An Overview of An").

   API's knWltdgt 18 beeicelly of CM types:
rules  thet  S"g8t`t possibly  interesting new
concepts from previously coojectured concepts; and
rulae     that    evaluetc     the    mathematical
"lntereetingne*e"  of a conjecture.  These rules
attempt to   capture   the   expertire of  the
profeeeionel   mathemeticlan at   the   teak of
mathematical diecovery.   Though LeneC ie not a
profeeeionel   mathematieien.    he vae   able
successfully to eerve  as his owe expert in the
building of this program.

    A?4 conducts  a heuristic aearch through the
space of  concepte treatable from its rules. Its
basic   frnmewrk is   generetion-end-test. The
generation is   plausible uove   gtwrecion, se
indicated by the rules for formation of new
concepts.   The   test I`   the   evaluation of
"incereatingness." Of particular note   is the
method of test-by-example that lends the flavor of

scientific hypothesis testing to the caterprlee of
mathematical discovery.

   Initialized tith concepts of elementary set
theory,  it conjectured  concepts in elerencary
aumhtr theory, euch as "add." "multiply"  (by four
dietlact   paths!),   *primes,"    the   unique
fectorlution  theorem, and o concept  o imllar to
prima` but previouely not much studied called
%exlmally divlelble numbers."

3.5.2 HOLCEN: planning experimaate in molecular

genetics

    UOLGW    a collaboration with the Stanford
Genetics  Depertmenc, is   wrk   La progress.
HOLCEN'e caek le to provide intelligent advice to
a  molecular geneticiet on  the   planning of
experimmcs involving the manipulation of DNA. The
geneticist   hae various   kinds  of laboratory
technique6  available for changing DNA material
(cute. joins,  insertions, deletione, and so on);
techniques   for  determining    the  biological
coneequencee of  the changes;  various instruments
for meeeurlng effects; various chemical methods
for inducing, facilitating. or Inhibiting changes;
end many other'toole.

   NLGEN will offer planning aeelatance in
organizing and sequencing such tooie to accomplish
an experimental goal.  In o dditioa HOIXEN will
check   ueer-provided   experiment   plans   for
fceeiblllt~; and  its knovledge baee will be a
repository for the rapidly expanding  knowledge of
this specialty, available by interrogation.

   Currant efforts to tngiMSr a knouledgc-base
management  eymtes for HOLGEN are described by
Kertin et al la a paper In these proceedings. This
aubeyetem uses and  o xteude the techniques  of the
TEIRESIAS system diecueeed earlier.

   In HOLCEN the probIeo of integration of many
divcree eourcee of knowledge is central since the
essence of the experiment planning process  is the
successful  merging of  biological,   8tnetiC,
chemical,  topological, and  inetrumcnt knovlcdge.
In MOUXN the problem of rtprtaenting processes is
also brought   into focus since  the expert's
knowledge of   txperlmmcel  strategies -- proto-
plane - wet also be represtated and put LO USC.

3.5.3 cR=ALIs: fnftrr%nR DrGtein StrUCtUre fKOm

electron density maos

   CRYSALE, too, is uork in progress. Its task
is to hypothesize the Structure of a protein from
a map of electron density that is derived  from x-
ray crystallographic data. The map is three-
dimensional. and the contour information is crude
and highly ambiguous.   Interpretation is guided
and eupporttd by auxiliary information, of which
the amino acid sequence of the  protein's backbone
is the moat important. Density map interpretation

200