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RESEARCH ARTICLE   Open Access    

Cognitive emulation in expert system design

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  • Summary: Cognitive emulation is an expert system design strategy which attempts to model system performance on human (expert) thinking. Arguments for and against cognitive emulation are reviewed. A major conclusion is that a significant degree of cognitive emulation is an inherent feature of design, but that an unselective application of the strategy is both unrealistic and undesirable. Pragmatic considerations which limit or facilitate the viability of a cognitive emulation approach are discussed. Particular attention is given to the conflict between cognitive emulation and established knowledge engineering objectives, detailed over 12 typical expert system features. The paper suggests circumstances in which a strategy of cognitive emulation is useful.
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  • 1Barr A. and Feigenbaum E.A. (Eds.) (1981) The Handbook of Artificial Intelligence, vol 1. Los Altos, Calif.: William Kaufman.

    Google Scholar

    2Barr A. and Feigenbaum E.A. (Eds.) (1982) The Handbook of Artificial Intelligence, vol 2. Los Altos, Calif.: William Kaufman.

    Google Scholar

    3Boden M.A. (1977) Artificial Intelligence and Natural Man. Brighton: Harvester.

    Google Scholar

    4Minsky M. (1975) A Framework for Representing Knowledge, In Winston P.H. (Ed.), The Psychology of Computer Vision. New York: McGraw Hill.

    Google Scholar

    [5]Newell A. and Simon H.A. (1976) Computer Science as Empirical Enquiry: Symbols and Search. (1975 ACM Turing Lecture) Communications of the ACM, 19 (3) 113–126.

    Google Scholar

    6Popper K.R. (1963) Conjectures and Refutations, London: Routledge.

    Google Scholar

    7Searle J. (1984) Brains, Minds and Science. Reith Lectures, BBC publications.

    Google Scholar

    8Simons G.L. (1983) Towards Fifth-Generation Computers. Manchester: NCC Publications.

    Google Scholar

    9Sloman A. (1984) Why We Need Many Knowledge Representation Formalisam. In Bramer M.A. (Ed.), Research and Development in Expert Systems. Cambridge: Cambridge University Press.

    Google Scholar

    10Weizenbaum J. (1976) Computer Power and Human Reason: From Judgement to Calculation. San Francisco: Freeman.

    Google Scholar

    11Buchanan B.G. (1982) New Research on Expert Systems. In Hayes J.E., Michie D. and Pao Y.H. (Eds.), Machine Intelligence10. Edinburgh: Edinburgh University Press

    Google Scholar

    12Bramer M.A. (1984) Expert Systems: The Vision and the Reality, In Bramer M.A. (Ed.), Research and Development in Expert Systems. Cambridge: Cambridge university Press.

    Google Scholar

    13Breucker J.A. and Weilinga R.A. (1983) Analysis Techniques for Knowledge Based Systems Part 1. Report 1.1 Esprit Project 12 (Memorandum 10 of the Research Project “The Acquisition of Expertise”).

    Google Scholar

    [14]Davies R. (1982) Expert Systems: Where are we? And where do we go from here?The AI Magazine, 3, 3–22.

    Google Scholar

    15Davis R. and King J. (1977) An Overview of Production Systems. In Elcock E. and Michie D. (Eds.), Machine Intelligence, vol 8. New York: Wiley.

    Google Scholar

    [16]Duda R.O. and Shortliffe E.H. (1983) Expert Systems Research, Science, 220, 261–268.

    Google Scholar

    17Fox J. (1982) Expertise in Man and Machine. Paper given at the Colloquium on “Applications of Knowledge-based (or Expert) Systems”. Institute of Electrical Engineers, Savoy Place, London.

    Google Scholar

    18Gammack J.G. and Young R.M. (1984) Psychological Techniques for Eliciting Knowledge. In Bramer M.A. (Ed.), Research and Development in Expert Systems. Cambridge: Cambridge University Press.

    Google Scholar

    19Gaschig J., Klahr P., Pople H., Shortliffe E. and Terry A. (1983) Evaluation of Expert Systems; Issues and Case studies. In Hayes-Roth F., F., Waterman D.A., Lenat D.B. (Eds.), Building Expert Systems. Reading, MA: Addison-Wesley.

    Google Scholar

    20Hayes-Roth F. (1984) The Knowledge-based Expert System: A Tutorial, Computer, 091984, 11–28.

    Google Scholar

    21Hayes-Roth F., Waterman D.A., and Lenat D.B. (Eds.) (1983) Building Expert Systems. Reading, Ma: Addison-Wesley.

    Google Scholar

    [22]Johnson T. (1984) The Commercial Application of Expert System Technology. Knowledge Engineering Review, 1.(1) 15–25.

