Two expert systems apply the Procedural SemanticNetworks formalism to assess left-ventricular behavior

and diagnose cardiac rhythm disorders.

Building Knowledge-Based SystemsThe PSN Experience

John Mylopoulos, Tetsutaro Shibahara, and John K. Tsotsos, University of Toronto

Knowledge representation is recognized today as acentral problem in artificial-intelligence research becausethe current design paradigm for expert systems stressesthe need for a knowledge base that stores expert knowl-edge and provides associated knowledge-handling facil-ities. This paradigm sharply contrasts with earlier onesthat emphasized general-purpose heuristic search tech-niques.

Research in knowledge representation may involve astudy of how we can represent particular semantic no-tions, such as time, causality, beliefs, and intentions. Onthe other hand, it may take the form of a language designproject, where the language is intended for knowledgerepresentation and the "programs" written in thelanguage are knowledge bases storing knowledge aboutsome domain of expertise. A third alternative is thatresearch in knowledge representation may involvedeveloping a programming environment, in the sense ofUnix or Interlisp, for building and using knowledgebases. The Procedural Semantic Networks, or PSN,project began at the University of Toronto in 1976 as alanguage design and implementation project. Since lastyear, however, we have been paying increasing attentionto programming environmental issues.

Knowledge-representation languages have been clas-sified traditionally as declarative or procedural, depend-ing on whether their basic features come from mathe-matical logic or data structures, on one hand, or fromprogramming languages.1 Declarative representationlanguages, such as Prolog and KL-One, encourage per-spicuity and support the maintainability of a knowledgebase. Procedural representation languages are partic-ularly well suited for heuristic knowledge, and their use

can lead to efficient searching on the part of an expertsystem. The general framework for procedural repre-sentation languages offered by production systems hasbeen used successfully in designing several expertsystems.

Since Minsky's influential "frames paper,"2 many at-tempts have been made to integrate features of declar-ative and procedural representation languages, a topicthat persists in ongoing research. PSN is one such at-tempt that focuses on the integration of semantic net-work and procedural notions.

An overview of PSN

The following description of the main features of PSNemphasizes the background and philosophy of its design.An initial design for PSN was proposed by Levesque3and outlined by Levesque and Mylopoulos.4 Furtherwork on the language and its implementation has beensummarized by Patel-Schneider and others.5

Semantic networks. Semantic networks were first pro-posed in the mid-1960's as a (rather crude) model ofhuman memory. Each semantic network can be viewedas a directed, labeled graph whose nodes represent en-tities or concepts-such as, the person John Smith, theword "apple," or the concept of student-and theirlinks represent binary relationships-for example, JohnSmith's relationship to his wife, the semantic relation-ship between the words "apple" and "fruit," or betweenthe concept of student and those of person and student-number. The main feature of semantic networks is their

0018-9162/83/1000-0083$01.00 ' 1983 IEEE 83October 1983

associative viewpoint, which admits an obvious graphicalrepresentation and can be used to define conceptual orimplementational access paths. Some of these paths canbe, and indeed have been, used to organize semantic net-works along different dimensions. Traditionally, theirmajor drawback has been their designers' overdepen-dence on intuitions derived from node and link labelsrather than on a formal semantics.6 PSN deals with thisissue by associating programs with every generic object ina PSN knowledge base, programs which specify how tooperate on instances of that generic object. This ap-proach is very much in the spirit of abstract data typesand some of the ideas of the frame proposal.

Basic features. A PSN knowledge base consists of ob-jects, which can be tokens, classes, links, relations, andprograms. Tokens represent particular entities-such asthe person John Smith, the number "7," and the string"story"-and are interrelated through links representingbinary relationships between entities. Classes representgeneric concepts-like person, number, and string-whilerelations represent such generic relationships as thebrother-of relation between a male and another person.Each token is an instance of at least one and usually severalclasses, while each link is an instance of at least one rela-tion.

