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AI: THE NEW WAVEA TECHNICAL TUTORIAL FOR R&D MANAGEMENT

Frederick Hayes-Roth

ABSTRACT

The field of Artificial Intelligence has maturedconsiderably since its inception 25 years ago.Now AI has become the focus of many government,industry and academic computer science programs .As the field advanced, it was infused by severalprincipal discoveries. First, the generality ofsymbolic reasoning and problem-solving becameapparent.

Second,

researchers discovered theimportance of in-depth knowledge for buildingexpert systems. Several systems have now beendeveloped that achieve expert-levelperformance intheir domains of application. Recently, theadvent of VLSI and the commercialization of AItechniques have suggested new prospects for the

future.

1. MOTIVATION

1.1 AI HAS FINALLY ARRIVED

The field of Artificial Intelligence beganaround 1956. Early successes caused a headinessthat produced excessive expectations. Forexample, Simon predicted a world-champion chessplayer by 1980. Chess 4.9 actually has achievedan "Expert" rating, just below "Master" and haswon a state-wide open tournament in Minnesota.

A five-year speech understanding systemproject conducted at Carnegie-Mellon Universityconcluded in 1976 on-time and on-budget. Itproduced two systems that understand connectedspeech from several speakers using a 1000-wordvocabulary. This was a stunning example of theability of the AI field to crack what previouslyhad been an insoluble problem.

The National Institutes of Health have fundeda national program in applications of AI tomedicine for six years. Now several systems haveachieved expert level performance on medicaltasks. Two are in daily use, and others are underfield test. The National Library of Medicinebegan a program two years ago to create the firstlarge-scale library of knowledge stored in activeforms .

The Defense Sciences Board in 1977 surveyedall scientific disciplines to identify those withthe most significant potential for the comingdecade. They identified 10 targets, three ofwhich included: AI , distributed computing, andmicroelectronics. These three should couple forstill greater impacts.

Recently, industry and military giants havefocused on AI and some other computer scienceareas as principal targets for growth. Most ofthese have little prior expertise in AI. Theindustrial firms that have decided to build AIcenters include: Xerox, Texas Instruments,Schlumberger, Fairchild

Semiconductor,

and

Views expressed in this paper ire the author's ownand are not necessarily shared by Rand or itsresearch sponsors.

Westinghouse. Many others include AI in broadercomputer science growth plans. These firmsinclude Bell Labs, General Electric, Hughes, andGeneral Motors .

Among the military, AI support has increaseddramatically in the last few years. Support fromDARPA has returned to previous high levels. TheCOMTEC-2000 report on command and control systemsproposed the establishment of a new militaryinstitute for AI and allied software areas. TheNavy has recently established an AI center at NRLwith 20 staff positions. A few RFPs have alreadyappeared that specify AI methods should be used infusion and targeting problems. The Navy hasworked jointly with DARPA to develop the ACCATtestbed and their own secure computing network forAI applications to command and control.

1.2 THE DEMAND EXCEEDS THE SUPPLY

The demand for AI personnel vastly exceedsthe supply, and this trend will probably continueindefinitely. About 75% of AI personnel aretrained at Stanford, MIT, and Carnegie-Mellon, andthese places graduates a total of about 10 PhDs orMasters a year. Undergraduate training in AI israre, but increasing. Training currently requires1 year of education plus a minimum of 1 year ofapprenticeship. Effective AI engineers have bothsharp analytical minds and extensive experiencewith advanced AI programming languages and tools.The work is challenging.

1.3 THERE IS NO ALTERNATIVE

TO

AI

AI provides techniques for

flexible,

non-numerical problem-solving. These techniquesinclude symbolic information processing, heuristicprogramming, knowledge representation, andautomated reasoning. No other fields oralternative technologies exist with comparablecapabilities. And nearly all complicated problemsrequire most of these techniques. Many forcescombine to identify AI as the central technologyfor exploitation. Systems that reason and chooseappropriate courses of action can be faster,cheaper, more effective and viable than rigidones. To make such choices in realisticallycomplex situations, the system needs at leastrudimentary understanding of mundane phenomena.

1.4 SUCCESS IN AI

REQUIRES

INFORMED ANDEFFECTIVE MANAGEMENT

Successful application of AI requires goodpeople, good tools, a good problem, and goodmanagement. Talented AI people are in greatdemand, so they are choosy about workenvironments. These days they can command highsalaries and can exploit computing systemsrequiring capitalizations of up to $50,000 perperson. Limiting their computing environmentconstrains their productivity in a direct fashion:they can write large, complex and practical

—Copyright 1981 Frederick Hayes-Roth

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programs that exceed the capacity of conventionaltime-shared machines. Picking a good problem forAI exploitation requires experience and taste: theproblem should be non-trivial but tractable, withpromising avenues for incremental expansion. AIproject managers need to remain abreast of currentresearch activities and must have a track recordin system development. Like other earlyengineering disciplines, AI amplifies thestrengths and weaknesses of its practitioners.

2. TECHNICAL ISSUES

2.1 THE DOMAIN OF AI: PROGRAMMINGINTELLECTUAL FUNCTIONS AND BEHAVIOR

Nearly all of the commonplace skills of humanbehavior are within the scope of AI . Notableamong these are: natural language production andunderstanding, vision, commonsense reasoning,deduction, induction, problem-solving, walking,navigating, manipulating. In addition, AI hasfocused upon some specific domains where it hasbeen able to achieve expert performance: medicaldiagnosis, signal interpretation, chemicalstructure elucidation, adaptive system control,symbolic mathematics, chess, and backgammon.Recently, it has turned attention to someextremely important problems within computerscience itself, including VLSI design, automaticgeneration and verification of programs, andinstruction.

2.2 EXISTING APPLICATIONS OF AI

A very incomplete sampling of applicationsfollows. I have represented more than one-half ofthe best applications.

