Nonadversary Problem Solving by Machine

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Human and Machine Problem Solving

Abstract

Almost all artificial intelligence programs can be said to be doing some form of problem solving whether it be interpreting a visual scene, parsing a sentence, or planning a sequence of robot actions. In this chapter we shall adopt a rather more specialized meaning for the term and regard it as covering the study of the properties of algorithms (1) for conducting search and (2) for construction and manipulating plans of action.

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References

  • Amarel, S. (1981). On representations of problems of reasoning about actions. In B. L. Webber and N. J. Nilsson (Eds.), Readings in knowledge representation. Los Altos, CA: Tioga.

    Google Scholar 

  • Barr, A., & Feigenbaum, E. A. (1981). The handbook of artificial intelligence (Vol. 1 ). Los Altos, CA: William Kaufmann.

    Google Scholar 

  • Chapman, D. (1987). Planning for conjunctive goals. Artificial Intelligence, 32(3), 333–377.

    Google Scholar 

  • Charniak, E., & McDermott D. (1985). Introduction to artificial intelligence. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Doran, J. (1968). New developments of the graph traverser. In E. Dale and D. Michie (Eds.), Machine intelligence (Vol. 2 ). Edinburgh: Oliver and Boyd.

    Google Scholar 

  • Ernst, G., & Newell, A. (1969). GPS: A case study in generality and problem solving. New York: Academic Press.

    Google Scholar 

  • Fikes, R. E., & Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2, 189–208.

    Article  Google Scholar 

  • Kowalski, R. (1979). Logic for problem solving. New York: North-Holland.

    Google Scholar 

  • Nilsson, N. J. (1980). Principles of artificial intelligence. Los Altos, CA: Tioga.

    Google Scholar 

  • Rich, E. (1983). Artificial intelligence. New York: McGraw-Hill.

    Google Scholar 

  • Sacerdoti, E. D. (1977). A structure for plans and behavior. New York: Elsevier.

    Google Scholar 

  • Tate, A. (1985). A review of knowledge-based planning techniques. Knowledge Engineering Review, 1, 2.

    Google Scholar 

  • Wilensky, R. (1983). Planning and understanding. Reading, MA: Addison-Wesley.

    Google Scholar 

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© 1989 Plenum Press, New York

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du Boulay, B. (1989). Nonadversary Problem Solving by Machine. In: Gilhooly, K.J. (eds) Human and Machine Problem Solving. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-8015-3_2

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  • DOI: https://doi.org/10.1007/978-1-4684-8015-3_2

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4684-8017-7

  • Online ISBN: 978-1-4684-8015-3

  • eBook Packages: Springer Book Archive

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