Figure I shows a simple configuration involving twotokens, John and 23; two classes, Person and Number; arelation, Age; and a link relating John and 23. The figurealso indicates the INSTANCE-OF relationships betweenJohn, 23, and the John-23 link as well as those betweenPerson, Number, and Age. Four programs provide the"meaning" of each class by specifying respectively howto insert, remove, and fetch instances of the class, andhow to test whether an object is an instance of the class.Similarly, each relation has four associated programsthat specify how to insert or remove instance links, howto fetch all the objects in the range of the relationassociated with a particular instance of the domain, andhow to test if two objects are interrelated through an in-stance of the relation.

Figure 1. Two tokens, two classes, a relation, and a link ina PSN configuration.

A large knowledge base, like a large program, needs tobe structured by intuitive organizational principles to beunderstood by its designers and users. Moreover, it is theresponsibility of the knowledge-representation languageto provide these principles. PSN offers three such prin-ciples in the primitive relations INSTANCE-OF, PART-OF, and IS-A.We have already seen INSTANCE-OF links between

tokens and classes that constitute their types. A tokencan be an instance of several classes (for example, JohnSmith may be an instance of Person and Student). InPSN, the INSTANCE-OF relation is used to relate allobjects-tokens, classes links, relations, or programs-to their types. Thus we can have metaclasses that haveother classes as instances, as indicated in Figure 2. In-deed, every object in a knowledge base must be an in-stance of at least one class or relation. Several built-inmetaclasses make this possible:

* Object has all objects, including itself, as instances.* Class has all classes, including itself and object, as

instances.* Relation has all relations as instances.* Program has all programs as instances.

These metaclasses can be viewed as constituting the top ofthe INSTANCE-OF dimension. The depth of the dimen-sion depends on the application and may range from twotokens, classes, or built-in metaclasses to any depth.

Aggregation is a second mechanism useful in struc-turing a knowledge base. Each concept can be consideredan aggregate of simpler concepts, composed together in aparticular way. For example, a person can be conceptu-alized as an aggregate of his physical characteristics or,alternatively, as an aggregate of his social characteristics,such as name, address, and insurance number. This orga-nizational principle is realized in PSN through slots,which are associated with a class and specify the parts ofthe concept it represents. Each slot has a type and otherconstraints delimiting the objects that can be bound tothe slot. By viewing a class one level higher than theclasses defining the types of its slots, we can define a sec-ond organizational dimension, PART-OF, which placescomposite classes nearer the top and atomic classes at thebottom.A third organizational dimension relates a concept to

its specializations or generalizations. Thus, Person isrelated to Student, Employee, Male, etc., while Studentis related to Graduate, Undergraduate, Full-time, etc.Respecting a long-established tradition, we call the rela-tion between a class and its generalizations IS-A (so STU-DENT IS-A PERSON). A partial order relation betweenclasses, IS-A defines a generalization hierarchy, or IS-Ahierarchy. A key issue for IS-A hierarchies is the kind ofslot inheritance they support. Thus, if Person has slotsName and Age, these slots are inherited by its specializa-tions, such as Student. Of course, Student can have addi-tional slots such as "student-number" and "supervisor."We mentioned earlier that, like everything else, pro-

grams are objects. In fact, each program is a class whoseslots determine its parameters, prerequisites (conditionsthat must be true before each execution, and actions

COMPUTER84

(that must be carried out during each execution). Since aprogram is a class, it has its own generalizations, or pro-grams, from which it can inherit slots specifyingparameters, prerequisites, and actions. Patel-Schneiderand others5 discuss this issue.Another structuring mechanism is introduced to the

PSN framework by Tsotsos7 because of the interest inusing PSN knowledge bases for recognition purposes.This mechanism, also proposed by Minsky,2 involvessimilarity links for classes. These links can suggest otherclasses to be tried when the match fails between a givenclass and input data. After a failure, an exception israised; the identity of the exception depends on thenature of the failure. This exception determines whichsimilarity link should be used to suggest other classes tobe tried for matching.

