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

E. T. Keravnou; J. Washbrook; E. T. Keravnou; J. Washbrook

doi:10.1017/S0269888900005038

1989 Volume 4

Article Contents

Next Previous

RESEARCH ARTICLE Open Access

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

More Information

Published online: 07 July 2009
The Knowledge Engineering Review 4, Article number: 10.1017/S0269888900005038 (1989) | Cite this article

Abstract

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.
Rights and permissions
Copyright © Cambridge University Press 19891989Cambridge University Press

References

Aikins J, 1983. “Prototypical knowledge for expert systems” Artificial Intelligence20163–210.

Google Scholar

Allen JF, 1983. “Maintaining knowledge about temporal intervals” Communications of the ACM26832–843.

Google Scholar

Allen JF, 1984. “Towards a general theory of action and time” Artificial Intelligence23123–154.

Google 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 Magazine081986, 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.

Google 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 Review3(3) 183–210.

Google Scholar

Chandrasekaran B, 1989. “Task structures, knowledge acquisition and learning”, to appear in Int. J. of Machine Learning.

Google Scholar

Chandrasekaran B and Mittal S, 1982. “Deep versus compiled knowledge approaches to diagnostic problem solving” Proc. AAAl-82349–354.

Google Scholar

Chandrasekaran B and Mittal S, 1983. “Conceptual representation of medical knowledge for diagnosis by computer: MDX and related systems” Advances in Computers22217–293.

Google Scholar

Chandrasekeran B, Gomez F, Mittal S and Smith J, 1979. “An approach to medical diagnosis based on conceptual structures” Proc. IJCAI-79134–142.

Google Scholar

Chandrasekaran B, Smith JW and Sticklen J, 1989. “Deep models and their relation to diagnosis” Int. J. of Artificial Intelligence in Medicine129–40.

Google Scholar

Clancey WJ, 1979a. “Dialogue management for rule-based tutorials” Proc. IJCAI-79155–161.

Google Scholar

Clancey WJ, 1979b. “Tutoring rules for guiding a case method dialogue” Int. J. Man-Machine Studies125–49.

Google 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-8374–78.

Google Scholar

Clancey WJ, 1983. “The epistemology of a rule-based expert system: a framework for explanation” Artificial Intelligence20215–251.

Google Scholar

Clancey WJ, 1985. “Heuristic classification” Artificial Intelligence27289–350.

Google Scholar

Clancey WJ, 1986. “From Guidon to Neomycin to Heracles in twenty short lessons: ORN final 1979–1985” The AI Magazine081986, 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 Studies19, 403–423.

Google Scholar

Davis R, 1983c. “Diagnosis via causal reasoning: paths of interaction and the locality principle” Proc. AAAI-8388–94.

Google Scholar

Davis R and King J, 1977. “An overview of production systems” Machine Intelligence8300–332.

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 makingCambridge: Cambridge University Press, pp 190–199.

Google Scholar

de Kleer J, 1986. “An assumption-based TMS” Artificial Intelligence28127–224.

Google Scholar

Dean TL and McDermott DV, 1987. “Temporal data base mangement” Artificial Intelligence321–55.

Google Scholar

Dhar V and Pople HE, 1987. “Rule-based versus structure-based models for explaining and generating expert behaviour” Communiations of the ACM30542–555.

Google Scholar

Dowie J. and Elstein A, eds., 1988. Professional Judgment: A reader in clinical decision makingCambridge: Cambridge University Press.

Google Scholar

Doyle JA, 1979. “A truth maintenance system” Artificial Intelligence12231–272.

Google Scholar

Elstein LD, Shulman LA and Sprafka SA, 1978. Medical problem solving: An analysis of clinical reasoningMassachusetts: Harvard University Press.

Google 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 IntelligencePAMI-7553–559.

Google Scholar

Fox J, 1984. “Formal and knowledge-based methods in decision technology” Acta Psychologica56303–31; and In: Kerschberg, L, ed, 1986. Expert Database Systems: Proc. 1st International Workshop, pp. 226–252. London: Benjamin Cummings.

Google Scholar

Fox J, 1989. “Symbolic decision procedures for knowledge based systems” to appear in Adeli (ed.), Handbook of Knowledge EngineeringNew 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 '88Cambridge: 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.

Google Scholar

Hart PE, 1982. “Direction for AI in the eighties” SIGART Newsletter No. 79, 011982.

Google Scholar

Hasling DW, 1983. “Abstract explanations of strategy in a diagnostic consultation system” Proc. AAAI-83157–161.

