Abstract
This chapter concerns learning heuristic problem-solving strategies through experience. In particular, we focus on the issue of learning heuristics to guide a forward-search problem solver, and describe a computer program called lex, which acquires problem-solving heuristics in the domain of symbolic integration. lex acquires and modifies heuristics by iteratively applying the following process: (i) generate a practice problem; (ii) use available heuristics to solve this problem; (iii) analyze the search steps performed in obtaining the solution; and (iv) propose and refine new domain-specific heuristics to improve performance on subsequent problems. We describe the methods currently used by lex, analyze strengths and weaknesses of these methods, and discuss our current research toward more powerful approaches to learning heuristics.
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© 1983 Springer-Verlag Berlin Heidelberg
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Mitchell, T.M., Utgoff, P.E., Banerji, R. (1983). Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds) Machine Learning. Symbolic Computation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-12405-5_6
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DOI: https://doi.org/10.1007/978-3-662-12405-5_6
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