Implementation. The current implementation of thelanguage is layered in the sense that each layer offers a setof representational features that includes features of theinner layers. Thus, PSN/0 offers the ability to constructknowledge bases structured only in terms of the IN-STANCE-OF relation. PSN/I adds IS-A and a simpleform of PART-OF to further facilitate the organizationof knowledge bases. PSN/2 introduces a more sophis-ticated and expensive version of PART-OF, along withsimilarity links and exceptions. The two systems de-scribed below use PSN/2. Further layers are beingcontemplated, for example, to treat programs as classes(at this point, they are treated as atomic objects) and torepresent temporal and causal notions (currently, theyare treated as constraints on slots values). An importantproperty of layered implementations - and one that weare striving toward-is that a PSN/i knowledge baseshould also be a PSN/j knowledge base forj< i.

Realizing that the construction of a large knowledgebase requires a friendly user interface, a number of pro-grams have been designed and implemented by studentsin the University of Toronto's Department of ComputerScience that are intended to help a PSN user visualize the

knowledge base he is constructing and to facilitate its con-struction. One program by Hugh Meyers implements anumber of display and editing functions, along with run-time support and version-control facilities. A second setof programs-the combined efforts of Ron Gershon,Yawar Ali, and Michael Jenkin-offers a restrictednatural-language front-end facility for queries on thecontents of the knowledge base. The replies take theform of small amounts of PSN code, simple natural-language statements, or raster graphics color displays foranswers involving knowledge-base structure, such as theIS-A or PART-OF hierarchies, the similarity net, or tem-poral interrelations among classes.

Applications: Alven and CAA

Two large knowledge-based systems built at theUniversity of Toronto use PSN as a knowledge-represen-tation language. The Alven project,7'8 begun in 1976, iscurrently being implemented a second time with a muchexpanded knowledge base and facilities that would makeit suitable for clinical testing. Research on extendingAlven's representation and control structure for applica-tion to electrocardiography began in 1979, resulting in aprototype design and implementation of the CAAsystem.9

The application environment. Because the perfor-mance of the human heart is complex, analysis of the'heart's behavior requires several knowledge sources. Thedomain of cardiology, rich in temporal and causal rela-tionships, is ideal for experimenting with representationand control strategies that manipulate such concepts.Of the many medical tests that can gather information

on cardiac performance, two involve cineradiographyand electrocardiography. In cineradiography, X-ray im-age sequences are obtained of the left ventricle as itpumps in the patient's chest. Analysis of these images isperformed mostly by human observation with little or no

Figure 2. In PSN, metaclasses can have other classes as instances.

October 1983 85

computer assistance, even though the motions of the leftventricle are more complex than the human visual systemcan objectively and consistently analyze.We are studying left ventricles that have had corrective

surgery either coronary bypass or valve prosthesis. Thegoal of the study is to appraise their postoperative func-tion and, therefore, the effectiveness of surgery. Duringcorrective surgery, nine tiny tantalum helixes (markers)are implanted into each left ventricular wall, all roughlyon the same plane. In addition, two clips are attached tothe aorta as points of reference. After surgery, follow-upexamination involves relatively simple cineradiography.Our first application attempt was in this domain,resulting in the design and construction of the Alvensystemi, a system that analyzes the motion of the left ven-tricular wall.The electrocardiogram, or ECG, can be viewed as the

resulting signal of an "antenna" receiving the aggregateof the electrical activity of the heart, so there is no simplecorrespondence between signal features and individualelectrical discharges in the heart. An important categoryof cardiac abnormalities that can, in principle, bedetected from ECGs involves arrhythmias that is, car-diac rhythm disorders. Detection of arrhythmias typical-ly requires ECGs gathered over 24 hours. The Causal Ar-rhythmia Analysis, or CAA, system attempts to applyphysiological knowledge of the heart conduction systemtogether with signal knowledge to the task of recognizingand describing arrhythmias.

