Book contents
- The Cambridge Handbook of Expertise and Expert Performance
- The Cambridge Handbook of Expertise and Expert Performance
- Copyright page
- Contents
- Notes on Contributors
- Acknowledgments
- Part I Introduction and Perspectives
- Part II Overview of Approaches to the Study of Expertise: Brief Historical Accounts of Theories and Methods
- 6 Studies of Expertise from Psychological Perspectives: Historical Foundations and Recurrent Themes
- 7 Expert Systems: A Perspective from Computer Science
- 8 Developing Occupational Expertise through Everyday Work Activities and Interactions
- 9 Professionalism, Science, and Expert Roles: A Social Perspective
- Part III Methods for Studying the Structure of Expertise
- Part IV Methods for Studying the Acquisition and Maintenance of Expertise
- Part V.I Domains of Expertise: Professions
- Part V.II Domains of Expertise: Arts, Sports, Games, and Other Skills
- Part VI Generalizable Mechanisms Mediating Types of Expertise
- Part VII General Issues and Theoretical Frameworks
- Index of Subjects
- References
7 - 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
Published online by Cambridge University Press: 10 May 2018
Book contents
- The Cambridge Handbook of Expertise and Expert Performance
- The Cambridge Handbook of Expertise and Expert Performance
- Copyright page
- Contents
- Notes on Contributors
- Acknowledgments
- Part I Introduction and Perspectives
- Part II Overview of Approaches to the Study of Expertise: Brief Historical Accounts of Theories and Methods
- 6 Studies of Expertise from Psychological Perspectives: Historical Foundations and Recurrent Themes
- 7 Expert Systems: A Perspective from Computer Science
- 8 Developing Occupational Expertise through Everyday Work Activities and Interactions
- 9 Professionalism, Science, and Expert Roles: A Social Perspective
- Part III Methods for Studying the Structure of Expertise
- Part IV Methods for Studying the Acquisition and Maintenance of Expertise
- Part V.I Domains of Expertise: Professions
- Part V.II Domains of Expertise: Arts, Sports, Games, and Other Skills
- Part VI Generalizable Mechanisms Mediating Types of Expertise
- Part VII General Issues and Theoretical Frameworks
- Index of Subjects
- References
Summary
A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
- Type
- Chapter
- Information
- The Cambridge Handbook of Expertise and Expert Performance , pp. 84 - 104Publisher: Cambridge University PressPrint publication year: 2018
References
Ambrosino, R., & Buchanan, B. G. (1999). The use of physician domain knowledge to improve the learning of rule-based models for decision support. In Proceedings of the AMIA Annual Symposium (pp. 192–196). Washington, DC.Google Scholar
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. Journal of the Learning Sciences, 4, 355–384.CrossRefGoogle Scholar
Berg, C. A., & Sternberg, R. J. (1992). Adults’ conception of intelligence across the adult life span. Psychology and Aging, 7, 221–231.Google Scholar
Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 284 (May), 34–43.Google Scholar
Bobrow, D. G., & Hayes, P. J. (1985). Artificial intelligence: Where are we? Artificial Intelligence, 25, 375–415.Google Scholar
Boose, J. H. (1989). A survey of knowledge acquisition techniques and tools. Knowledge Acquisition, 1, 39–58.Google Scholar
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. New York: Norton.Google Scholar
Buchanan, B. G. (1994). The role of experimentation in artificial intelligence. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 349, 153–166.Google Scholar
Buchanan, B. G. (1995). Verification and validation of knowledge-based systems: A representative bibliography. Workshop on Evaluation of Knowledge-Based Systems, Lister Hill Center, National Library of Medicine, Bethesda, MD, December. www.quasar.org/21698/tmtek/biblio.html.Google Scholar
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., & Djerassi, 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., & Smith, R. G. (1988). Fundamentals of expert systems. In Traub, J. F., Grosz, B. J., Lampson, B. W., et al. (eds.), Annual review of computer science (Vol. 3, pp. 23–58). Palo Alto, CA: Annual Reviews Inc.Google Scholar
