The Psychology of Expertise: Cognitive Research and Empirical AI

By Robert R. Hoffman | Go to book overview

Fortunately, taking into account the "inner state of the living organism" no longer threatens anyone with the abyss of metaphysics. In fact, inner states can be formalized within automata theory -- one of the sciences of the artificial -- and this is perhaps as concrete and as "unmetaphysical" as one can get. Attitudes in psychology have certainly changed over the years, and the chapters in this volume indicate how much the range of interest has been broadened.

We can now even look back upon a "tradition" of KA within psychology. The elicitation phase of the KA process is specifically within the domain of expertise of the psychologist, and we can anticipate further work in the area. Psychologists will not be implementing expert systems. Rather, they will work on the knowledge-elicitation phase of the project as members of a multidisciplinary team. In the short run, we expect that knowledge elicitation and the validation of the acquired knowledge is where empirical, experimental, applied psychology is going to be most helpful. In the long run, we can anticipate that ideas and methods from psychology will have a profound effect on the changing architecture of future expert systems (see, e.g., Sternberg & Frensch, this volume). Additionally, we can anticipate that insight from psychological work that probes the complex nature of expertise and expertise transfer will suggest new uses for the acquired expertise, such as the preserving of expertise through a general "knowledge medium," as suggested by Klein (this volume).

It is quite likely that psychologists will play an active role in the knowledge-elicitation phase of the knowledge acquisition process, and that their insight will help make expert systems more usable for actual users ( Hart & Foley, this volume). The division of labor and the use of development teams combined of several diverse professional skill sets seem like inevitable developments as expert systems become larger and more sophisticated. One can foresee an exciting era of cooperation between AI and psychology based on the necessity of delivering empirically sound, workable systems that are usable by real people who are ecologically situated in actual contexts.


Notes
2
A more detailed description of various theories and metaphors of knowledge can be found in Regoczei and Hirst, 1989, and Hoffman, Cochran, and Nead, 1990.
3
The appelation naive is intended to indicate that such knowledge is wrong. Conversely, "commonsense" knowledge is often assumed to be basically correct although fallible.

References

Buchanan B. G., & Smith R. G. ( 1989). "Fundamentals of expert systems". In A. Barr, P. R. Cohen , & E. A. Feigenbaum (Eds.), The handbook of artificial intelligence (pp. 149-192). Reading, MA: Addison-Wesley.

Chandrasekaran B. ( 1988). "Generic tasks as building blocks for knowledge-based systems". The Knowledge Engineering Review, 3, 183-210.

Feigenbaum E. A. ( 1983). "Knowledge engineering: The applied side". In J. E. Hayes & D. Michie (Eds.), Intelligent systems: The unprecedented opportunity (pp. 37-55). New York: Halstead Press.

Feigenbaum E. A., & McCorduck P. ( 1983). The fifth generation. Reading, MA: Addison-Wesley.

Frege G. ( 1950). The foundations of arithmetic. (J. L. Austin, Trans.). New York: The Philosophical Library.

Gardner H. ( 1985). The mind's new science: A history of the cognitive revolution. New York: Basic Books.

Hayes P. ( 1984). "The second naive physics manifesto". In J. Hobbs (Ed.), Formal theories of the commonsense world. Norwood, NJ: Ablex.

Hayes-Roth F., Waterman D., & Lenat D. ( 1983). Building expert systems. Reading, MA: Addison-Wesley.

Hoffman R. R. ( 1987, Summer). "The problem of extracting the knowledge of experts from the perspective of experimental psychology". The AI Magazine, pp. 53-66.

Hoffman R. R., Cochran E. L., & Nead J. ( 1990). "Cognitive metaphors in experimental psychology".

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