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What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems

Published online by Cambridge University Press:  07 July 2009

E. T. Keravnou  and
J. Washbrook
E. T. Keravnou
Affiliation:
Department of Computer Science, University College, London, Gower Street, London WC1E 6BT, UK
J. Washbrook
Affiliation:
Department of Computer Science, University College, London, Gower Street, London WC1E 6BT, UK
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Abstract

First-generation expert systems have significant limitations, often attributed to their not being sufficiently deep. However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

Type
Research Article
Information
The Knowledge Engineering Review , Volume 4 , Issue 3 , September 1989 , pp. 205 - 233
Copyright
Copyright © Cambridge University Press 1989

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References

Aikins, J, 1983. “Prototypical knowledge for expert systems” Artificial Intelligence 20 163–210.CrossRefGoogle Scholar
Allen, JF, 1983. “Maintaining knowledge about temporal intervals” Communications of the ACM 26 832–843.CrossRefGoogle Scholar
Allen, JF, 1984. “Towards a general theory of action and time” Artificial Intelligence 23 123–154.CrossRefGoogle Scholar
Bottacci, L, 1985. Modifiability of rule-based expert systems, PhD Thesis, Division of Cybernetics, Brunel University, UK.Google Scholar
Brachman, RJ and Levesque, HJ, 1986. “What makes a knowledge base knowledgeable? A view of databases from the knowledge level” In: Kerschberg, L, ed, 1986. Expert Database Sytems: Proc. 1st International Workshop, pp. 69–78. London: Benjamin-Cummings.Google Scholar
Breuker, J and Wielinga, B, 1985. “KADS: Structural knowledge acquisition for expert systems” Proc. 5th International Workshop in Expert Systems and their Applications, Avignon, 887–900.Google Scholar
Breuker, JA and Wielinga, BJ, 1987a. “Use of models in the interpretation of verbal data” In: Kidd, AL, ed Knowledge acquisition for expert systems: a practical handbook, New York, NY: Plenum, pp 14–44.Google Scholar
Breuker, JA and Wielinga, BJ, 1987b. “Knowledge acquisition as modeling expertise: the KADS methodology” Proceedings of the 1st European Workshop on Knowledge Acquisition for Knowledge-Based Systems, Reading University.Google Scholar
Bylander, T and Mittal, S, 1986. “CSRL: a language for classification problem solving and uncertainty handling” The AI Magazine 08 1986, 66–76.Google Scholar
Chandrasekaran, B, 1986. “Generic tasks in knowledge-based reasoning: high level building blocks for expert system design” IEEE Expert Fall 1986, 23–30.CrossRefGoogle Scholar
Chandrasekaran, B, 1987. “Towards a functional architecture for intelligence based on generic information processing tasks” Proc. IJCA187, pp 1183–1192.Google Scholar
Chandrasekaran, B, 1988. “Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples” The Knowledge Engineering Review 3(3) 183–210.CrossRefGoogle Scholar
Chandrasekaran, B, 1989. “Task structures, knowledge acquisition and learning”, to appear in Int. J. of Machine Learning.CrossRefGoogle Scholar
Chandrasekaran, B and Mittal, S, 1982. “Deep versus compiled knowledge approaches to diagnostic problem solving” Proc. AAAl-82 349–354.Google Scholar
Chandrasekaran, B and Mittal, S, 1983. “Conceptual representation of medical knowledge for diagnosis by computer: MDX and related systems” Advances in Computers 22 217–293.CrossRefGoogle Scholar
Chandrasekeran, B, Gomez, F, Mittal, S and Smith, J, 1979. “An approach to medical diagnosis based on conceptual structures” Proc. IJCAI-79 134–142.Google Scholar
Chandrasekaran, B, Smith, JW and Sticklen, J, 1989. “Deep models and their relation to diagnosis” Int. J. of Artificial Intelligence in Medicine 1 29–40.Google Scholar