MEDICAL APPLICATIONS (See Freiherr 1980)

MYCIN--infectious blood disease diagnosis.This system was a landmark because of its simpleand readable formalism for knowledge:

Rule 280:

If: 1) The infection which requirestherapy is meningitis,

and 2) The type of the infection isfungal ,

and 3) Organisms not seen on thestain of the culture..

Then There is suggestive evidence (.5)that cryptococcus is notone of the organisms whichmight be causing theinfection.

These rules could explain the reasoningbehind a computer-generated recommendation, andthis proved essential for user acceptability. TheEnglish form of rules was provided for output tothe user, but not for programming the rulesthemselves .

The program conducted an English dialoguewith the user (a doctor) . It asked forinformation needed to develop its hypotheses. It

searched the space of possible diagnosesexhaustively. It surpassed the performance ofmedical experts in diagnosis.

TEIRESIAS

An extension to MYCIN that helped a user addnew rules and check his knowledge base forinconsistencies. It provided a mechanism formeta-rules: rules that governed how other rulesshould be used during the interview and diagnosisprocess.

GUIDON

An extension to MYCIN that uses the knowledgebase to help train a medical student.

EMYCIN

An extraction of the "inference engine" fromMYCIN that makes it easy to apply the same type oftechniques to other problems. Hughes, forexample, has tried to apply EMYCIN.

INTERNIST

A huge system with a knowledge base coveringmost of the diseases of internal medicine. It isin field trials in Pittsburgh currently. It hasbeen created by teams of doctors and students overthe past 5 years. It associates causes withsymptoms in extensive associational networks.'Then it reasons from symptoms to causes, and canseparate the effects of multiple simultaneousdiseases.

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PUFF/VMThe only AI systems that are in regular

medical use. PUFF is an application of rule-basedtechniques like those in MYCIN to pulmonaryfunction analysis.. In the same hospital, VM isbeing tested for automatic control of arespiratory ventilator in the critical care ward.This system too uses rules of the form, If

NATURAL SCIENCES

DENDRAL/META-DENDRAL

These programs analyze chemical instrumentdata to interpret the underlying structure.DENDRAL does this by generating all possiblechemical structures that are consistent with thedata. It has a fast, complete, and non-redundantgenerator. META-DENDRAL induces new rules forDENDRAL by inferring generalizations from patternsof experimental data. These programs are in useby commercial chemical companies.

PROSPECTOR

This program uses MYCIN-like rules to assessthe likely occurrence of geological deposits inthe presence of some observables. This workproduced a very rich set of geological knowledge.Funding recently dried up. One novel idea in thisproject was using PROSPECTOR to do photo and mapinterpretation by running the systemdiagnostically upon each pixel. To do this, the

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system's knowledge and inference scheme werecompiled for efficiency, producing a speed of morethan 1000.

KAS

This program allows a user to describerelationships for PROSPECTOR in an Englishdialogue.

MOLGEN

A large package of tools for buildingknowledge bases in molecular genetics. Provideseditors for creating and maintaining taxonomies ofgenetic concepts, experiment resources, chemicaland biological properties. A recent applicationused this knowledge base to design some elegantgenetic engineering experiments. The key to thisexperimental design, work was the use of symbolicconstraints to guide successive elimination ofalternatives and to identify feasible choicesamong alternative objects and experimental steps.Apparently, many biologists are now acquiringMOLGEN from Stanford.

SPEECH AND VISION

HEARSAY-II _ HARPYThese systems both understood spoken English

sentences drawn from 1000-word vocabularies .HEARSAY-II correctly understood 90% of thesentences, while HARPY achieved 95% accuracy.Both systems integrated numerous diverse sourcesof knowledge, drawn from signal processing,acoustics, phonetics, "lexicology," andlinguistics. HEARSAY-II employed a cooperativeproblem-solving architecture in which differentprogrammed "specialists" interacted exclusively bysharing a common blackboard on which decisionswere recorded. HARPY, on the other hand, fullysystematized the interactions among thespecialists by compiling all of their potentialdecisions into one network of successive speechsegments that could be matched to an input. Thenetwork matching employed a "beam search"technique, which was effectively a dynamicprogramming algorithm with pruning. HARPY wasreprogrammed as LOCUST, on a special PDP-11/45.HARPY ran about 10 times real-time on a machinewith .4MIPS, and thus required about 20MIPSS(nillion- instruct ions-per-speech-second) toachieve real time performance. HEARSAY-11, withits more flexible and interactive problem-solvingstyle, searched a significantly smaller number ofalternatives but ran about 5-10 times more slowly.These systems culminated a 5-year research programthat achieved or surpassed all of its objectives.

ARGOS

This system generalized HARPY's beam searchto two-dimensional photo interpretation. Itsuccessfully interpreted various Pittsburgh scenesby comparing the photo to a variety of referencemodels of the scene taken from differentorientations .

ACRONYM

This system currently under development atStanford uses models of objects and scenes toconstructively interpret scenes. It attempts tobuild a plausible interpretation by analyzing asituation and explaining its principal features byrationalizing it from its known possibilities. Ithas recognized a photo of an airplane on a runwayand correctly identified its principal parts.

Many other contractors under DARPA supporthave built vision systems, including

SRI,

Hughes,and USC.

MACHINE INTELLIGENCE CORP.

This company has packaged some vision workpreviously done at SRI. For $16,000 you can buy asystem with camera that can quickly learn torecognize new objects and identify theirorientation. The system can be trained in lessthan 5 minutes. It performs well, and shouldcontribute significantly to the enhancement ofrobotic arms. It uses contrast and region growingto identify boundaries; it uses statisticalmeasures of overall shape to determine identityand orientation. The user can accentuate orattenuate the system's learned features.