Google Scholar

Hasling DW, Clancey WJ and Rennels G, 1984. “Strategic explanations for a diagnostic consultation system” Int. J. Man-Machine Studies203–19.

Google 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 Science123–36.

Google Scholar

Johnson L and Keravnou ET, 1986. “Analysing, representing and interpreting expert strategic knowledge” Cybernetics and Systems '86743–750.

Google Scholar

Johnson PE, Duran AS, Hassebrock F, Moller J, Prietula M, Feltovich PJ and Swanson DB, 1981. “Expertise and error in diagnostic reasoning” Cognitive Science5, 235–283.

Google Scholar

Johnson TR, Smith JWJr 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 Medicine73251–259.

Google 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 Journal132–42.

Google Scholar

Keravnou ET and Johnson L, 1988. “Neocrib” In: Keravnou and Johnson (eds.), Expert systems architecturesLondon: 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 Medicine111–28.

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.

Google 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-87559–562.

Google Scholar

Kolodner JL, 1982. “The role of experience in development of expertise” Proc. AAAI-82273–277.

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 systemsLondon: 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-88256–261, Saint Paul, Minnesota.

Google Scholar

Kowalski R and Sergot M, 1986. “A logic-based calculus of events” New Generation Computing467–95.

Google Scholar

Kuipers B, 1984. “Commonsense reasoning about causality: deriving behaviour from structure” Artificial Intelligence24169–203.

Google Scholar

Kuipers B, 1986. “Qualitative Simulation” Artificial Intelligence29289–338.

Google Scholar

Kuipers B, 1987. “Qualitative simulation as causal explanation” IEEE Transactions on Systems, Man, and CyberneticsSMC-17432–444.

Google Scholar

Laird JE, Newell A and Rosenbloom PS, 1987. “SOAR: an architecture for general intelligence” Artificial Intelligence331–64.

Google 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 Magazine321–22.

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 Medicine307468–476.

Google Scholar

Mittal S. and Chandrasekaran B, 1980. “Conceptual representation of patient databases” J. Med. Syst.4169–185.

Google Scholar

Mittal S, Chandrasekaran B and Sticklen J, 1984. “Patrec: a knowledge-directed database for a diagnostic expert system” IEEE computer, 091984, 51–58.

Google Scholar

Murdoch STR, 1987. “A review of the intersection of expert systems and database systems: Expert/Database systems” Int. J. Systems Research and Information Science4111–119.

Google Scholar

Murdoch STR and Johnson L, 1987. Intelligent data handling by active databasesLondon: 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.

Google 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-81893–899.

Google Scholar

Patin RS, Szolovits P and Schwartz WB, 1982a. “Information acquisition in diagnosis” Proc. AAAI-82345–348.

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.

Google Scholar

Riesbeck CK, 1984. “Knowledge reorganization and reasoning style” In: Coombs MJ (ed.), Developments in expert systemsLondon: 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.

Google Scholar

Shoham Y, 1987. “Temporal logics in AI: semantical and ontological considerations” Artificial Intelligence3389–104.

Google 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-87824–829.

Google Scholar

Swanson DB, Feltovich PJ and Johnson PE, 1977. “Psychological analysis of physician expertise: implications for design of decision support systems” MEDINF077161–164.

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 Intelligence21, 285–325.

Google Scholar

Swartout WR, 1985. “Knowledge needed for expert systems explanation” AFIPS Conference Proceedings5495–98.

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.

Google 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.21127–136.

Google Scholar

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 Intelligence11145–172.

Google Scholar

Whitbeck C, 1981. “What is diagnosis? a preface to the investigation of clinical reasoning” Metamedicine2319–329.

Google Scholar

Wiederhold G, 1984. “Knowledge and database management” IEEE Software011984, 63–73.

Google Scholar

Wielinga BJ and Breuker J, 1986. “Models of expertise” Proc. ECAI-86306–318.

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-86105–112.

Google Scholar

About this article

Cite this article

E. T. Keravnou, J. Washbrook. 1989. What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems. The Knowledge Engineering Review. 4:38 doi: 10.1017/S0269888900005038

E. T. Keravnou, J. Washbrook. 1989. What is a deep expert system? An analysis of the architectural requirements of second-generation expert systems. The Knowledge Engineering Review. 4:38 doi: 10.1017/S0269888900005038

Download PDF

Article Metrics

Article views(21) PDF downloads(245)

{{lists.name}}

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

Abstract

Rights and permissions

References

About this article

Cite this article

Article Metrics

Access History

Other Articles By Authors