Buchanan, B. G., & Wilkins, D. C. (1993). Readings in knowledge acquisition and learning. San Mateo, CA: Morgan Kaufmann.Google Scholar
Byford, S. (2016). Google’s AlphaGo AI beats Lee Se-dol again to win Go series 4–1. The Verge. www.theverge.com/2016/3/15/11213518/alphago-deepmind-go-match-5-result.Google Scholar
Chandrasekaran, B., Josephson, J. R., & Benjamins, V. R. (1999). What are ontologies, and why do we need them? IEEE Intelligent Systems, 14, 20–26.Google Scholar
Chang, M. D., & Forbus, K. D. (2014). Using analogy to cluster hand-drawn sketches for sketch-based educational software. AI Magazine, 35, 76–84.Google Scholar
Cheetham, W. E. (2004). Tenth anniversary of the plastics color formulation tool. In Proceedings of the Sixteenth Conference on Innovative Applications of Artificial Intelligence (IAAI-04) (pp. 770–776). San Jose, CA: Association for the Advancement of Artificial Intelligence.Google Scholar
Chi, M., Glaser, R., & Farr, M. J. (eds.) (1988). The nature of expertise. Hillsdale, NJ: Erlbaum.Google Scholar
Chun, H. W. C., & Suen, T. Y. T. (2014). Engineering works scheduling for Hong Kong’s rail network. In Proceedings of the Twenty-Sixth Conference on Innovative Applications of Artificial Intelligence (IAAI-14) (pp. 2890–2897). Quebec City: Association for the Advancement of Artificial Intelligence.Google Scholar
Clancey, W. J. (1985). Heuristic classification. Artificial Intelligence, 27, 289–350.Google Scholar
Davis, R. (1979). Interactive transfer of expertise: Acquisition of new inference rules. Artificial Intelligence, 12, 121–157.Google Scholar
Davis, R. (1980). Meta-rules: Reasoning about control. Artificial Intelligence, 15, 179–222.Google Scholar
Davis, R. (1984). Diagnostic reasoning based on structure and behavior. Artificial Intelligence, 24, 347–410.Google Scholar
Davis, R. (1989). Expert systems: How far can they go? Part I. AI Magazine, 10, 61–67; Part II: AI Magazine, 10, 65–77.Google Scholar
Davis, R., & King, J. (1984). The origin of rule-based systems in AI. In Buchanan, B. G. & Shortliffe, E. H. (eds.), Rule-based expert systems: The MYCIN experiments of the Stanford Heuristic Programming Project (pp. 29–52). 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
Dzierzanowski, J. M., Chrisman, K. R., MacKinnon, G. J., & Klahr, P. (1989). The authorizer’s assistant: A knowledge-based credit authorization system for American Express. In Proceedings of the First Conference on Innovative Applications of Artificial Intelligence (IAAI-89) (pp. 168–172). Stanford, CA: Association for the Advancement of Artificial Intelligence.Google Scholar
Elstein, A. S., Shulman, L. S., & Sprafka, S. A. (1978). Medial problem solving: An analysis of clinical reasoning. Cambridge, MA: Harvard University Press.Google Scholar
Engelmore, R. S. (ed.) (1993). JTEC Panel on KNOWLEDGE-BASED SYSTEMS IN JAPAN, Distributed by National Technical Information Service, ISBN-10: 1883712009, ISBN-13: 978–1883712006. www.wtec.org/loyola//pdf/kb.pdf.Google Scholar
Erman, L. D., Hayes-Roth, F., Lesser, V. R., & Reddy, D. R. (1980). The Hearsay II Speech Understanding System: Integrating knowledge to resolve uncertainty. ACM Computing Surveys, 12, 213–253.Google Scholar
Feigenbaum, E. A., & Feldman, J. (1963). Computers and thought. New York: McGraw-Hill.Google Scholar
Feigenbaum, E. A., McCorduck, P., & Nii, P. (1988). The rise of the expert company. New York: Times Books.Google Scholar
Forbus, K., Usher, J., Lovett, A., Lockwood, K., & Wetzel, J. (2011). CogSketch: Sketch understanding for cognitive science research and for education. Topics in Cognitive Science, 3, 648–666.Google Scholar
Forsythe, D. E., & Buchanan, B. G. (1992). Nontechnical problems in knowledge engineering: Implications for project management. Expert Systems With Applications, 5, 203–212.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 and Biomedical Research, 25, 181–200.Google Scholar
Glasgow, B., Mandell, A., Binney, D., Ghemri, L., & Fisher, D. (1997). MITA: An information-extraction approach to the analysis of free-form text in life insurance applications. AI Magazine, 19, 59–72.Google Scholar
