Abstract
This paper demonstrates how explanation-based generalization (EBG) can create overgeneral rules if relevant knowledge is hidden from the EBG process, and proposes two solutions to this problem. EBG finds the weakest preconditions of a proof to from a generalization covering all cases for which the proof succeeds. However, when knowledge relevant to the success of EBG is hidden from the EBG process, the results of the process can be overgeneral. Two examples of suchknowledge are discussed: search control, and theories of operationality. The key idea is that when such relevant knowledge is hidden from the EBG process, the results of EBG are no longer unconditionally true. Additional conditions — the conditions on when the extra information would still in general hold — must be added to the results of EBG to guarantee their correctness. Two methods for generating these additional conditions are presented: explicitly generalizing meta-level reasoning and including the results in the base-level generalization; and rewriting the domain theory to include the additional knowledge so that standard EBG forms correct results.
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© 1990 Kluwer Academic Publishers
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Hirsh, H. (1990). Overgenerality in Explanation-Based Generalization. In: Brazdil, P.B., Konolige, K. (eds) Machine Learning, Meta-Reasoning and Logics. The Kluwer International Series in Engineering and Computer Science, vol 82. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1641-1_6
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DOI: https://doi.org/10.1007/978-1-4613-1641-1_6
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