NATURAL LANGUAGE FOR DATA BASE RETRIEVAL

LADDER

This system was developed under DARPA supportfor the Navy ACCAT. It provides an English queryfront-end to ship data bases. It can answer manytypical and complex questions. It translatesthese questions into a semantic representation andthen determines the best way to obtain the neededdata. Because the data base system may usedistributed files, this retrieval can be verycomplicated. LADDER provides many nice human-interaction features. The user can extend thelanguage it understands by creating new syntacticparaphrases for existing query forms.

ROBOT INC.

This company markets another natural languagefront-end to data base systems.

SYSTEM BUILDING TOOLS

INTERLISP

Undoubtedly the most powerful programmingfacility, INTERLISP has evolved for about 5 yearsunder DARPA support. The programming systemprovides hundreds of useful and powerfulfacilities for interactive programming. INTERLISPprograms can be interpreted or compiled. Theprogramming environment includes comprehensivefile systems , error and debugging packages ,spelling correctors, redo and undo facilities, andmany other support features .

RITA/ROSIE

These two systems developed at Rand providerule-based programming languages that can supportinteractive network communications and pattern

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matching. RITA is quite similar to EMYCIN exceptfor its additional communicatijOn capabilities. Itwas developed for PDP-11 UNIX systems. Althoughintended exclusively as an experimental language,it was used by more than 50 government offices.

ROSIE is a vastly more powerful and elegantprogramming system. It uses a large and friendlysubset of English as the programming languagesyntax. It translates user programs intoINTERLISP. It is intended for knowledge bases ofa few thousand relations and relatively large setsof heuristic rules .SYMBOLIC MATHEMATICS

MACSYMA

This system developed at MIT is probably theworld's best expert at symbolic integration anddifferentiation problems. It is in regular use byhundreds of people worldwide. Symbolicmathematics problems are solved by reformulatingone expression into an equivalent one in the hopeof eventually finding computable or simplif iableexpressions .

REDUCE

This system supports a variety of algebraicreasoning tasks. It has the additional feature ofportability, because it uses a bootstrapping LISPcalled R-LISP that has been ported to numerousmachines. This system was developed at Utah.

MILITARY APPLICATIONS

TECA

This system was developed for Navy ThreatEvaluation and Countermeasures Assessment at Rand.It was programmed in RITA to play the role of anon-board intelligent assistant. It accessedremote and dynamically changing data bases andconversed in English with the user. TECA computedthe relative threat to a blue task force in termsof the firepower advantage of red fleet elements.In addition, if the user desired, TECA couldreason forward in time to assess potentialthreats. For example, if blue's mission requiredit to follow a certain route and to avoiddetection, TECA could assess the possible actionsof red fleet elements that would jeopardize itsmission. Although originally written at Rand as250 rules, TECA was adapted by the end-users atNOSC. Within a year, they had written over 1000rules in RITA to make TECA valuable to them. TECAcontrolled graphic displays and networkingcapabilities .

HASP

This system .was built for signal processingproblems in ocean surveillance. The work, whichis classified, demonstrated the superiority ofheuristic methods over statistical correlationapproaches to signal interpretation. It achievednearly 1000 times greater speed and betteraccuracy. The system was developed to ademonstration capability in one year. It used theHEARSAY-II architecture. Subsequent developmentefforts have continued on this system at SystemControls Inc.

MULTISENSOR ESM

This system being developed at SRI integratesa variety of sensor data fed to a fighter-bomber.It reasons about the most likely identities of theemitters and allocates sensor and computingresources to process the most critical data. Itbalances uncertainty, threat, informationgathering, and computing time in an effort tomaximize survivability of the plane.

TATR

This system being developed at Rand willproduce Tactical Air Targeting Recommendations.Three preliminary systems have been produced inRITA,

INTERLISP,

and ROSIE. Current efforts aimto analyze the best way to attack air bases underdifferent high-level objectives. All of thesystems developed thus far have used Englishlanguage as the human interface. Those in ROSIEare also programmed in English.

2.3 FUNCTIONS UNDERLYING CURRENT APPLICATIONS

Heuristic rules of inferenceDeductionHypothesization and lines -of-reasoningDefault reasoningData dependencies and belief revisionNatural language understandingNatural language generationMultiple sources of knowledgeStatistical/probabilistic reasoningHeuristic search

Examples follow.

REPRESENTATION FORMALISMS

English: If a ship is missing,it's not-a-friendly vessel

NOT FRIENDLY-VESSEL(x)

LISP: (DEFINE ( (NOTFRIENDLYVESSELP(LAMBDA (X) (AND (SHIPP X)(MISSINGP X))))))

ROSIE: Assert every missing shipis not a friendly-vessel.

English: Morgan is a dog, all dogsare mammals, all mammals

LISP: (PUTPROP MORGAN DOG T)(DEFINE ((MAMMALP (LAMBDA (X)

(GETPROP X (QUOTE DOG)))(ANIMALP (LAMBDA (Y)(MAMMALP Y))))

ROSIE: Assert Morgan is a dogand any dog is a mammaland any mammal is an animal.

Representation formalismsFacts

Causal models and simulation

Logic: SHIP(x) _ MISSING (x) =>

are animals.Logic: DOG(Morgan) _ FORALL(x) DOG(x) =>

MAMMAL(x) _ FORALL(y)MAMMAL(y) => ANIMAL(y)

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FACTS

Facts include taxonomies, commonsenserelationships, and ordinary data. Here are a fewfrom a tactical military application.

Assert unit 13 is a unitwhose level is companyand whose type is engineering

brigadeand create a state (s) of unitl3

whose time of arrival is 0400 hoursand whose location is from PVand whose probability is 100 percentand whose time-engaged in battle

is 0 hours .For every unit which has a state whose

time of arrival > 0200 hoursassert that unit is deployed on

flat_ground in that stateand that unit is able to

maneuver_rapidly in that stateand that unit is deployed since

0200 hours in that state

HEURISTIC RULES OF INFERENCE

These are the informal but practical rulesthat a person uses to solve most non-numericalproblems. Some familiar examples follow.