Clancey, WJ, 1979a. “Dialogue management for rule-based tutorials” Proc. IJCAI-79 155–161.Google Scholar
Clancey, WJ, 1979b. “Tutoring rules for guiding a case method dialogue” Int. J. Man-Machine Studies 1 25–49.CrossRefGoogle Scholar
Clancey, WJ, 1983. “Methodology for building an intelligent tutoring system” In: Kintsch, Polson and Miller, (eds), Methods and Tactics in Cognitive Science, Lawrence Erlbaum Publishers.Google Scholar
Clancey, WJ, 1983b. “The advantages of abstract control knowledge in expert system design” Proc. AAAI-83 74–78.Google Scholar
Clancey, WJ, 1983. “The epistemology of a rule-based expert system: a framework for explanation” Artificial Intelligence 20 215–251.CrossRefGoogle Scholar
Clancey, WJ, 1985. “Heuristic classification” Artificial Intelligence 27 289–350.CrossRefGoogle Scholar
Clancey, WJ, 1986. “From Guidon to Neomycin to Heracles in twenty short lessons: ORN final 1979–1985” The AI Magazine 08 1986, 40–60.Google Scholar
Clancey, WJ and Letsinger, R, 1981. “Neomycin: Reconfiguring a rule-based expert system for application to teaching” Proc. IJCAI-81, pp 829–836.Google Scholar
Corlett, R, Davies, N, Khan, R, Reichgelt, H and van Harmelen, F, 1989. “The architecture of Socrates” In: Jackson, P, Reichgelt, H and van Harmelen, F, Logic-based knowledge representation, Massachutts: The MIT Press, pp 37–64.Google Scholar
Davis, R, 1983. “Expert systems: where are we and where are we going?”, The AI Magazine Winter 1988.Google Scholar
Davis, R, 1983b. “Reasoning from first principles in electronic troubleshooting” Int. J. Man-Machine Studies 19, 403–423.CrossRefGoogle Scholar
Davis, R, 1983c. “Diagnosis via causal reasoning: paths of interaction and the locality principle” Proc. AAAI-83 88–94.Google Scholar
Davis, R and King, J, 1977. “An overview of production systems” Machine Intelligence 8 300–332.Google Scholar
de Dombal, FT, 1988. “Computer-aided diagnosis of acute abdominal pain: the British experience” In: Dowie, J and Elstein, A (eds.), Professional judgement: A reader in clinical decision making Cambridge: Cambridge University Press, pp 190–199.Google Scholar
de Kleer, J, 1986. “An assumption-based TMS” Artificial Intelligence 28 127–224.CrossRefGoogle Scholar
Dean, TL and McDermott, DV, 1987. “Temporal data base mangement” Artificial Intelligence 32 1–55.CrossRefGoogle Scholar
Dhar, V and Pople, HE, 1987. “Rule-based versus structure-based models for explaining and generating expert behaviour” Communiations of the ACM 30 542–555.CrossRefGoogle Scholar
Dowie, J. and Elstein, A, eds., 1988. Professional Judgment: A reader in clinical decision making Cambridge: Cambridge University Press.Google Scholar
Doyle, JA, 1979. “A truth maintenance system” Artificial Intelligence 12 231–272.CrossRefGoogle Scholar
Elstein, LD, Shulman, LA and Sprafka, SA, 1978. Medical problem solving: An analysis of clinical reasoning Massachusetts: Harvard University Press.CrossRefGoogle Scholar
Feltovich, PJ, Johnson, PE, Moller, JH and Swanson, DB (1984). “LCS: the role and development of medical knowledge in diagnostic expertise” In Clancey, WJ and Shortliffe, EH (eds.), Readings in Medical Artificial Intelligence: The first decade, New York: Addision-Wesley, pp 275–319.Google Scholar
Fink, PK, Lusth, JC and Duran, JW, 1985. “A general expert system design for diagnostic problem solving” IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-7 553–559.CrossRefGoogle Scholar
Fox, J, 1984. “Formal and knowledge-based methods in decision technology” Acta Psychologica 56 303–31; and In: Kerschberg, L, ed, 1986. Expert Database Systems: Proc. 1st International Workshop, pp. 226–252. London: Benjamin Cummings.CrossRefGoogle Scholar
