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6 - Expert Systems: A Perspective from Computer Science

from PART II - OVERVIEW OF APPROACHES TO THE STUDY OF EXPERTISE – BRIEF HISTORICAL ACCOUNTS OF THEORIES AND METHODS

By
Bruce G. Buchanan ,
Randall Davis  and
Edward A. Feigenbaum
Edited by
K. Anders Ericsson ,
Neil Charness ,
Paul J. Feltovich  and
Robert R. Hoffman
Bruce G. Buchanan
Affiliation:
Computer Science Department, University of Pittsburgh
Randall Davis
Affiliation:
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
Edward A. Feigenbaum
Affiliation:
Department of Computer Science, Stanford University
K. Anders Ericsson
Affiliation:
Florida State University
Neil Charness
Affiliation:
Florida State University
Paul J. Feltovich
Affiliation:
University of West Florida
Robert R. Hoffman
Affiliation:
University of West Florida
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Summary

Expert systems are computer programs that exhibit some of the characteristics of expertise in human problem solving, most notably high levels of performance. Several issues are described that are relevant for the study of expertise and that have arisen in the development of the technology. Moreover, because expert systems represent testable models that can be manipulated in laboratory situations, they become a new methodology for experimental research on expertise. The main result from work on expert systems has been demonstrating the power of specialized knowledge for achieving high performance, in contrast with the relatively weak contribution of general problem solving methods.

AI and Expert Systems: Foundational Ideas

A science evolves through language and tools that express its concepts, mechanisms, and issues. The science of studying expertise evolved largely in the second half of the 20th century. It is not accidental that this coincides with the development of the digital stored-program computer, computer programming, artificial intelligence (AI) research, and information-processing models of human cognition (Feltovich, Prietula, & Ericsson, Chapter 4). The language of cognitive information processing was developed by the same AI researchers and cognitive psychologists that had adopted computation as the basis for models of thought (Anderson, 1982; Feigenbaum & Feldman, 1963; Newell & Simon, 1972; VanLehn, 1996).

AI's scientific goal is to understand intelligence by building computer programs that exhibit intelligent behavior and can be viewed as models of thought.

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The Cambridge Handbook of Expertise and Expert Performance , pp. 87 - 104
Publisher: Cambridge University Press
Print publication year: 2006

