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  1. Explanation-based learning:A problem solving perspective.Steven Minton, Jaime G. Carbonell, Craig A. Knoblock, Daniel R. Kuokka, Oren Etzioni & Yolanda Gil - 1989 - Artificial Intelligence 40 (1-3):63-118.
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  • Explanation-based interpretation of open-textured concepts in logical models of legislation.Stefania Costantini & Gaetano Aurelio Lanzarone - 1995 - Artificial Intelligence and Law 3 (3):191-208.
    In this paper we discuss a view of the Machine Learning technique called Explanation-Based Learning (EBL) or Explanation-Based Generalization (EBG) as a process for the interpretation of vague concepts in logic-based models of law.The open-textured nature of legal terms is a well-known open problem in the building of knowledge-based legal systems. EBG is a technique which creates generalizations of given examples on the basis of background domain knowledge. We relate these two topics by considering EBG''s domain knowledge as corresponding to (...)
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  • Acquiring search-control knowledge via static analysis.Oren Etzioni - 1993 - Artificial Intelligence 62 (2):255-301.
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  • A structural theory of explanation-based learning.Oren Etzioni - 1993 - Artificial Intelligence 60 (1):93-139.
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  • Computer Go: An AI oriented survey.Bruno Bouzy & Tristan Cazenave - 2001 - Artificial Intelligence 132 (1):39-103.
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  • A high-performance explanation-based learning algorithm.Alberto Segre & Charles Elkan - 1994 - Artificial Intelligence 69 (1-2):1-50.
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  • On the relations between intelligent backtracking and failure-driven explanation-based learning in constraint satisfaction and planning.Subbarao Kambhampati - 1998 - Artificial Intelligence 105 (1-2):161-208.
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  • Quantitative results concerning the utility of explanation-based learning.Steven Minton - 1990 - Artificial Intelligence 42 (2-3):363-391.
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  • Proving theorems by reuse.Christoph Walther & Thomas Kolbe - 2000 - Artificial Intelligence 116 (1-2):17-66.
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  • Logical approaches to machine learning-an overview.P. Flach - 1992 - Think (misc) 1 (2):25-36.
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