Fox, J, 1989. “Symbolic decision procedures for knowledge based systems” to appear in Adeli, (ed.), Handbook of Knowledge Engineering New York: John Wiley.Google Scholar
Fox, J, Glowinski, AJ, O'Neil, M and Clark, DA, 1988. “Decision making as a logical process” Proc. of Expert Systems '88 Cambridge: Cambridge University Press.Google Scholar
Glowinski, A, O'Neil, M and Fox, J, 1989. “Design of a generic information system and its application to Primary Care” AIME 89 pp 221–233.CrossRefGoogle Scholar
Hart, PE, 1982. “Direction for AI in the eighties” SIGART Newsletter No. 79, 01 1982.Google Scholar
Hasling, DW, 1983. “Abstract explanations of strategy in a diagnostic consultation system” Proc. AAAI-83 157–161.Google Scholar
Hasling, DW, Clancey, WJ and Rennels, G, 1984. “Strategic explanations for a diagnostic consultation system” Int. J. Man-Machine Studies 20 3–19.CrossRefGoogle Scholar
Inoue, K, 1988. “Pruning search trees in assumption-based reasoning” Proceedings of the 8th Int. Workshop on Expert Systems & their Applications pp 133–151.Google Scholar
Johnson, L. 1985. “The need for competence models in the design of expert consultant systems” Int. J. Systems Research and Information Science 1 23–36.Google Scholar
Johnson, L and Keravnou, ET, 1986. “Analysing, representing and interpreting expert strategic knowledge” Cybernetics and Systems '86 743–750.CrossRefGoogle Scholar
Johnson, PE, Duran, AS, Hassebrock, F, Moller, J, Prietula, M, Feltovich, PJ and Swanson, DB, 1981. “Expertise and error in diagnostic reasoning” Cognitive Science 5, 235–283.Google Scholar
Johnson, TR, Smith, JW Jr and Chandreskaran, B, 1989. “Generic tasks and SOAR” Laboratory for AI Research, Department of Computer and-Information Sciences, The Ohio State University, Columbus, Ohio 43210.Google Scholar
Kassirer, JP, Kuipers, BJ and Gorry, G A, 1982. “Towards a theory of clinical expertise” The American J. of Medicine 73 251–259.CrossRefGoogle Scholar
Keravnou, ET, 1985. “Building expert systems that model competence: a case study in fault diagnosis” PhD Thesis, Division of Cybernetics, Brunel University, UK.Google Scholar
Keravnou, ET and Johnson, L, 1986. Competent expert systems: A case study in fault diagnosis, London: KoganPage.Google Scholar
Keravnou, ET and Johnson, L, 1987. “Intelligent handling of data by integration of commonsense reasoning” Knowledge-Based Systems Journal 1 32–42.CrossRefGoogle Scholar
Keravnou, ET and Johnson, L, 1988. “Neocrib” In: Keravnou, and Johnson, (eds.), Expert systems architectures London: Kogan-Page, pp 168–182.Google Scholar
Keravnou, ET and Washbrook, J, 1989. “Deep and shallow models in medical expert systems” Int. of Artificial Intelligence in Medicine 1 11–28.Google Scholar
Keravnou, ET, Washbrook, J, Da wood, RM, Hall, CM and Shaw, D, 1989. “A model-based diagnostic expert system for skeletal dysplasias”, AIME 89 pp 47–56.CrossRefGoogle Scholar
Kerschberg, L, ed., 1986. Expert Database Systems, Proc. 1st Int. Workshop, London: Benjamin-Cummings Co.Google Scholar
Klein, D and Finin, T, 1987. “What's in a deep model: a characterization of knowledge depth in intelligent safety systems” Proc. IJCAI-87 559–562.Google Scholar
Kolodner, JL, 1982. “The role of experience in development of expertise” Proc. AAAI-82 273–277.Google Scholar
Kolodner, JL, 1984. “Towards an understanding of the role of experience in the evolution from novice to expert” In: Coombs, MJ (ed.), Developments in expert systems London: Academic Press.Google Scholar
Koton, P, 1988a. “A medical reasoning program that improves with experience” Proceedings of the 12th Annual Symposium on Computer Applications in Medical Care.Google Scholar
Koton, P, 1988b. “Reasoning about evidence in causal explanations” Proc. AAAI-88 256–261, Saint Paul, Minnesota.Google Scholar
Kowalski, R and Sergot, M, 1986. “A logic-based calculus of events” New Generation Computing 4 67–95.CrossRefGoogle Scholar