    Google Scholar

    [23]Leith P. (1983) Heirarchically Structured Production Rules. Computer Journal, 26, 1–5.

    Google Scholar

    [24]Michie D. (1980) Expert Systems. Computer Journal, 23, 369–376.

    Google Scholar

    25Michie D. (1982) The State of the Art in Machine Learning. In Michie D. (Ed.), Introductory Readings in Expert Systems. London: Gordon and Breach.

    Google Scholar

    26Sparck Jones K. (1984) Natural Language Interfaces for Expert System In Bramer M.A. (Ed.), Research and Development in Expert Systems. Cambridge: Cambridge University Press.

    Google Scholar

    [27]Stefik M., Aikins J., Balzer R., Benoit J., Birnbaum L., Hayes-Roth F. and Sacerdoti E. (1982) The Organization of Expert Systems: A Tutorial. Artificial Intelligence, 18, 135–173.

    Google Scholar

    28Welbank M. (1983) A Review of Knowledge Acquisition Techniques for Expert Systems. Martlesham Consultancy Services, British Telecom Research Labs: Martlesham Heath, Ipswich.

    Google Scholar

    29White A.P. (1984) Inference Deficiences in Rule-based Expert Systems. In Bramer M.A. (Ed.), Research and Development in Expert Systems. Cambridge: Cambridge University Press.

    Google Scholar

    30Young R.M. (1985) Human Interface Aspects of Expert Systems, in Fox J. (Ed.), State of the Art Report in Expert Systems. Pergamon Infotech, 1984.

    Google Scholar

    [31]Buchanan B.G. and Feigenbaum E.A. (1978) DENDRAL and Meta-DENDRAL; Their Application Dimension. Artificial Intelligence, 11, 5–24.

    Google Scholar

    [32]Davies R., Buchanan B.G. and Shortliffe E. (1977) Production Rules as a Representation for a Knowledge-based Consultation Program. Artificial Intelligence, 8, 15–45.

    Google Scholar

    33Duda R.O., Gaschnig J.G. and Hart R.E. (1979) Model Design in the PROSPECTOR Consultant System for Mineral Exploration. In Michie D. (Ed.) Expert Systems in the Micro-electronic Age. Edinburgh: Edinburgh Press.

    Google Scholar

    [34]Fox J., Barber D. and Bardhan K.D. (1980) Alternatives to Bayes? A Quantitative Comparison with Rule-based Diagnosis. Methods of Information in Medicine, 19, 210–215.

    Google Scholar

    [35]McDermott J. (1982) A Rule-Based Configurer of Computer Systems. Artificial Intelligence, 19, 39–88.

    Google Scholar

    [36]Michalski R.S. and Chilausky R.L. (1980) Knowledge Acquisition by Encoding Expert Rules Versus Computer Induction From Examples: A Case Study Invoving Soybean Pathology. International Journal of Man-Machine Studies, 12, 63–87.

    Google Scholar

    37Anderson J.R. (1976) Language, Memory and Thought. Hillsdale, NJ: Erlbaum.

    Google Scholar

    38Anderson J.R. (1983) The Architecture of Cognition. Cambridge, MA: Harward.

    Google Scholar

    [39]Anderson J.R. (1984) Cognitive Psychology, Artificial Intelligence, 23, 1–11.

    Google Scholar

    40Anderson J.R. and Bower G.H. (1983) Human Associative Memory. Washington, D.C.: Winston.

    Google Scholar

    41Baddeley A.D. (1976) The Psychology of Memory. New York: Harper and Row.

    Google Scholar

    [42]Barslou L.W. and Bower G.H. (1984) Discrimination Nets as Psychological Models, Cognitive Science, 8, 1–26.

    Google Scholar

    43Bartlett F.C. (1932) Remembering: A Study in Experimental and Social Psychology. Cambridge: Cambridge University Press.

    Google Scholar

    [44]Elio R. and Anderson J.R. (1981) Effects of Category Generalizations and Instance Similarity on Schema Abstraction. Journal of Experimental Psychology: Human Learning and Memory, 7, 397–417.

    Google Scholar

    [45]Garner W.R.Interaction of Stimulus Dimensions in Concept and Choice Processes. Cognitive Psychology, 1976, 8, 98–123.

    Google Scholar

    46Hunt E.B., Marin J. and Stone P.J. (1966) Experiments in Induction. New York: Academic Press.

    Google Scholar

    47Jeffries R., Turner A., Polson P., and Atwood M. (1981) The Processes Involved in Designing Software. In Anderson J.R. (Ed.), Cognitive Skills and Their Acquisition. Hillsdale, NJ: Erlbaum.

