Abstract
Explanation-Based Learning is drawing an increasing interest in the Machine Learning community. Many researchers are now interested in the problem of integrating Explanation-Based Learning (EBL) with different techniques of empirical learning. We propose a system that relies on Explanation-Based Generalization (EBG). In our case, the EBG module receives multiple concept instances. The learning mechanism presented in this paper allows incremental modifications of the EBG generated generalization.
When dealing with incomplete theories, we propose to complete proofs that fail using an abduction mechanism. The problem then is to limit the number of possible explanations to be considered. For that purpose, the abduction process is guided by comparison to a reference explanation. We look for an augmented explanation which is analogous to the already known explanation of the concept being studied. We thus propose incremental refinements to the existing rules of the theory.
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Duval, B., Kodratoff, Y. (1990). A Tool for the Management of Incomplete Theories: Reasoning about Explanation. 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_7
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DOI: https://doi.org/10.1007/978-1-4613-1641-1_7
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