Kuipers, B, 1984. “Commonsense reasoning about causality: deriving behaviour from structure” Artificial Intelligence 24 169–203.CrossRefGoogle Scholar
Kuipers, B, 1986. “Qualitative Simulation” Artificial Intelligence 29 289–338.CrossRefGoogle Scholar
Kuipers, B, 1987. “Qualitative simulation as causal explanation” IEEE Transactions on Systems, Man, and Cybernetics SMC-17 432–444.Google Scholar
Laird, JE, Newell, A and Rosenbloom, PS, 1987. “SOAR: an architecture for general intelligence” Artificial Intelligence 33 1–64.CrossRefGoogle Scholar
Lauritzen, SL and Spiegelhalter, DJ, 1988. “Local computations with probabilities on graphical structures and their application to expert systems” J. R. Statist. Soc. 50.Google Scholar
Mitchie, D, 1982. “High-road and low-road programs” AI Magazine 3 21–22.Google Scholar
Miller, RA, Pople, HE and Myers, JD, 1982. “Internist-I: an experimental computer-based diagnostic consultant in general internal medicine” New England J. of Medicine 307 468–476.CrossRefGoogle ScholarPubMed
Mittal, S. and Chandrasekaran, B, 1980. “Conceptual representation of patient databases” J. Med. Syst. 4 169–185.CrossRefGoogle Scholar
Mittal, S, Chandrasekaran, B and Sticklen, J, 1984. “Patrec: a knowledge-directed database for a diagnostic expert system” IEEE computer, 09 1984, 51–58.CrossRefGoogle Scholar
Murdoch, STR, 1987. “A review of the intersection of expert systems and database systems: Expert/Database systems” Int. J. Systems Research and Information Science 4 111–119.Google Scholar
Murdoch, STR and Johnson, L, 1987. Intelligent data handling by active databases London: Kogan-Page.Google Scholar
O'Neil, M, Glowinski, A and Fox, J, 1989. “A symbolic theory of decision-making applied to several medical tasks” AIME 89 pp 62–71.CrossRefGoogle Scholar
Patil, RS, 1981. “Causal representation of patient illness for electrolyte and acid-base diagnosis” MIT/LCS/TR-267.Google Scholar
Patil, RS, Szolovits, P and Schwartz, WB, 1981. “Causal understanding of patient illness in medical diagnosis” Proc. IJCAI-81 893–899.Google Scholar
Patin, RS, Szolovits, P and Schwartz, WB, 1982a. “Information acquisition in diagnosis” Proc. AAAI-82 345–348.Google Scholar
Patil, RS, Szolovits, P and Schwartz, WB, 1982b. “Modelling knowledge of the patient in acid-base and electrolyte disorders” In: Szolovits, P (ed.), Artificial intelligence in medicine, AAAS Selected Symposium Series, Boulder, Co: West View Press, pp 191–226.Google Scholar
Pople, HE, 1982. “Heuristic methods for imposing structure on ill-structured problems: the structuring of medical diagnosis” In: Szolovits, P (ed.), Artificial Intelligence in Medicine, AAAS Selected Symposium Series, Boulder, CO: West View Press, pp 119–185.Google Scholar
Price, CJ and Lee, M, 1988. “Deep knowledge tutorial and bibliography” Alvey Report IKBS3/26/048.CrossRefGoogle Scholar
Riesbeck, CK, 1984. “Knowledge reorganization and reasoning style” In: Coombs, MJ (ed.), Developments in expert systems London: Academic Press, pp 159–176.Google Scholar
Ross, SP, 1989. “Case-based reasoning: position paper”, submitted to Workshop on Medical AI: current issues, in medical decision support, 2nd Scandinavian Conference on Artificial Intelligence,Tampere, Finland.Google Scholar
Schreiber, G, Breuker, J, Bredeweg, B and Wielinga, B, 1988. “Modelling in KBS development” Proc. 8th Int. Workshop on Expert Systems & their Applications, pp. 283–296.Google Scholar
Schwartz, S and Griffin, T, 1986. Medical Thinking: the psychology of medical judgment and decision making, Berlin: Springer-Verlag.CrossRefGoogle Scholar
Shoham, Y, 1987. “Temporal logics in AI: semantical and ontological considerations” Artificial Intelligence 33 89–104.CrossRefGoogle Scholar