Goldstein, I., & Papert, S. (1977). Artificial intelligence, language, and the study of knowledge, Cognitive Science, 1, 84–123.Google Scholar
Gordon, J., & Shortliffe, E. H. (1985). A method for managing evidential reasoning in a hierarchical hypothesis space. Artificial Intelligence, 26, 323–357.Google Scholar
Halevy, A. Y., Norvig, P., & Pereira, F. (2009). The unreasonable effectiveness of data. IEEE Expert / IEEE Intelligent Systems – EXPERT, 24, 8–12.CrossRefGoogle Scholar
Hammond, T., & Davis, R. (2004). Automatically transforming symbolic shape descriptions for use in sketch recognition. In Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI-04) (pp. 450–456). San Jose, CA: Association for the Advancement of Artificial Intelligence.Google Scholar
Hayes, J. R. (1985). Three problems in teaching general skills. In Chipman, S. F., Segal, J. W., & Glaser, R. (eds.), Thinking and learning skills, Vol. 2: Research and open questions (pp. 391–405). Hillsdale, NJ: Erlbaum.Google Scholar
Hayes-Roth, F., Waterman, D. A. & Lenat, D. B. (eds.) (1983). Building expert systems. Reading, MA: Addison-Wesley.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.Google Scholar
Hendler, J. A., & Feigenbaum, E. A. (2001). Knowledge is power: The semantic web vision. In Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development, Maebashi City, Japan (pp. 18–29). London: Springer Verlag.Google Scholar
Hoffman, R., Baur, E., Dumer, J., Hanratty, T., & Ingham, H. (1999). Turbine engine diagnostics (TED). AI Magazine, 20, 69–76.Google Scholar
Kirkland, J. D., Senator, T. E., Hayden, J. J., Dybala, T., Goldberg, H. G., & Shyr, P. (1999). The NASD regulation Advanced-Detection System (ADS). AI Magazine, 20, 55–68.Google Scholar
Larkin, J., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.Google Scholar
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. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence (IJCAI-87), Milan, Italy (pp. 1173–1182). San Mateo, CA: Morgan Kaufmann.Google Scholar
Lindsay, R. K. (2012). Understanding understanding: Natural and artificial intelligence. CreateSpace. ISBN-13: 978–1466450585.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.Google Scholar
Michie, D. (ed.) (1979). Expert systems in the micro-electronic age. 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
Moses, J. (1971). Symbolic integration: The stormy decade. Communications of the ACM, 14, 548–560.Google Scholar
Moskowitz, A. J., Kuipers, B. J., & Kassirer, J. P. (1988). Dealing with uncertainty, risks, and tradeoffs in clinical decisions: A cognitive science approach. Annals of Internal Medicine, 108, 435–449.Google Scholar
Motta, E. (2013). Editorial: 25 years of knowledge acquisition. International Journal of Human–Computer Studies, 71 (Special Issue), 131–134.Google Scholar
Muratore, J. F., Heindel, T. A., Murphy, T. B., Rasmussen, A. N., & McFarland, R. Z. (1989). Applications of artificial intelligence to space shuttle mission control. In Proceedings of the First Conference on Innovative Applications of Artificial Intelligence (IAAI-89) (pp. 15–22). Stanford, CA: Association for the Advancement of Artificial Intelligence.Google Scholar
Nayak, P., & Williams, B. C. (1998). Model-directed autonomous systems. AI Magazine, 19, 126.Google Scholar
Newell, A. (1985). Artificial intelligence: Where are we? Artificial Intelligence, 2, 375–415.Google Scholar
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
O’Dell, C., & Hubert, C. (2011). The new edge in knowledge: How knowledge management is changing the way we do business. Hoboken, NJ: John Wiley.Google Scholar
Pan, S. J., & Yang, Q. (2010). A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22, 1345–1359.Google Scholar
Patel, V. L., & Groen, G. J. (1991). The general and specific nature of medical expertise: A critical look. In Ericsson, K. A. & Smith, J. (eds.), Toward a general theory of expertise: Prospects and limits (pp. 93–125). Cambridge University Press.Google Scholar
Pauker, S. P., & Szolovits, P. (1977). Analyzing and simulating taking the history of the present illness: Context formation. In Schneider, W. & 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.Google Scholar