The Alven system. Alven is an expert computer-visionsystem for assessing the performance of the humanheart's left ventricle. Input to the system consists of aninmage sequence obtained by cineradiography of thehuman left ventricle; however, the methodology is ap-plicable to other forms of dynamic cardiac imagery. Thegoal of the Alven system is three-fold:

* to experimelnt with and study methodologies forunderstanding visual motion in the context of a richtemllporal domaini;

* to consistently and objectively evaluate the perfor-mance of the leftt ventricle and

* to compile and refine relevant cardiologicknowledge, with the intent of providing both aclinical and a research tool for cardiologists.

Our main concern has been representing and organiz-ing temporal concepts, such as event start and end states,rates and forms of event changes, changes of relation-ships between the subject of the event and other events orobjects; temporal constraints between events; and tem-poral differences between event classes.The knowledge base must also capture some special

features of the knowledge being represented. First, thedata involved often have a "fuzzy" quality, and rangesof acceptable values may overlap with correspondingranges for distinct concepts. Next, the representationmust have a strong descriptive basis with qualitative con-cepts related to quantitative ones, since the system dealsvith real signal data. Finally, the knowledge base mustieiludC concept descriptions at different levels of

abstraction, depending on the level of expertise of theperson entering the knowledge.

Alven events are represented as PSN classes, while thesemantic components of events such as motion subject,volume changes, length changes, velocities, and trajec-tories are represented as slots, their expected character-istics constrained. For each constraint, exceptions in-cluded are raised whenever a matching failure occurs,i.e., when the given constraint is dissatisfied. Moreover,the IS-A and PART-OF relations are used to representdifferent levels of abstraction.Time is handled using an interval-based scheme: each

event has a time interval slot as one of its components. Inturn, the class Time-Interval has three slots, eachrepresenting an event's start time, end time, or duration.Duration is required because, in many cases, the start

Perhaps the most useful representationalfeature for recognition is the similarity link,

which serves as an exception-handlingmechanism.

and end times for an event are unknown, and only theconstraints on the duration are present. Such a schemepermits flexible reference to any point on a time-line ofevents, the representation of temporal constraints on anyof these three time-interval features for a particularevent, and the ability to form relations between suchevents as During, Overlapping, Before, and Simul-taneous.

Perhaps the most useful representational feature forrecognition is the similarity link. These links connect eventclasses by their patterns of temporal differences andsimilarities, serving as an exception-handling mechanism.Besides its source and target classes, each similarity linkcarries two types of information:

* A similarity expression specifies conditions underwhich the source and target classes of a similaritylink are to be treated as similar.

* A difference expression indicates exceptions thatmay be raised while matching the source class to in-put data. Each exception has an associated time-interval specifying when it should occur as inputdata are matched against the source class of thesimilarity link. In effect, the difference expressionshows the time course of the matching failures in thesource-class context, indicating that the context ofthe target class would be more relevant.

Generally, then, similarity links point to alternative classesthat are potentially viable when exceptions are raised.

Motion knowledge base. The basic ideas underlyingthe representation of motion concepts are derived fromBadler,t who has studied a large number of English mo-tion verbs. The system recognizes approximately 80general-motion concepts, including a variety of two-dimensional translations and rotations; changes of area,

COMPUTER86

length, and shape; and compound motions defined assets of simultaneous, overlapping, or sequential mo-tions.

Using this general-motion knowledge base, a domain-specific set of classes has been constructed, with thegeneral motions as components. The domain of left ven-tricular performance has a broad spectrum of temporalconcepts, including

* specific temporal patterns of volume change, longaxis dimension change, or perimeter change,

* motions relative to the center of the left ventricle,such as inward or outward, and the extent of suchmotions,

* motion onset asynchronies, i.e., specific patterns ofstart-time delays, both normal and abnormal, be-tween different objects, and

* motions of one part of the left ventricular wall inrelation to another.