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References

Aikins, J. S. (1983). Prototypical knowledge for expert systems. Artificial Intelligence, 20, 163–210.CrossRefGoogle Scholar
Ambrosino, R. & Buchanan, B. G. (1999). The use of physician domain knowledge to improve the learning of rule-based models for decision-support. Proceedings of the 1999 American Medical Informatics Association (AMIA).
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.CrossRefGoogle Scholar
Arocha, J. F. & Patel, V. L. (1995). Novice diagnostic reasoning in medicine: Accounting for evidence. The Journal of the Learning Sciences, 4, 355–384.CrossRefGoogle Scholar
Bennett, J. S. (1985). ROGET: A knowledge-based system for acquiring the conceptual structure of a diagnostic expert system. Journal of Automated Reasoning, 1, 49–74.CrossRefGoogle Scholar
Berg, C. A. & Sternberg, R. J. (1992). Adults' conception of intelligence across the adult life span. Psychology and Aging, 7, 221–231.CrossRefGoogle Scholar
Berners-Lee, Hendler, T. J., & Lassila, O. (2001). The semantic web. Scientific American, 284, 34–43.CrossRefGoogle Scholar
Borron, J., Morales, D., & Klahr, P. (1996). Developing and deploying knowledge on a global scale. AI Magazine, 17, 65–76.Google Scholar
Boose, J. H. (1989). A survey of knowledge acquisition techniques and tools. Knowledge Acquisition, 1, 39–58.CrossRefGoogle Scholar
Buchanan, B. G. (1989). Can machine learning offer anything to expert systems? Machine Learning, 4, 251–254.CrossRefGoogle Scholar
Buchanan, B. G. (1994). The role of experimentation in artificial intelligence. Philosophical Transactions of the Royal Society, 349, 153–166.CrossRefGoogle Scholar
Buchanan, B. G. (1995). Verification and validation of knowledge-based systems: A representative bibliography. http://www.quasar.org/21698/tmtek/biblio.html.
Buchanan, B. G. & Shortliffe, E. H. (Eds.) (1984). Rule-based expert systems: The MYCIN experiments of the Stanford heuristic programming project. Reading, MA: Addison-Wesley.Google Scholar
Buchanan, B. G., Smith, D. H., White, W. C., Gritter, R. J., Feigenbaum, E. A., Lederberg, J., & Djerassii, C. (1976). Application of artificial intelligence for chemical inference XXII: Automatic rule formation in Mass Spectrometry by means of the Meta-DENDRAL program. Journal of the American Chemical Society, 98, 61–68.CrossRefGoogle Scholar
Buchanan, B. G. & Wilkins, D. C. (1993). Readings in knowledge acquisition and learning. San Mateo, CA: Morgan Kaufmann.Google Scholar
Chi, M., Glaser, R., & Farr, M. J. (Eds) (1988). The nature of expertise. Hillsdale, NJ: Erlbaum.Google Scholar
Clancey, W. J. (1985). Heuristic classification. Artificial Intelligence, 27, 289–350.CrossRefGoogle Scholar
CLIPS web site. (2004). http://www.ghg.net/clips/CLIPS.html.
Davis, R. (1979). Interactive transfer of expertise: Acquisition of new inference rules. Artificial Intelligence, 12, 121–157.CrossRefGoogle Scholar
Davis, R. (1980). Meta-rules: Reasoning about control. Artificial Intelligence, 15, 179–222.CrossRefGoogle Scholar
Davis, R. (1989a). Expert systems: How far can they go? Part I. AI Magazine, 10, 61–67.Google Scholar
Davis, R. (1989b). Expert systems: How far can they go? Part II. AI Magazine, 10, 65–67.Google Scholar
Davis, R. & King, J. (1984). The origin of rule-based systems in AI. In Buchana, B. G. & Shortliffe, E. H. (Eds.), Rule-based expert systems: The MYCIN experiments of the stanford heuristic programming project reading. MA: Addison-Wesley.Google Scholar
Davis, R., Shrobe, H. E., & Szolovits, P. (1993). What is a knowledge representation? AI Magazine, 14, 17–33.Google Scholar
Deep Blue (2005). Web site http://www.research.ibm.com/deepblue/.
Elstein, A. S., Shulman, L. S., & Sprafka, S. A. (1978). Medial problem solving: An analysis of clinical reasoning. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
Engelmore, R. & Morgan, T. (1988). Blackboard systems. Reading, MA: Addison-Wesley.Google Scholar
Erman, L., Hayes-Roth, F., Lesser, V., & Reddy, D. R. (1980). The Hearsay-II speech-understanding system: Integrating knowledge to resolve uncertainty, ACM Computing Surveys, 12, 213–253.CrossRefGoogle Scholar
Feigenbaum, E. A. & Feldman, J. (1963). Computers and thought. New York: McGraw-Hill.Google Scholar
Feltovich, P. J., Ford, K. M., & Hoffman, R. R. (Eds.) (1997). Expertise in context: Human and machine. Menlo Park, CA and Cambridge, MA: AAAI Press/MIT Press.Google Scholar
Forsythe, D. E., Osheroff, J. A., Buchanan, B. G., & Miller, R. A. (1991). Expanding the concept of medical information: An observational study of physicians' needs. Computers & Biomedical Research, 25, 181–200.CrossRefGoogle Scholar
Forsythe, D. E. & Buchanan, B. G. (1992) Non-technical problems in knowledge engineering: Implications for project management. Expert Systems with Applications, 5, 203–212.CrossRefGoogle Scholar
Goldstein, I. & Papert, S. (1977). Artificial intelligence, language and the study of knowledge. Cognitive Science, 1, 84–123.CrossRefGoogle Scholar
Gordon, J. & Shortliffe, E. H. (1985). A method for managing evidential reasoning in a hierarchical hypothesis space. Artificial Intelligence, 26, 323–357.CrossRefGoogle Scholar
Hammond, T. & Davis, R. (2004). Automatically transforming symbolic shape descriptions for use in sketch recognition. Proceedings of the Nineteenth National Conference on Artificial Intelligence, USA, 450–456.Google Scholar