    Google Scholar

    [48]Johnson P.E., Duran A.S., Hassebrock F., Moller J., Prietula M., Feltovich P.J., and Swanson D.B. (1981) Expertise and Error in Diagnostic Reasoning. Cognitive Science, 5, 235–283.

    Google Scholar

    [49]Johnson-Laird P.N. (1982) Thinking as a Skill. Quarterly Journal of Experimental Psychology. 34A, 1–29.

    Google Scholar

    50Johnson-Laird P.N. and Wason P.C. (Eds.) (1977) Thinking: Readings in Cognitive Science. Cambridge: Cambridge University Press.

    Google Scholar

    51Kahneman D., Slovic P., and Tversky A. (Eds.) (1982) Judgment under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press.

    Google Scholar

    [52]Kuipers B. and Kassirer J.P. (1984) Causal Reasoning in Medicine: Analysis of a Protocol. Cognitive Science, 8, 363.385.

    Google Scholar

    [53]McCelland J.L. and Rumelhart D.E. (1981) An Interactive Model of Contextual Effects in Letter Perception: Pt 1, An Account of Basic Findings. Psychological Review, 88, 375–407.

    Google Scholar

    [54]Miller G. A. (1956) The Magical Number Seven. Plus or Minus Two: Some Limits on Our Capacity to Process Information. Psychological Review, 63, 81–97.

    Google Scholar

    55Neisser U. (1967) Cognitive Psychology. New York: Appleton.

    Google Scholar

    56Newell A. and Simon H.A. (1972) Human Problem solving. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar

    [57]Nisbett R.E. and Wilson T.D. (1977) Telling More Than We Can Know: Verbal Reports on Mental Processes. Psychological Review, 84, 231–259.

    Google Scholar

    58Norman D.A. (1981) Twelve Issues for Cognitive Science. In Norman D.A. (Ed.), Perspectives on Cognitive Science, Hillsdale, NJ: Erlbaum.

    Google Scholar

    [59]Pinker S. (1984) Visual Cognition: An Introduction. Cognition, 18, 1–63.

    Google Scholar

    [60]Pople H. (1977) The Formation of Composite Hypotheses in Diagnostic Problem Solving — An Exercise in Synthetic Reasoning. International Joint Conferences on AI5, 1030–1037.

    Google Scholar

    [61]Posner M.I. and Keele W.W. (1970) Retention of Abstract Ideas. Journal of Experimental Psychology, 83, 304–308.

    Google Scholar

    [62]Reder L.M. and Anderson J.R. (1980) A Partial Resolution of the Paradox of Interference: The Role of Integrating Knowledge. Cognitive Psychology, 12, 447–472.

    Google Scholar

    63Rumelhart D.E. and Norman D.A. (1978) Accretion, Tuning and Restructuring: Three Modes of Learning, In Cotton J.W. and Klatzky R. (Eds.), Semantic Factors in Cognition. Hillsdale, NJ: Erlbaum.

    Google Scholar

    64Rumelhart D.E. and Norman D.A. (1981) Analogical Processes in Learning. In Anderson J.R. (Ed.), Cognitive Skills and Their Acquisition. Hillsdale, NJ: Erlbaum.

    Google Scholar

    [65]Shiffrin R.M. and Schneider W. (1977) Controlled and Automatic Human Information Processing. II Perceptual Learning, Automatic Attending and a General Theory. Psychological Review, 84, 127–190.

    Google Scholar

    66Slack J.M. (1984) Cognitive Science Research. In O'Shea T. and Eisenstadt M. (Eds.), Artificial Intelligence: Tools, Techniques, and Applications. New York: Harper and Row.

    Google Scholar

    [67]Wason P.C. and Evans J. St B.T. (1975) Dual Processes in Reasoning?Cognition, 3, 141–154.

    Google Scholar

    68Young R.M. (1979) Production Systems for Modelling Human Cognition. In Michie D. (Ed.), Expert Systems in The Micro-electronic Age. Edinburgh: Edinburgh University Press.

    Google Scholar

    [69]Young R.M. and O'Shea T. (1981) Errors in Children's Subtraction. Cognitive Science, 5, 153–177.

    Google Scholar

    [70]Adelson B. (1984) When Novices Surpass Experts: The Difficulty of a Task May Increase With Expertise. Journal of Experimental Psychology: Learning, memory and Cognition, 10, 483–495.

    Google Scholar

    71Anderson J.R. (1983) Acquisition of Proof skills in Geometry. In Carbonell J.G., Michalski R. and Mitchell T. (Eds.), Machine Learnig, An Artificial Intelligence Approach, San Francisco: Tioga.