These concepts, plus all the standard terms used withincardiology-such as hypokinesis (abnormally diminish-ed motion), dyskinesis (motion in the wrong direction),or akinesis (no motion, best indicated by no circumfer-ential shortening)-are defined in the knowledge base.Normal left ventricles as well as 10 classes of common ab-normalities are defined, forming a large knowledge baseof approximately 450 classes.To properly compare the sizes of the knowledge base

in expert systems, we must keep in mind the "knowledgegranularity" of each system. We have found that Alvenclasses generally contain more information than repre-sentational units of expert systems using productionsystems as their knowledge-representation language.

Control strategy. Our recognition control structure isbased on the paradigms of (1) competition and coopera-tion among hypotheses and (2) hypothesize and test. Thekey feature is that the control structure is driven by thestructure of the knowledge base. Recognition proceedsfrom the general to the specific along IS-A, from theprimitive to the aggregate along PART-OF, across sets ofmutually exclusive events along the similarity relation,and, finally, forward through time.

Hypotheses are ranked according to certainty factors.A modified relaxation labeling process11 is used to up-date the certainty factors representing the notion of"good temporal continuity" of observed events. Thisprocess is based on conceptual adjacency, whichspecifies which hypotheses are competitors or com-plementary and in what respect. The compatibilities be-tween hypotheses necessary for the relaxation labelingprocess are derived dynamically, depending on whichconceptual adjacencies are present between hypothesesactive during the input image sequence. The best, orhighest ranked, hypotheses are used to derive the expec-tations for the next image. The generation of expecta-tions incorporates constraints from each level of the mo-tion's description. If image characteristics are not foundas predicted, the IS-A relation is used to generalize themotion hypothesis. Such generalization relaxes the con-

straints while widening the image search space in apredictable manner.

The CAA system. Because ECG analysis has receivedconsiderable attention over the years, current systemscan properly interpret up to 80 percent of all ECGs.However, the techniques used to build such systems, in-cluding signal transforms and simple contour analysis,do not seem to apply in the analysis of the remaining 20percent-particularly when rhythm disorders are presentin the signal.The goal of our research in this domain is not only to

provide a system capable of analyzing some of those re-maining cases, especially those involving arrhythmias,but also to make three specific contributions to expertsystem technology, namely

* to represent and use causal knowledge in predictingevents in the signal domain, which are observable,as well as events in the physiological domain, whichare not directly observable but can be inferred byusing causal knowledge;

* to create two knowledge bases, one each for thesignal and physiological domains, and to explore theuse of "projection" as a transduction mechanismbetween the two knowledge bases; and

* to represent statistical information, existing inabundance for many medical domains, in terms ofPSN metaclasses, thus permitting default reasoningfor those classes for which more specific descrip-tions are not available.

CAA has adapted much of the Alven control struc-ture, including its use of IS-A, PART-OF, and similarityrelations in activating hypotheses. Perhaps the most in-teresting CAA feature is the representation of causal in-formation-that is, the description of a flow of influencefrom one distinguishable event to another. CAA causallinks interrelate events in two important ways: first, theyspecify the existential dependency of an effected event onits causative event(s); second, they impose temporal con-straints between causative and effected events. Thus, theeffected events cannot occur without the occurrence ofthe corresponding causative events, with effects tem-porally following their causes. Several types of causallinks are useful for our ECG domain:

* Transfer: the subject of the event normally com-pletes the current event or state and proceeds to thefollowing event.

* Transition: the subject is forced to terminate its cur-rent event and proceed to a new event.

* Initiation: the causative event, due to a given sub-ject, triggers a new event of another subject.

* Interrupt: the causative event, due to a given sub-ject, interrupts and forces the termination of anevent by another subject.

These "one-shot" causal links are based on workdescribed by Rieger and Grinsberg.12

October 1983 87

Etlent predicetion and statistical estimation. The role ofcausal links is to provide local integrityi, coniditions for theevents they relate. Thus, once an event has been iden-tified, the system tises causal links to look ahead or backin time for other causalls, related events arid predict theirprobable ternporal locationis. These events are then coni-fiinied if their observable counlterparts cain be found inthe predicted temporal locationis of the signal.