Commuting: The fastest route betweenhome and work goes from home tothe nearest freeway on-ramp,over the freeway system to theexit nearest work, and thenceto work.

Route -planning in general: The fastestroute between x and y goes from xto the nearest freeway on-ramp,over the freeway system to theexit nearest y, and thence to y.

Transformations in general: The mostefficient way "o transform x to yis to use the most efficientmeans possible for the largestpossible portion (xl to yl) andthen to use the most efficientmeans possible to transform x toxl and yl to y.

Notice that these heuristics also form anabstraction hierarchy, as dogs, mammals, andanimals did.

DEDUCTION

The simplest form of deduction is thesyllogism:

All greeks are mortal.Socrates is a greek.Therefore, Socrates is mortal.

Logic has developed the predicate calculus toprovide syntactic rules of logical deduction.

AI has implemented such calculi, but theyprove computationally intractable because they

search exponential spaces (exponential in thelength of the derivation of a theorem).

Both people and machines can limit the spacesthey search to derive theorems or to prove them.One approach is to work backwards from the goal;this is called backward-chaining. Another is towork simultaneously from the goal and what isknown; this is called either means-ends orbidirectional search. A large class of AI systemsuse pattern-directed rules to deduce subgoalsfrom goals and consequences from antecedents.These rules independently "recognize" situationsto which they can apply.

The assertions and goals are stored in arelational database. The rules are proceduresthat check the database and make new assertions.The over-all flow-of-control is up to theprogrammer or the system in which the program iswritten.

HYPOTHESIZATION AND LINES-OF-REASONING

Whenever we want to identify an unknownobject or explore the consequences of a potentialaction, we need to consider a correspondinghypothesis. This is like "case analysis" inmathematics or "conditional reasoning' ingeometry. In AI systems, an hypothesis istentatively added to the database. Problem-solving and deduction continue as if thehypothesis were true. Competing hypotheses arekept separate. Possibly consistent hypotheses areallowed to merge and interact.

A single set of plausibly consistentpossibilities constitute a "line-of-reasoning"for the program. Most programs follow a smallnumber of lines-of-reasoning based on heuristicsabout the most promising paths to pursue. Inprojecting the likely axis of an enemy attack, forexample, we would need to hypothesize alternativemovements of each of the force elements usingknowledge of their doctrine, tactics and likelyobjectives. Below are some ROSIE rules for thatkind of application:

If there is a general attack,go hypothetically move unitB6 between at (the timeof the general attack) withprobability 40 since "UnitB6probably will move betweenunits 78 and 82"

and go hypothetical ly_move unit22 between

at (the time of the generalattack) with probability 50since "Col Newell said so".

For each potential state of unit22in window,if unit22 does move inside in that statego hypothetically_move unitB6 between

at (the time of that state)with (the probability of that state)* 80 percent / 100 percentsince "Orange doctrine calls forconcentration of attackingforce, and unit 22 is betweenunits 18 and 82".

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DEFAULT REASONING

One big difference between informal andformal logics arises from the fact that we oftenhave to reason without perfect information or inthe absence of much potentially usefulinformation. Humans reason by defaultextensively. AI programs do this by checkingcertain paths until nothing is found or until anallocated amount of time has passed. Then, theprogram assumes a particular proposition is true.

Examples in ROSIE:

If USS Enterprise has a runway and thatrunway has no length

and there is some ship (s) in the classof USS Enterprise which hasa runway (r) such that r hasa length (len)

let the length of USS Enterprise'srunway be len.

If the sex of Cmdr Jones is not knownand 'Cmdr Jones is female'

is not provably true,assert Cmdr Jones is male.

DATA DEPENDENCIES AND BELIEF REVISION

Any system that reasons hypothetically orassumes default cases will need to be able tochange its beliefs when contradictory data arise.To do this, AI systems keep audit trails called"dependencies" that link current beliefs to rulesand data which suggested and supported them. Whennew data arise that controvert some

belief,

theerroneous belief must be denied. Beliefs thatdepended on that fallacious belief must also bechanged appropriately.

NATURAL LANGUAGE UNDERSTANDING

Most systems today have natural languagefront-ends or user interfaces. A large number ofparsers have been constructed. These languagesystems typically have a grammar of English withsemantic actions associated with each grammaticalrule. The grammar and actions are input to acompiler-compiler that generates a parser forsentences in the language. For example, a part ofROSIE 's grammar looks like this:

[SUCH] 2/dl/ cl2/sl/rc]desc

class cnoun [(var)] 2/pl/ 2/ 1/pp prep term

( prep term )

re WHERE/SUCH THAT sentenceWHERE/SUCH THAT (condition)

THAT/WHO/WHICH vp

tenli A/AN [NEW] descSOME descEVERY descEACH OF term 2/ 1/rraT AN2/tl/mONE OF term 2/ 1/rmT OR2/el/ANY desc

ROSIE does not understand much of thesemantics of natural language. It has beendesigned as a general purpose programminglanguage. On the other hand, systems like LADDERwhich are only used for database retrieval havemore powerful semantic procedures that make themmore useful in question answering.

Speech understanding systems, in addition tothese other capabilities, must also performdifficult signal interpretation tasks. Theyperform signal filtering, segmentation, phonepattern classification, word hypothesization, andsyntactic and semantic analyses. The problem ishard because many sources of knowledge are neededto reduce the uncertainty sufficiently. Becausethe search spaces for these tasks are so large,they use many clever strategies to search the mostpromising paths first.

NATURAL LANGUAGE GENERATION

Many systems have been built that print -outEnglish descriptions. These systems use a syntaxof English but begin with a thing to be described,e.g. an answer to a question, or a summary of astory. Text generation uses a sentence grammarand a discourse grammar, the latter determiningwhich things to say first and how to use anaphoraand ellipsis to reduce verbiage.

