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dc.contributor.authorShavlik, Jude Wen_US
dc.description.abstractIn explanation-based learning, a specific problem?s solution is generalized into a form that can be later used to solve conceptually similar problems. Most research in explanation-based learning involves relaxing constraints on the variables in the explanation of a specific example, rather than generalizing the graphical structure of the explanation itself. However, this precludes the acquisition of concepts where an iterative or recursive process is implicity represented in the explanation by a fixed number of applications. This paper presents an algorithm that generalizes explanation structures and reports empirical results that demonstrate the value of acquiring recursive and iterative concepts. The BAGGER2 algorithm learns recursive and iterative concepts, integrates results from multiple examples, and extracts useful subconcepts during generalization. On problems where learning a recursive rule is not appropriate, the system produces the same result as standard explanation-based methods. Applying the learned recursive rules only requires a minor extension to a PROLOG-like problem solver, namely, the ability to explicitly call a specific rule. Empirical studies demonstrate that generalizing the structure of explanations helps avoid the recently reported negative effects of learning.en_US
dc.publisherUniversity of Wisconsin-Madison Department of Computer Sciencesen_US
dc.titleAcquiring Recursive and Iterative Concepts With Explanation-Based Learningen_US
dc.typeTechnical Reporten_US

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  • CS Technical Reports
    Technical Reports Archive for the Department of Computer Sciences at the University of Wisconsin-Madison

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