It a predicted event is not directly obsersable, thesystem provides the event with default slot values. [orexamilple, sshen the stait-time of a nonobsersvable evenit isdeterimiined usinig other observed events, the duration(and thus the enid-tirne) is estinmated by retries,ingstatistical infoi-mation about the eseint ancd then fitting itto the Current context.

Projection het ween knowledge bases. The CAA strati-fied knowledge base includes a morphological knowl-edge base, wshicl describes suLch aspects of ECG wave-foi-mns as their shape and durationi, ancd a physiologicalknowledge base, shich maintainis information about thecardiac conduction systeiim. Conduction events arerelatecl to theii moi-phological counterparts through aprojection mechaniism we have developed.

NVas eform recoginition starts with the detectioni ofpromiinenlt ssaseforms in ECG(, signals, resulting in a start-ing set ot hypotheses. Alven's recognitioni strategy is thenapplied to this set to generate and evaluate additionalMnorphological hypotheses. As these hypotheses aregenerated, the system seeks to establish correspondinigpb sioslogical hypot heses t hrough p,rojection lin1ksielatinig a morphological event class to a collection ofpossible physiological esent classes sith binding condidtioins toi each. Since the oserall arrhythmia recognitioniprocess mnust start sith a rhythm hypothesis, which in-cludes a sequence of beats, the system uses this process toexamiinie each projected class and decide whether theclass mnu.st be inclucded in the curienit global hypothesis asa comiiponient hypothesis. LJsing this process, the systemiirecuLrsiVely hypothesizes consecutive beat events andrates the degree of consistenicy by testing themn against thecorresponding ssasve sequence. Shibaharat3 provides adetailed accounLt of CAA.

We have presented a macroscopic siesw of the PSNpr-oject sshose goal is to develop and test linguistic anidother- tools that facilitate the construction of largeknowledge bases. Obviously, IImUCh remiiains to be cloniebefore thi.s goal is achieved.

[hle tulldamental idea that underlies this knowledge-r-epresenltation project is that organizational issues arecriticalls important in constructing andi using largekinowledge bases. We believe that our careful treatmentoft organizational principles anid their application inorFanizinrg differenit kindis of knowledge and in de-relopinp more effective anid natural recognitionaleorithm.s contributes to klnowledge-prpresentation andcxpert-ssstems researclh. U

Acknowledgments

A number of colleagues have made important contribu-tions to this project. Hector Levesque, now with the Fair-child Al laboratory, has been a drising force for theToronto know ledge-representation group as well as a prin-cipal designer of PSN. Dominic Covvey, with his medicalexpertise and contacts, contributed greatly in the develop-ment of Alven and CAA. Gordon McCalla-nos at theUniversity of Saskatchewan, Saskatoon anid Lou Velliwere part of the early phases of the project. Peter Patel-Schneider, Bryan Kramer, anid Yves Lesperanice helpecdin the design of PSN, while Andirew Gullen is responisiblefor the current implementation

This research has been supported by the Canadian andOntario Heart Foundations, by the Natural Sciences andEngineering Research Council of Canada, and by a con-tract sponsored by the Department of Supply and Ser-vices of Canada and administered by the DefenseResearch Establishlment Atlantic.

References

I .J. Nllopouols anld H. [ cvesque, "'An Osersies ofKnow ledge Represcietation,'' Otn ConceptuiaModelling,XM. Broclie, J. yllopoulos, anid J. Schmllidlt, eds., SpringerNeVrla, Ness Xork, 1983.

2. MI. Minsks, ''A Frainework lor- Represertinu Knoswledge,''Tire PscY!choloog ol Comtpiter Visioti, P. Winston, ed.,McGraw-Hill, Nes York, 1975.

3. IH. L es esque, ''"A lroceduiral Approaclh To Semiianitic Net-s orks,'' masters thesis, t)ept of C0om1puiter Scienice,Unis ersits of Toronto, 1977.