Speech generation needs some additionalthings. A chip like Tl's speak-and-spell, usespre-stored LPC templates for each phrase. Othersystems synthesize each word from its syllablesaccording to phonetic grammars. These grammarstransform symbols into frequency, amplitude, andduration patterns which drive speakers.

MULTIPLE SOURCES OF KNOWLEDGE

As problems get harder, problem-solvingrequires more knowledge to overcome the complexityand difficulty. Often this means more than onekind of knowledge bears on the same problem.Speech and vision tasks reveal this clearly; eachuses about a dozen different kinds of knowledge.AI has developed system organizations tofacilitate the interaction between multiplespecialist programs. Hearsay-II is the mostwell-developed of these architectures. It allowseach knowledge source to be coded as an

iction GO procedure [term] 2/pl/ 2/ 1/ASSERT assertsent 2/Nl/assertsentT 2/ 1/DENY assertsent 2/Nl/assertsentT 2/ 1/LET term BE term 2/Nl/term BE termT 2/ 1,

issertsent primsentterm IS A/AN descterm IS [NOT] PROVABLY TRUE/FALSE

irimsent term primvp

>rimvp IS/WAS/WILL [NOT] [BE]A/AN cnoun 2/pl/ 2/ 1/

IS/WAS/WILL [NOT] [BE] adj 2/pl/ 2/ 1/IS/WAS/WILL [NOT] [BE] complement

term 2/pl/ 2/ 1/DID/DOES/WILL [NOT] verb 2/pl/ 2/ 1/DID/DOES/WILL [NOT] verb term 2/pl/ 2/ 1/

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independent specialist. Each specialist isinvoked to work on problems that match its patternspecification. An overall system schedulermaintains a queue and decides who should executefirst. This system makes it easy to distributethe problem-solving over parallel machines , andthis has been done.

Multiple sources of knowledge usuallycorrespond to different levels of aggregation andabstraction. In speech: acoustic segments,phones, phonemes, syllables, words, phrases,syntactic phrase structures, semantical lymeaningful fragments, sentences, paragraphs. Invision: pixels,

reflections,

shadows, edges,boundaries, regions, surfaces, objects,configurations, scenes.

STATISTICAL AND PROBABILISTIC REASONING

Many problems require numeric weightings. AIsystems have used many different schemes fordifferent purposes. Some of these include:probability, likelihood, conditional likelihood,joint probability, ranges of uncertainty,certainty factors, Bayes estimators, odds,estimates of necessity or sufficiency, shortfallfrom optimal, "interestingness" and degree ofsupport. These techniques are usually essentialbut the variations in detailed procedures seeminsignificant.

HEURISTIC SEARCH

One of the weakest but most general methodsin AI problem-solving is heuristic search. Itrefers to a procedure of generating and testingsuccessive moves towards a goal. For example, ingoing from home to work, the heuristic searchmethod asks you to generate alternatives from thehome starting point and, iteratively, asks foralternatives to succeed each of these.Eventually, one sequence of alternatives willreach the goal. Several important systems haveused heuristic search effectively, includingDENDRAL. In such cases, the search was alwaysmade tractable by introducing early and effectivepruning to eliminate or postpone unlikely paths.

CAUSAL MODELS AND SIMULATION

AI systems frequently need to reason aboutthe effects of planned actions. To do this, theyincorporate models about causal relationships inthe domain. Then they can look for actions thatentail desired effects and avoid those that entailundesired effects. One familiar way of projectingthe effects of some actions is through simulation.Another method uses deduction to infer possiblecauses of an effect or possible effects of anaction. The same mechanisms are involved here asin other kinds of deduction and problem-solving,except that the system needs to represent actions,events, causes, effects, preconditions, andsuccessive non-deterministic states of the world.Some or all of these things are represented inmost diagnosis systems.

2.4 CENTRAL CONCEPTS AND THEORETICALFRAMEWORK

Most of AI can be unified under a broad viewof intelligent behavior as problem-solving. ManyAI tasks naturally seem to be problem-solvingactivities, and much of the expertise in AIconcerns the construction of expert problem-solvers.

In addition to understanding AI as problem-solving, we need also to recognize some of theother central concepts in AI including especiallythe acquisition and organization of knowledge, andmachine architecture. Some of the elements of thetheoretical framework are briefly sketched below.

AI AS PROBLEM-SOLVING

Given a goal, how do we achieve it? Or evenmore generally, given some capabilities, whichshould we exploit? Most tasks can be formulatedin these terms. The more direct are our methodsfor solving a problem, the faster we perform.Conversely, if we need to try out possibilities invarious combinations to find a solution, such asearch process can take lots of time. As we gainknowledge about a problem domain, we should beable to reduce the amount of search which problemsin that domain require. Most expert systems uselots of knowledge encoded as independent ruleswhich can be added incrementally. These rulessuggest promising lines-of-reasoning which areexamined before less promising ones. Searchmethods which examine all possibilities areexhaustive but can guarantee optimal solutions.Ordinarily, less than optimal solutions aresatisfactory, especially if one can be found veryrapidly.

