Abstract
We use analogy when we say something is a Cinderella story and when we learn about resistors by thinking about water pipes. We also use analogy when we learn subjects like economics, medicine, and law. This paper presents a theory of analogy and describes an implemented system that embodies the theory. The specific competence to be understood is that of using analogies to do certain kinds of learning and reasoning. Learning takes place when analogy is used to generate a constraint description in one domain, given a constraint description in another, as when we learn Ohm's law by way of knowledge about water pipes. Reasoning takes place when analogy is used to answer questions about one situation, given another situation that is supposed to be a precedent, as when we answer questions about Hamlet by way of knowledge about Macbeth.
- 1 Brotsky, D. Efficient graph matching through exploitation of constraint. M.1.T. Artif. lntell. Lab. Memo No. 600, Cambridge, Mass., Oct. 1980.Google Scholar
- 2 Carroll, J.B., Daves, P., and Richmond, B. Word Frequency Book. Houghton-Mifflin and American Heritage, New York, 1971.Google Scholar
- 3 Evans, T.G. A heuristic program to solve geometric analogy problems. Ph.D. Th., M.I.T., Cambridge, Mass. in Semantic Information Processing, M. Minsky, Ed., The M.I.T. Press, Cambridge, Mass., 1968.Google Scholar
- 4 Filmore, C.J. The case for case. In Universals in Linguistic Theory, E. Bach and R. Harms, Eds., Holt, Rinehart, and Winston, New York, 1968.Google Scholar
- 5 Givon, T. Cause and control: On the semantics of interpersonal manipulation. In Syntax and Semantics, Vol IV, J. Kimball, Ed., Academic, New York, 1975.Google Scholar
- 6 Katz, B. A three-step procedure for language generation. M.I.T. Artif. Intell. Lab. Memo. No. 599, Cambridge, Mass. Oct. 1980.Google Scholar
- 7 Lenat, D. AM: An artificial intelligence approach to discovery in mathematics as heuristic search. Ph.D. Th., Stanford Univ., Stanford, Calif. in Knowledge-Based Systems in Artificial Intelligence, McGraw- Hill, New York, 1979. Google ScholarDigital Library
- 8 Martin, W.A. Philosophical foundations for a linguistically oriented semantic network (in preparation).Google Scholar
- 9 Meldman, J. A preliminary study in computer-aided legal analysis. Ph.D. Th. and Tech. Rep. No. MAC-TR-157, M.I.T. Lab. for Comptr. Sci, Cambridge, Mass., Nov. 1975. Google ScholarDigital Library
- 10 Minsky, M. A framework for representing knowledge. In The Psychology of Computer Vision, P.H. Winston, Ed., McGraw-Hill, New York, 1975.Google Scholar
- 11 Moore, J. and Newell, A. How can Merlin understand? In Knowledge and Cognition. L. Gregg, Ed., Lawrence Erlbaum Associates, Potomac, Md., 1974.Google Scholar
- 12 Ogden, C.K. Basic English: International Second Language. Harcourt, Brace, and World, New York, 1968.Google Scholar
- 13 Rieger, C. The commonsense algorithm as a basis for computer models of human memory, inference, belief, and contextual language comprehension. Dept. Comptr. Sci. Tech. Rep. No. 373, Univ. of Maryland, College Park, Md., 1975.Google Scholar
- 14 Roberts, R.B. and Goldstein, I.P. The FRL primer. M.1.T. Artif. lntell. Lab. Memo No. 408, Cambridge, Mass., July 1977. Google ScholarDigital Library
- 15 Roberts, R.B. and Goldstein, I.P. The FRL manual. M.I.T. Artif. lntell. Lab. Memo No. 409, Cambridge, Mass., June 1977. Google ScholarDigital Library
- 16 Rosch, E., Mervis, C.B., Gray, W.D., Johnson, D.M., and Boye'.,- Braem, P. Basic objects in natural categories. Cog. Psych 8, 3 (July 1976) 382-439.Google ScholarCross Ref
- 17 Schank, R.C. Conceptual Information Processing. North-Holland, New York, 1975. Google ScholarDigital Library
- 18 Tversky, A. Features of similarity. Psych. Rev. 84, 4 (July 1977), 327-352.Google ScholarCross Ref
- 19 Wilks, Y.A. Grammar, Meaning, and the Machine Analysis of Language. Routlege and Kegan Paul, London, 1972.Google Scholar
- 20 Winston, P.H. Learning structural descriptions from examples. Ph.D. Th., M.I.T., Cambridge, Mass. in The Psychology of Computer Vision, P.H. Winston, Ed., McGraw-Hill, New York, 1975.Google Scholar
- 21 Winston, P.H. Learning by creating and justifying transfer frames. Artif lntell. 10, 2 (1978), 147-172.Google Scholar
- 22 Winston, P.H. Learning and reasoning by analogy: The details (formerly titled "Learning by understanding analogies"). M.I.T. Artif. Intell. Lab. Memo No. 520, Cambridge, Mass., April 1979.Google Scholar
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