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Four general representations and processes for use in problem solving

  • Knowledge Representation
  • Conference paper
  • First Online:
Knowledge Based Computer Systems (KBCS 1989)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 444))

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Abstract

It is argued that “knowledge representation” as normally understood is one of four very general constructs — two representations and two processes — which are commonly used in artificial intelligence (AI) research yet are largely unrecognised. These constructs mirror much of the “generate-and-test” strategy for problem solving in AI where competing solutions are generated and then tested to select the best one — except that one of the constructs, “coherence representation,” is not present in the strategy. Examples of various problems are given that show uses of the constructs. Some implications for artificial intelligence research of the constructs, especially coherence representation, are discussed.

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References

  • Amarel, Saul. Problem Solving. In Stuart C. Shapiro (Ed.) Encyclopaedia of Artificial Intelligence. New York, NY: John Wiley & Sons, pp. 767–779, 1987.

    Google Scholar 

  • Barr, Avron, and Edward A. Feigenbaum. Knowledge Representation. Handbook of Artificial Intelligence, Volume I. Los Altos, CA: William Kaufman. Chap. III, pp. 141–222, 1981.

    Google Scholar 

  • Cercone, Nicholas J., and Gordon McCalla (Eds.). The Knowledge Frontier: Essays in the Representation of Knowledge. New York, NY: Springer Verlag, 1987.

    Google Scholar 

  • Fass, Dan C. Semantic Relations, Metonymy, and Lexical Ambiguity Resolution: A Coherence-Based Account. In Proceedings of the 9th Annual Cognitive Science Society Conference. University of Washington, Seattle, WA, pp. 575–586, 1987.

    Google Scholar 

  • Falkenhainer, Brian, Kenneth D. Forbus, and Dedre Gentner. The Structure-Mapping Engine. In Proceedings of the 5th National Conference on Artificial Intelligence (AAAI-86). Philadelphia, PA, pp. 272–277, 1986.

    Google Scholar 

  • Fass, Dan C. An Account of Coherence, Semantic Relations, Metonymy, and Lexical Ambiguity Resolution. In Steven L. Small, Gary W. Cottrell, and Michael K. Tanenhaus (Eds.). Lexical Ambiguity Resolution: Perspectives from Psycholinguistics, Neuropsychology, and Artificial Intelligence. Los Altos, CA: Morgan Kaufmann, pp. 151–178, 1988a.

    Google Scholar 

  • Fass, Dan C. Collative Semantics: A Semantics for Natural Language Processing. (PhD thesis.) Memorandum MCCS-88-118, Computing Research Laboratory, New Mexico State University, NM, 1988b.

    Google Scholar 

  • Fass, Dan C. Collative Semantics: A Study in the Discrimination of Meaning. Technical Report CSS/LCCR TR 88-24, Centre for Systems Science, Simon Fraser University, Burnaby, BC, Canada, 1988c.

    Google Scholar 

  • Fass, Dan C., and Yorick A. Wilks. Preference Semantics, Ill-Formedness and Metaphor. American Journal of Computational Linguistics, Special Issue on Ill-Formed Input, 9, (3–4), pp. 178–187, 1983.

    Google Scholar 

  • Gentner, Dedre. Structure Mapping: A Theoretical Framework for Analogy. Cognitive Science, 7, pp. 155–170, 1983.

    Google Scholar 

  • Mylopoulos, John, and Hector Levesque. An Overview of Knowledge Representation. In Bernd Neumann (Ed.). Proceedings of the 7th German Workshop on Artificial Intelligence, Dassel/Solling, September 1983. New York, NY: Springer Verlag, pp. 143–157, 1983.

    Google Scholar 

  • Schank, Roger C., and Robert P. Abelson. Scripts, Plans, Goals and Understanding. Hillsdale, NJ: Lawrence Erlbaum Associates, 1977.

    Google Scholar 

  • Small, Steven L., Gary W. Cottrell, and Michael K. Tanenhaus (Eds.). Lexical Ambiguity Resolution: Perspectives from Psycholinguistics, Neuropsychology, and Artificial Intelligence. Los Altos, CA: Morgan Kaufmann, 1988.

    Google Scholar 

  • Wilks, Yorick A. A Preferential Pattern-Seeking Semantics for Natural Language Inference. Artificial Intelligence, 6, pp. 53–74, 1975a.

    Google Scholar 

  • Wilks, Yorick A. An Intelligent Analyser and Understander for English. Communications of the ACM, 18, pp. 264–274, 1975b.

    Google Scholar 

  • Wilks, Yorick A. Making Preferences More Active. Artificial Intelligence, 11, pp. 197–223, 1978. Reprinted in In Nicholas V. Findler (Ed.). Associative Networks: Representation and Use of Knowledge By Computers. New York, NY: Academic Press, pp. 239–266, 1979.

    Google Scholar 

  • Winston, Patrick H. Learning by Creatifying Transfer Frames. Artificial Intelligence, 10, pp. 147–172, 1978.

    Google Scholar 

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Authors and Affiliations

  1. Centre for Systems Science, Simon Fraser University, V5A 1S6, Burnaby, British Columbia, Canada

    Dan Fass

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  1. Dan Fass
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Editor information

S. Ramani R. Chandrasekar K. S. R. Anjaneyulu

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© 1990 Springer-Verlag Berlin Heidelberg

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Fass, D. (1990). Four general representations and processes for use in problem solving. In: Ramani, S., Chandrasekar, R., Anjaneyulu, K.S.R. (eds) Knowledge Based Computer Systems. KBCS 1989. Lecture Notes in Computer Science, vol 444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0018377

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  • DOI: https://doi.org/10.1007/BFb0018377

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-52850-0

  • Online ISBN: 978-3-540-47168-4

  • eBook Packages: Springer Book Archive

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