THE BASIC IDEAS

3. Efficiency directly affects success

The quality and generality of knowledgeThe rapid elimination of "blind alleys"The elimination of redundant computationThe speed of the computerThe use of multiple sources of knowledge

5. Problem complexity increases with:- Errorful or dynamically changing data- The number of possibilities to beruled out

The amount of effort required to ruleout a possibility

Acquisition and Organization of Knowledge

AI systems must be concerned with knowledgebecause it is knowledge that makes for competentbehavior. A chess master is no better atproblem-solving in general than is an electrician.Each is a specialist, possessing considerableknowledge about their particular domains ofexpertise. Thus, a central task in AI is

1. Knowledge = Facts + Beliefs + Heuristics

2. Success = Finding a good-enough answerwith the resources available

4. Efficiency increases with:

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capturing and encoding knowledge about specificdomains. This has led to an emphasis on ways toacquire and organize knowledge. The most recentwork in this area uses hierarchical taxonomies ofboth domain concepts and heuristics, as previouslyillustrated in 2.3. Much additional work is nowstarting on knowledge acquisition as a problem-solving task. In such a task, we need to developheuristic methods for acquiring new knowledge.This work has begun. There are now a few programsthat translate English directly into programs,some that develop operational methods for carryingout unoperationalized heuristics, and others thatattempt to reorganize knowledge for purposes ofefficient application.

Machine Architecture

AI systems integrate knowledge and problem-solving organizations to search as rapidly aspossible. Efficiency of search is the keyengineering issue. Improvements in machinearchitecture are central objectives of the field.For example, each increase in speed of generalpurpose processors enables Chess 4.9 to searchfurther ahead. The last speed improvement, afactor of 10, allowed one-ply deeper searcheswhich took the program from "A" rated to "expert."Some have speculated that another factor of 10will take it to "master" or "grand master." Iconcur .

Most AI systems are written in

LISP,

becausethis language allows flexible manipulation ofsymbolic expression. LISP systems have severalarchitectural features that motivate some recentmachine designs. Data and program are stored inthe same format and are freely interchangeable.New programs can be constructed dynamically andneed to be interpreted or incrementally compiled.Previously used data or programs may cease to bereferenced by current data and programs, and thusrequire "garbage collection." To avoid longpauses for garbage collection, incremental garbagecollection would be nice. This leads to tags onevery machine word that maintain reference countsto the word and interleaved or asynchronousreclamation of words whose counts go to zero.

Nearly all AI systems have been built on theDigital PDP-10 or PDP-20, which has 36-bit wordsand 18-bits of addressable (word) memory. Thismeans programs cannot easily exceed 256K words insize without memory management functions. BecauseLISP programs consist exclusively of lists ofpointers to other elements, these pointers need tobe at least 18 bits just to refer to items incore. They would have to be longer if virtualmemory were available. But 18 bits allows twopointers per word, and these correspond to LISP sCAR and CDR, the two components of each LISP cell.In short, the PDP-10 makes it very hard to exceed256K addressable core and this greatly constrainsprograms.

Nearly all practical AI programs exceed thismemory limitation. This means most of theprogrammer's time is concerned with ways to workaround the memory limitation. Thus, the PDP-10and PDP-20 cannot long be tolerated.

Many new systems are being built with largeraddress spaces. The best LISP system,

INTERLISP,

already exists on two different in-house machines

built at Xerox PARC. These are personal computerswith 24 bits of address space and the speed of thePDP-10 (about .3 and .8 MIPS). MIT built aspecial machine exclusively for MACLISP and haslicensed two manufacturers in Santa Monica. Theirmachine has a 32-bit address space, incrementalgarbage collector, high-resolution rastergraphics display, and other integral LISPfacilities. It will sell for about $80K. One ofthese companies is partially owned by ControlData. One LISP system has already been built forthe Digital VAX-11/780, and DARPA is sponsoringthe development of INTERLISP for this machine.

The DARPA VLSI program promises to make newAI machines readily available. Many AI people areworking on intelligent assistants for VLSI designas well as special designs for AI machines. Amongthese new architectures are VLSI designs forrelational data bases and for associativememories. Tree-searching, as in ordered backwardchaining, can be implemented extremely efficientlywith some new "tree machine" designs. AI and VLSIshould interact heavily in the 80 's to helparticulate the theory and practice of machine-problem-solving. The whole MIT LISP machine, forexample, would easily fit on one chip.

Advances in microelectronics will boostrobotics possibilities enormously. New sensorsand on-board intelligence for mechanical controlwill make many previously intractable tasksfeasible.

A Functional Breakdown of IntelligentSystems

Another way to organize thinking about AI isto comprehend the functional parts of intelligentsystems. A rudimentary breakdown is given below:

Main processes:PerceivingModelingActing

Perceiving uses a model to interpretsensor data

Interpretation requiresproblem-solving

Examples include speech, vision,text understanding

Modeling produces a model of the worldModeling requires knowledge

acquisition and organizationActing requires

MonitoringPerceivingPlanningCommunicatingMonitoring requires forecasting

Forecasting requires deductionand simulation

Planning requiresModelingProblem- solving

Communicating requiresLanguage generation and

understandingPlanning

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2.5 NEW APPLICATIONS OF EXISTING

TECHNIQUES

The most promising avenues for applyingexisting technology include:

o knowledge bases for storing andretrieving symbolic data

o natural language front-ends

o speech input and output

o intelligent assistants fortime-stressed tasks

o exemplary programming foruser-tailoring of procedures

o vision controlled robots

o intelligent computer-aided instruction

o weapons control systems

o built-in diagnosis

o diagnosis and maintenance aids

o hypothesization and hypothesismanagement aids for analysts

2.6 IMPEDIMENTS TO WIDER APPLICATION

The major impediments to wider application ofAI are (1) a shortage of AI professionals; (2) theexpense or unavailability of suitable, large, fastmachines; (3) the difficulty of acquiring andformulating knowledge.

2.7 CONTINUING FOCI OF ATTENTION

1. Knowledge acquisition.

This is really knowledge programming, so it takeshybrid experts who know both AI and some domain ofapplication or teams of these people. Also,there's no cookbook approach, so there's currentlymore than one way to approach most tasks.Moreover, there no generally good way to integratediverse sources of knowledge in reaching asolution. Independent sources of knowledge areeasy to acquire and apply, but hard to integrate.