Pearl, J. (2001). Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (pp. 411–420). San Francisco, CA: Morgan Kaufmann.Google Scholar
Polya, G. (1954). Mathematics and plausible reasoning, 2 vols. Princeton University Press.Google Scholar
Pople, H. E., Myers, J., & Miller, R. (1975). DIALOG: A model of diagnostic logic for internal medicine. In Proceedings of the Fourth International Joint Conference on Artificial Intelligence (IJCAI-75), Tbilisi, Georgia (pp. 848–855). San Mateo, CA: Morgan Kaufmann.Google Scholar
Rennels, G. D., Shortliffe, E. H., & Miller, P. L. (1987). Choice and explanation in medical management: A multiattribute model of artificial intelligence approaches. Medical Decision Making, 7, 22–31.CrossRefGoogle ScholarPubMed
Richards, D., & Compton, P. (1998). Taking up the situated cognition challenge with ripple down rules, International Journal of Human–Computer Studies, 49, 895–926.Google Scholar
Robinson, J. A. (1968). The generalized resolution principle. In Michie, D. (ed.), Machine Intelligence 3. Edinburgh University Press.Google Scholar
Robinson, J. A., & Sibert, E. E. (1982). LOGLISP: An alternative to PROLOG. In Hayes, J. E., Michie, D., & Pao, Y.-H. (eds.), Machine intelligence 10. Chichester: Ellis Horwood.Google Scholar
Rulequest (2017). Rulequest editors. C5.0: An informal tutorial. www.rulequest.com/see5-unix.html.Google Scholar
Rychtyckyj, N. (1999). DLMS: Ten years of AI for vehicle assembly process planning. In Proceedings of the Eleventh Conference on Innovative Applications of Artificial Intelligence (IAAI-99) (pp. 821–828), Orlando, FL: Association for the Advancement of Artificial Intelligence.Google Scholar
Samuel, A. (1959). Some studies in machine learning using the game of checkers. IBM Journal of Research and Development, 3, 535–554.Google Scholar
Scott, A. C., Clayton, J. E., & Gibson, E. L. (1991). A practical guide to knowledge acquisition. Boston: Addison-Wesley Longman.Google Scholar
Shadbolt, N. R., & Burton, A. M. (1989). Empirical studies in knowledge elicitation. ACM-SIGART Bulletin (Special Issue on Knowledge Acquisition), 108, 15–18.Google Scholar
Shanteau, J. (1988). Psychological characteristics and strategies of expert decision makers. Acta Psychologica, 68, 203–215.Google Scholar
Shaw, M. L. G., & Gaines, B. R. (1987). An interactive knowledge elicitation technique using personal construct technology. In Kidd, A. (ed.), Knowledge elicitation for expert systems: A practical handbook (pp. 109–136). New York: Plenum Press.Google 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., & Eckroth, J. (2016). Building AI applications: Yesterday, today, and tomorrow. AI Magazine, in press.Google Scholar
Smith, R., & Farquhar, A. (2000). The road ahead for knowledge management: An AI perspective. AI Magazine, 21, 17–40.Google Scholar
Tecuci, G., Marcu, D., Boicu, M., & Schum, D. A. (2015). Knowledge engineering: Building personal learning assistants for evidence-based reasoning. Cambridge University Press.Google Scholar
Thompson, D. (2015). A world without work. The Atlantic (July–August). www.theatlantic.com/magazine/archive/2015/07/world-without-work/395294/.Google Scholar
Thompson, E. D., Frolich, E., Bellows, J. C., Bassford, B. E., Skiko, E. J., & Fox, M. S. (2015). Process Diagnosis System (PDS): A 30 year history. In Proceedings of the Twenty-Seventh Conference on Innovative Applications of Artificial Intelligence (IAAI-13) (pp. 3928–3933). Austin, TX: Association for the Advancement of Artificial Intelligence.Google Scholar
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.Google Scholar
Urmson, C., Baker, C., Dolan, J., Rybski, P., Salesky, B., Whittaker, W., … & Darms, M. (2009). Autonomous driving in traffic: Boss and the urban challenge. AI Magazine, 30, 17–28.Google Scholar
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.Google Scholar
Wilkins, D. C., Clancey, W. J., & Buchanan, B. G. (1987). Knowledge base refinement by monitoring abstract control knowledge. International Journal of Man–Machine Studies, 27, 281–293.Google Scholar
- 20
- Cited by