4. H. Lesesque and J. \lylopoulos, "A Pr-ocedural Semiianr-tics ftor Semantic Netssorks,'' Assoc'latiue Networks, N.IFindler, ed., Acadernic Press, Nes York, 1979.

5. P. Patel-Schleicler et al., "I'SN: An Extenisible Knowl-edge Representationi Schemiie," Proc. Canadian Soc. Cootti-oiaational Studlie.s Intellig'ence Conf., 1982.

6. WX'. A. XX oods, ''XN bat's in a Link: I oundationis forSemllanltic Netiorks," Representution at(d Utrderstandclingt.:Slidle.s in Cognittue Science, D. Bobrow arid A. C'ollins,eds., Academic Press, Nes York, 1975.

7. 1. Tsotsos, "A Framesork tor Visual NMotioi Unuder-standing,'' PhiD thesis, D)ept ot C omputer Scienlce,Un.irersity ot Toronto, 1980.

8. J. K. Tsotsos, ''Temipor-al Event Recognition: Art Ap-plicationi to t ett V'sentricular Performance Esaluation,''Proc. Iot 'I Joint C ottl. 1 rtificill Intelligencce, 1 98 1.

9. 1. Slibahara et al., "CAA: A Knosledge-Based Systemiiwith Causal Knosledge to t)iagnose Rhythtmi Disorder-s iIIthe Heart,'" Proc. Cana(lian Soc. Cootiplotational StuidesIntelligetnc'e Contf., 1982.

1t). N. B3adler, ''Termporal Scenre Analy sis: ConceCptLualDescriptions ot Object Mosernents," PhD thesis, Dept. ot(ompuIter Scienice, Unisersity of Toronito, 1975.

COMPUTER88

11. S. Zucker, "Production Systems with Feedback,"Pattern-Directed Inference Systems, D. Waterman and S.Hayes-Roth, eds., Academic Press, New York, 1978.

12. C. Rieger and M. Grinsberg, "The Declarative Represen-tation and Procedural Simulation of Causality in PhysicalMechanics," Proc. Int'l Joint Conf. Artificial In-telligence, 1977.

13. T. Shibahara, "CAA: Computer Diagnosis of CardiacRhythm Disorders from ECGs," PhD thesis, Dept. ofComputer Science, University of Toronto (forthcoming).

John Mylopoulos has been with the01:j_sIS0;e00 Department of Computer Science at the

t--: University of Toronto in Toronto, On-0 _110 tario, Canada, since 1970. His research

-_ > , interests include knowledge representa-U tion and the design of knowledge-based

systems.Mylopoulos completed his undergrad-

uate studies at Brown University in 1966and his graduate studies at Princeton

University in 1970, both in electrical engineering.

Tetsutaro Shibabara is working toward aPhD in computer science at the Universityof Toronto. He was previously an assis-tant professor in the Information ScienceLaboratory for Biomedicine at KyushuUniversity Hospital, Fukuoka, Japan,where he contributed to the installation ofone of the largest medical data processingsystems in Japan. His special interest is inbuilding intelligent knowledge-based sys-

tems that emulate highly technical human activities, such asthose in medical diagnoses, signal/pattern recognition andanalysis, and computer-aided design and control.

Shibahara received an undergraduate degree in electricalengineering from Yokohama National University in 1967, andan MS in electrical engineering from the University of Illinois in1971. He is a member of the Information Processing Society ofJapan and a student member of the ACM and the IEEE Com-puter Society.

John K. Tsotsos is an assistant professor> of computer science at the University of

Toronto and Canadian Heart Foundation- Research Fellow at Toronto General

_ Hospital. His research interests includeccomputer vision, knowledge-based sys-tems, and application of artificial in-ndelligence to biomedical image analysisand diagnosis.From the University of Toronto, Tso-

tsos received a BASc in engineering science in 1974, and an MScand PhD in computer science in 1976 and 1980. He is a memberof the Canadian Society for Computational Studies of In-telligence, the ACM, and the IEEE-CS.

The authors' address is Dept. of Computer Science, Universi-ty of Toronto, Ontario, Canada.

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