2. Knowledge compilation

How do we reconfigure a knowledge system to makeit efficient? A few examples point the way, butthis is a big problem. Of course, it subsumesconventional compiling issues that arise inprogramming.

3. Machine architecture for AI.

4. Causal models for comoonsensephysics.

5. Vision.

6. Unrestricted natural languageand speech.

7. Robotics.

Learning.8.

9. Distributed AI .There's a need and a push to exploit the cheap,small machines. Cooperative problem-solving willintegrate humans and machines.

10. General purpose deductionsystems .

3. RELEVANT ECONOMIC AND SOCIAL ISSUES

I believe there are many non-technicalforces that are impelling us towards AIapplications. Understanding these may help usidentify the right targets for AI R_D. I willdiscuss four forces: (1) the informationprocessing revolution, (2) the skill pool andproductivity decline of the

US,

(3) limits on newsystem acquisitions, and (4) rising costs ofmaintenance and readiness .3.1 INFORMATION PROCESSING REVOLUTION

It is not necessary to discuss all the waysthat computers are changing our lives. Theimportant point is that because computers arebecoming übiquitous, familiarity with computing isincreasing widely and rapidly. This leads toincreased expectations on the part of consumers.High quality arcade games, home computers, andinformation networks will soon be commonplace.Large, unsophisticated, and unattractivecomputer-based systems will no longer satisfy theknowledgeable client.

Machines that now sell for peanuts have somebetter man-machine interfaces than many largesystems. Tl's toy speak-and-spell for example hasvery good quality artificial speech output.Raster-scan graphics have become commonplace.Touch sensitive screens and free standing mouseshave replaced joysticks, lightpens, andtrackballs. Many home computers offer thesefeatures. For a few hundred dollars, you can buya chess playing machine that'll beat any averagechess player. For a few dollars more, you can buythe version of the machine that obviates unnaturalforms of communication; it simply feels thepositions of all pieces.

The point of this discussion is that manyexisting industrial firms now produce dull systemswith out-moded technology, while others are racingahead with exciting, flashy and cheaper products.People will come to view the older technology as apoor reflection on those who purvey it.

At the same time, commercial versions ofintelligent systems are relatively few. However,we should anticipate an explosion of intelligentgames and home products in the next 10 years.Meanwhile, there is an opportunity to marketmilitary and industrial systems that combineintelligence with the other types of improvedcomputing devices. Such systems should enable usto offer much more "bang for the buck." In otherareas of computing, decreases in manufacturingcosts have been coupled with simultaneousofferings of bigger and better systems. This hasenabled the vendors to keep average system pricesrelatively constant while increasing variety andoverall volume.

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3.2 SKILL POOL AND PRODUCTIVITY DECLINE

Intelligent systems are necessary to overcomethe reduction in skilled manpower and thereduction in worker productivity. The militaryfaces an inevitable shortage of skilled workerswhose tasks, by comparison to expert systems, arerelatively simple. Moreover, the intelligence ofnew recruits is declining to the point where manytasks may be too hard for them. Intelligentsystems should offset this problem.

Companies that have applied vision-lessrobot arms to manufacturing tasks report thatthese arms (which cost up to $200,000) pay-off inless than 3 years. Cheap vision systems will makerobots much more widely applicable. Robot armswill also decline in cost considerable. Mobilerobots should increase dramatically with theexplosion of cheap, light-weight computer power.Robots are here today, and they will probably playthe leading role in national efforts to increaseproductivity.

AI provides the base of technology on whichthese advances depend.

3.3 LIMITS ON NEW SYSTEM

ACQUISITIONS

I believe the 80's will be characterized bylimited initiative and system development efforts.The budget pressures seem overwhelming, and astime passes, the labor costs of the military willincrease substantially. These factors willadversely affect efforts to undertake huge newsystems .

Nevertheless, there are pressing problemswhich will get top priority. Chief among these,in my opinion, is making command and controlsystems that can perform at all and, after that,than can perform flexibly and effectively.Current systems are a hodge-podge of hardware withlittle in the way of integrating concepts ofperformance. Tactical systems, for example, areexceedingly vulnerable to communication attacks.Reconstitutable communication systems seemessential. Such a capability would seem torequire distributed intelligent processors toreestablish pathways and to help regulateexcessive demands for use.

Other high priority problems arise from thetime-stress of modern warfare. Humans cannotassimilate target data or control weapons systemsquickly enough to withstand attacks . Thus pointdefense seems to require intelligent systems.Similarly, current platforms may not be able topenetrate densely defended areas. I believe theanswer to this problem lies in unmanned,cooperative, dynamically replanning vehicles.These vehicles should acquire, process, and adaptto real-time sensor data.

Finally, smaller systems do not necessarilymean less effective systems. Humans, for example,can outperform many larger beasts of prey.'Similarly, we should anticipate many opportunitiesfor less expensive space and defense systems thatcompensate by substituting knowledge for othersources of power. The trick is to learn to buildknowledge-based augmentations to new systems as

effectively as we currently build othersubsystems, such as power and communications.

3.4 RISING COSTS OF MAINTENANCE _READINESS

For many reasons, maintenance costs are sky-rocketing and readiness is falling. The militarymay be seriously limited by' its inability toovercome this problem. Again, intelligent systemsseem to be the only good idea around. Expertsystems for maintenance, intelligent aids formechanics, built-in diagnosis systems, and active"procedure manuals" are all underway incommercially available systems. One obvious placefor improved maintenance tools is in softwareitself.

4. AI FOR MILITARY SYSTEMS: SELLINGPOINTS

I have previously touched on the various waysAI might help solve military problems. Thissection takes a different cut at the issue byaddressing the kinds of needs AI can address andthe kinds of sales appeal it can offer.