Hearn, A. C. (1966). Computation of algebraic properties of elementary particle reactions using a digital computer. Communications of the ACM, 9, 573–577.CrossRefGoogle Scholar
Hoffman, R. R., Shadbolt, N. R., Burton, A. M., & Klein, G. (1995). Eliciting knowledge from experts: A methodological analysis. Organizational Behavior and Human Decision Processes, 62, 129–158.CrossRefGoogle Scholar
Kolodner, J. (1993). Case-based reasoning. San Mateo, CA: Morgan Kaufmann.CrossRefGoogle Scholar
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.CrossRefGoogle ScholarPubMed
Leake, D. B. (Ed.) (1996). Case-based reasoning: Experiences, lessons, and future directions. Menlo Park, CA: AAAI Press /MIT Press. Google Scholar
Lenat, D. & Feigenbaum, E. A. (1987). On the thresholds of knowledge. Proceedings of the Tenth International Joint Conference on Artificial Intellingence, Italy, 1173–1182.Google Scholar
Lenat, D. B. & Guha, R. V. (1990). Building large knowledge-based systems: Representation and inference in the Cyc project. Reading, MA: Addison-Wesley.Google Scholar
Lindsay, R. K., Buchanan, B. G., Feigenbaum, E. A., & Lederberg, J. (1980). Applications of artificial intelligence for chemical inference: The DENDRAL project. New York: McGraw-Hill.Google Scholar
McDermott, J. (1982). A rule-based configurer of computer systems. Artificial Intelligence, 19, 39–88.CrossRefGoogle Scholar
Michie, D. (Ed.) (1979). Expert systems in the micro-electronic age. Edinburgh: Edinburgh University Press.Google Scholar
Minsky, M. (1981). A framework for representing knowledge. In Haugland, J. (Ed.), Mind design: Philosophy, psychology, artificial intelligence (pp. 95–128). Montgomery, VT: Bradford Books.Google Scholar
Mitchell, T. Machine learning. New York: McGraw Hill, 1997.Google Scholar
Moses, J. (1971). Symbolic integration: The stormy decade. Communications of the ACM, 14, 548–560.CrossRefGoogle Scholar
Nayak, P. & Williams, B. C. (1998). Model-directed autonomous systems. AI Magazine, 19, 126.Google Scholar
Newell, A., Shaw, J. C., & Simon, H. A. (1957). Empirical explorations with the logic theory machine. Reprinted in Feigenbaum & Feldman (1963).CrossRef
Newell, A. & Simon, H. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
Nilsson, N. J. (1995). Eye on the prize. AI Magazine, 16, 9–17.Google Scholar
Pauker, S. P. & Szolovits, P. (1977). Analyzing and simulating taking the history of the present illness: Context formation. In Schneider, W. and Sagvall-Hein, A. L. (Eds.), IFIP working congress on computational linguistics in medicine (pp. 109–118). Amsterdam: North-Holland.Google Scholar
Pazzani, M. J., & Brunk, C. A. (1991). Detecting and correcting errors in rule-based expert systems: An integration of empirical and explanation-based learning. Knowledge Acquisition, 3, 157–173.CrossRefGoogle Scholar
Pearl, J. (2002). Reasoning with cause, effect. AI Magazine, 23, 95–112.Google Scholar
Polanyi, M. (1962). Personal knowledge. Chicago: University of Chicago Press.Google Scholar
Polya, G. (1954). Mathematics and plausible reasoning (Vols. I & II). Princeton: Princeton University Press.Google Scholar
Pople, H. E., Myers, J., & Miller, R. (1975). DIALOG: A model of diagnostic logic for internal medicine. Proceedings of the Fourth International Joint Conference on Artificial Intellingence, USSR, 848–855.Google Scholar
Richards, D. & Compton, P. (1998). Taking up the situated cognition challenge with ripple down rules. International Journal of Human Computer Studies, 49, 895–926.CrossRefGoogle Scholar
Rutledge, G., Thomsen, G. E., Farr, B. R., Tovar, M. A., Polaschek, J. X., Beinlich, I. A. Sheiner, L. B., & Fagan, L. M. (1993). The design and implementation of a ventilator-management advisor. Artificial Intelligence in Medicine, 5, 67–82.CrossRefGoogle ScholarPubMed
Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3(3) 211–229. Reprinted in (Feigenbaum & Feldman, 1963).CrossRefGoogle Scholar
Scott, A. C., Clayton, J. E., & Gibson, E. L. (1991). A practical guide to knowledge acquisition. Boston: Addison-Wesley Long-man.Google Scholar
Shanteau, J. (1988). Psychological characteristics and strategies of expert decision makers. Acta Psychologica, 68, 203–215.CrossRefGoogle Scholar
Shortliffe, E. H. (1976). Computer-based medical consultation. MYCIN. New York: American Elsevier.Google Scholar
Simon, H. A. & Chase, W. G. (1973). Skill in chess. American Scientist, 621, 394–403.Google Scholar
Smith, R. & Farquhar, A. (2000). The road ahead for knowledge management: An AI perspective. AI Magazine, 21, 17–40.Google Scholar
Tversky, A. & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.CrossRefGoogle ScholarPubMed
VanLehn, K. (1996). Cognitive skill acquisition. Annual Review of Psychology, 47, 513–539.CrossRefGoogle ScholarPubMed
Weiss, S. M., Kulikowski, C. A., Amarel, S., & Safir, A. (1978). A model-based method for computer-aided medical decision making. Artificial Intelligence, 11, 145–172.CrossRefGoogle Scholar
Wilkins, D. C., Clancey, W. J., & Buchanan, B. G. (1988). Knowledge base refinement by monitoring abstract control knowledge. International Journal of Man-Machine Studies, 27, 281–293.CrossRefGoogle Scholar
Zadeh, L. (1965). Fuzzy sets. Information and Control, 8, 338–353.CrossRefGoogle Scholar

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