Shortliffe, EH, 1976. Computer-based medical consultations: Mycin, New York: Elsevier.Google Scholar
Smith, B and Kelleher, G, ed., 1988. Reason maintenance systems and their applications, Chichester: Ellis Horwood.Google Scholar
Steels, L, 1986. “Second-generation expert systems” In: Research and development in expert systems III, Bramer, MA (ed.), Cambridge: Cambridge University Press.Google Scholar
Steels, L., 1987. “The deepening of expert systems” AI Memo 87–16, Artificial Intelligence Laboratory, Vrije Universiteit Brussel.Google Scholar
Steels, L, 1988. “Components of expertise” AI Memo 88–16, Artificial Intelligence Laboratory, Vrije Universiteit Brussel.Google Scholar
Sticklen, J, 1983. “Manual for IDABLE”, Technical Report, Laboratory for Artificial Intelligence Research, Department of Computer and Information Science, The Ohio State University, Columbus.Google Scholar
Sticklen, J, 1987. “MDX2: An integrated diagnostic system”, PhD Dissertation. Department of Computer & Information Science, The Ohio State University.Google Scholar
Soloway, E, Bachant, J and Jensen, K, 1987. “Assessing the maintainability of XCON-in-RIME: coping with the problems of a VERY large rule-base” Proc. AAAI-87 824–829.Google Scholar
Swanson, DB, Feltovich, PJ and Johnson, PE, 1977. “Psychological analysis of physician expertise: implications for design of decision support systems” MEDINF077 161–164.Google Scholar
Swartout, WR, 1981. “Producing explanations and justifications of expert consulting programs” MIT Laboratory for Computer Science, Technical Report MIT/LCS/TR-251.Google Scholar
Swartout, WR, 1983. “XPLAIN: a system for creating and explaining expert consulting programs” Artificial Intelligence 21, 285–325.CrossRefGoogle Scholar
Swartout, WR, 1985. “Knowledge needed for expert systems explanation” AFIPS Conference Proceedings 54 95–98.Google Scholar
Torasso, P and Console, L, 1989. Diagnostic problem solving: Combining heuristic, approximate and causal reasoning, North Oxford Academic.Google Scholar
van de Brug, A, Bachant, J and McDermott, J, 1986. “The taming of R1” IEEE Expert, Fall 1986, 33–38.CrossRefGoogle Scholar
Van de Velde, W, 1986. “Learning heuristics in second-generation expert systems” Proc. Sixth Int. Workshop on Expert Systems and their Applications, Avignon 1986.Google Scholar
Van Harmelen, F, 1989. “A classification of meta-level architectures” In: Logic-based knowledge representation, Jackson, P, Reichgelt, H and van Harmelen, F (eds.), Massachusetts: MIT Press.Google Scholar
Wallis, JW, and Shortliffe, EH, 1982. “Explanatory power for medical expert systems: studies in the representation of causal relationships for clinical consultations” Meth. Info. Med. 21 127–136.Google ScholarPubMed
Weiss, SM, 1974. “A system for model-based computer-aided diagnosis and therapy” Ph.D Thesis, Computers in Biomedicine, Department of Computer Science, Rutgers University, CBM-TR-27-Thesis.Google Scholar
Weiss, SM, Kulikowski, CA, Amarel, S and Safir, A, 1978. “A model-based method of computer-aided medical decision-making” Artifical Intelligence 11 145–172.CrossRefGoogle Scholar
Whitbeck, C, 1981. “What is diagnosis? a preface to the investigation of clinical reasoning” Metamedicine 2 319–329.CrossRefGoogle Scholar
Wiederhold, G, 1984. “Knowledge and database management” IEEE Software 01 1984, 63–73.CrossRefGoogle Scholar
Wielinga, BJ and Breuker, J, 1986. “Models of expertise” Proc. ECAI-86 306–318.Google Scholar
Wilkins, DC, Buchanan, BG and Clancey, WJ, 1984. “Inferring an expert's reasoning by watching” Heuristic Programming Project Report No HPP-84–29, Department of Computer Science, Stanford University.Google Scholar
Williams, BC, 1986. “Doing time: putting qualitative reasoning on firmer ground” Proc. AAAI-86 105–112.Google Scholar