4.1 OVERCOMING RESOURCE LIMITATIONS

It's a truism that intelligence makes up forother deficiencies. A corollary is, where there'sa resource in short supply, there's a naturalmarket for intelligent systems. AI can saveenergy, can save time, can reduce manpower needs,can eliminate paper, and so on. The trick isfinding those applications which permit bigimprovements through small amount of AI R&D.However, the biggest AI program to date took lessthan 50 man-years to develop. Most take about 10.

4.2 ENHANCING PERFORMANCE

Nearly every system can have its performanceenhanced through AI capabilities. Naturallanguage and deductive capabilities would augmentmost systems that process information. Manysystems place excessive burdens on humans and willnot perform well in a crisis. These systemsshould be augmented with intelligent assistantsthat make the human a manager of softwareprocesses

4.3 TRACTABILITY

Many problems cannot be solved byconventional, algorithmic means. Oceansurveillance, for example, can't be solved bycorrelational methods for any reasonable amount ofmoney. These tasks require heuristic methods fortractability. By giving up the search for optimalsolutions, many otherwise intractable problemsremain feasible. Moreover, by using AI methods,we can assimilate and exploit the non-algorithmicknowledge essential for solving most naturalproblems .4.4 FLEXIBILITY

Old computer systems were inflexible becausethe software technology required the program to

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algorithmetize the problem-solving. This is ahard task and it can only be done for relativelyconstrained problems. On the other hand, AIsystems provide a variety of types of flexibility.

"Natural language allows flexible queries anddialogues. Opportunistic problem-solvers canincorporate new data at any time, can takeguidance about priorities, and can interact withother human or machine problem-solvers. Knowledgebases support multiple types of uses of the samepiece of knowledge, e.g., prediction, diagnosis,and training.

4.5 INTELLIGENCE

Surely the greatest attraction of AI is thatit offers the promise of making really smartsystems. The work in expert systems hasdemonstrated that very high performance can beachieved with AI systems. The military has a fewcandidate problems that deserve this kind ofexpert performance: strategic battle management;tactical situation assessment; targeteering androute planning; logistics; and weapons control inpoint defense.

4.6 MODIFIABILITY AND INTELLIGIBILITY

4.7' MANAGING COMPLEXITY

Early periods of technological advance arecharacterized by increases in complexity due tonew and varied ways of solving problems. Modernmilitary systems are extremely complex because ofdiverse, even chaotic hardware and softwaresubsystems. Intelligent behavior requires thiscomplexity to be managed. Management in turnrequires many of the capabilities of AI, such asplanning, causal modeling, diagnosis, andforecasting. AI systems should be built to helpmanage the complexity of military systems andorganizations. This should help return humans tosimpler yet more important tasks than theycurrently face.

4.8 ATTRACTIVENESS

Any large hardware-software system built in1985 should have friendly, flexible, intelligent,and robust human interfaces. Those that don twill be judged unattractive. By 1990 systemswithout such capabilities will probably seembackwards and quite archaic.

BIBLIOGRAPHY

1. B. W. Arden (Ed.). What can beautomated? The computer science andengineering research study.Cambridge, Ma. MIT Press, 1980

Many computer .systems persist for decades,although they probably should be rewritten everyfive years. The world changes so often thatmodifiability is increasingly important. Forsystems to be modifiable in a changing world, theusers of the system need to understand thecomputer code. This makes the representation andorganization of knowledge in computer systems veryimportant. AI provides several techniques forincreasing the intelligibility of systems:hierarchical organization, English programming,and question answering facilities, among others.

Artificial intelligence2. M. Boden.Basic Books, NY.and natural man.

1977

3. R. A. Brooks, R. Greiner, _ T. 0.Binford. The ACRONYM model-basedvision system. Proceedings of 6thIJCAI . Stanford Computer ScienceDept. August 1979.

4. E. Charniak, C. K. Riesbeck, & D. V.McDermott . Artificial Intelligence

5. L. D. Erman, F. Hayes-Roth, V. R.Lesser, _D. R. Reddy. The Hearsay-II speech-understanding system:integrating knowledge to resolveuncertainty. ACM Computing Surveys ,vol. 12, no. 2, June 1980, 213-254.

6. G. Freiherr. The seeds of ArtificialIntelligence. NIH Publication No.80-2071. Division of ResearchResources, NIH. Bethesda, Md. 20205.March 1980.

7. Gorlin, D., Hayes-Roth, F. ,Rosenschein, S., Waterman, D.,Sowizral, H. Programming in ROSIE:An introduction by means of examples.N-1646-ARPA, The Rand Corporation,Santa Monica, Calif., February 1981.

8. G. G. Hendrix, E. D. Sacerdoti, D.Sagalowicz, _J. Slocum. Developinga natural language interface tocomplex data. ACM Transactions onDatabase Systems , Vo l . 3 . , No . 2 ,June 1978, 105-147.

9. D. R. Hofstaeder. Goedel, Eschar,Bach. New York: Basic Books, 1980.

10. D. M. Keirsey. Natural languageprocessing applied to Navy tacticalmessages. Technical Document 234.Tactical Command and ControlDivision, NOSC Code 824, Naval OceanSystems Center, San Diego, Ca. 92152February 1980.

11. W. A. Lea (ed.). Trends in speechrecognition. Prentice-Hall,Englewood Cliffs, NJ. 1980. tiO 12P. McCorduck. Machines who think.Prentice-Hall, Englewood Cliffs, NJ1979.

13. A. Newell _H. A. Simon. Humanproblem solving. Prentice-Hall,Englewood-Cliffs, NJ. 1972.

15. D. A. Waterman _ F. Hayes-Roth(eds.). Pattern-directed inferencesystems. Academic Press, NY. 1978.

16. P. H. Winston. ArtificialIntelligence. Addison-Wesley,Reading, MA. 1977

Programming. Erlbaum Associates,Hillsdale, NJ. 1980.

14. N. Nilsson. Artificial IntelligenceTioga Press, Menlo Park, CA. 1980