    Google Scholar

    [72]Anderson J.R., Farrell R. and Sauers R. (1984) Learning to Program in Lisp. Cognitive Science, 8, 87–129.

    Google Scholar

    [73]Arkes H.R. and Freedman M.R. (1984) A Demonstration of the Cost and Benefits of Memory. Memory and Cognition, 12, 84–89.

    Google Scholar

    [74]Berry D.C. and Broadbent D.E. (1984) On the Relationship between Task Performance and Associated Verbalizable Knowledge. Quarterly Journal of Experimental Psychology, 36A, 209–231.

    Google Scholar

    75Chase W.O. and Ericsson K.A. (1982) Skill and Working Memory. In Bower G.H. (Ed.), The Psychology of Learning and Motivation, vol 16. New York: Academic Press.

    Google Scholar

    [76]Chase W.G. and Simon H.A. (1973) Perception in Chess. Cognitive Psychology, 4, 55–81.

    Google Scholar

    [77]Chi M.T.H., Feltovich P.J. and Glaser R. (1981) Categorization and Representation of Physics problems by Experts and Novices. Cognitive Science, 5, 121–152.

    Google Scholar

    78Eisenstadt M. and Kareev Y. (1975) Aspects of Human Problem Solving: The Use of Internal Representations. In Norman D.A. and Rumelhart D.E. (Eds.), Explorations of Cognition. San Francisco: Freeman.

    Google Scholar

    [79]Fox J. (1980) Making Decisions Under the Influence of Memory. Psychological Review, 87, 190–211.

    Google Scholar

    80Larkin J.H. (1981) Enriching Formal Knowledge: A Model for Learning to Solve Textbook Problems. In Anderson J.R. (Ed.), Cognitive Skills and Their Acquisition. Hillsdale, NJ: Erlbaum.

    Google Scholar

    [81]Larkin J.H., McDermott J., Simon D.P. and Simon H.A. (1980) Expert and Novice Performance in Solving Physics Problems. Science, 208, 1335–1242.

    Google Scholar

    82Lewis C. (1981) Skills in Algebra, In Anderson J.R. (Ed.), Cognitive Skills and Their Acquisition. Hillsdale, NJ: Erlbaum.

    Google Scholar

    [83]Medin D.L., Altom M.W., Edelson S.M. and Freko D. (1982) Correlated Symptoms and Simulated Medical Classification. Journal of Experimental Psychology: Learning, Memory and Cognition, 8, 37–50.

    Google Scholar

    [84]Murphy G.L. and Wright J.C. (1984) Changes in Conceptual Structure with Expertise: Differences Between Real-World Experts and Novices. Journal of Experimental Psychology: Learning, Memory and Cognition, 10, 144–155.

    Google Scholar

    [85]Simon H.A. and Gilmartin K. (1973) A Simulation of Memory for Chess Positions. Cognitive Psychology, 5, 29–46.

    Google Scholar

    [86]Smith E.E., Adams N. and Schorr D. (1978) Fact Retrieval and the Paradox of Interference. Cognitive Psychology, 10, 438.464.

    Google Scholar

  • Cite this article

    Philip E. Slatter. 1984. Cognitive emulation in expert system design. The Knowledge Engineering Review. 1:3 doi: 10.1017/S0269888900000503
    Philip E. Slatter. 1984. Cognitive emulation in expert system design. The Knowledge Engineering Review. 1:3 doi: 10.1017/S0269888900000503

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RESEARCH ARTICLE   Open Access    

Cognitive emulation in expert system design

The Knowledge Engineering Review  1 Article number: 10.1017/S0269888900000503  (1984)  |  Cite this article

Abstract: Summary: Cognitive emulation is an expert system design strategy which attempts to model system performance on human (expert) thinking. Arguments for and against cognitive emulation are reviewed. A major conclusion is that a significant degree of cognitive emulation is an inherent feature of design, but that an unselective application of the strategy is both unrealistic and undesirable. Pragmatic considerations which limit or facilitate the viability of a cognitive emulation approach are discussed. Particular attention is given to the conflict between cognitive emulation and established knowledge engineering objectives, detailed over 12 typical expert system features. The paper suggests circumstances in which a strategy of cognitive emulation is useful.

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    Cite this article
    Philip E. Slatter. 1984. Cognitive emulation in expert system design. The Knowledge Engineering Review. 1:3 doi: 10.1017/S0269888900000503
    Philip E. Slatter. 1984. Cognitive emulation in expert system design. The Knowledge Engineering Review. 1:3 doi: 10.1017/S0269888900000503
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