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An Integrated Framework for Learning and Reasoning

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Permanent URL	http://hdl.lib.byu.edu/1877/2414
Title	An Integrated Framework for Learning and Reasoning
BYU Creator	Giraud-Carrier, Christophe G. Martinez, Tony R.
Keywords	artificial intelligence; learning; reasoning
Description/Abstract	Learning and reasoning are both aspects of what is considered to be intelligence. Their studies within AI have been separated historically, learning being the topic of machine learning and neural networks, and reasoning falling under classical (or symbolic) AI. However, learning and reasoning are in many ways interdependent. This paper discusses the nature of some of these interdependencies and proposes a general framework called FLARE, that combines inductive learning using prior knowledge together with reasoning in a propositional setting. Several examples that test the framework are presented, including classical induction, many important reasoning protocols and two simple expert systems.
Source	Giraud-Carrier, C. and Martinez, T. R., "An Integrated Framework for Learning and Reasoning", Journal of Artificial Intelligence Research, vol. 3, pp. 147-185, 1995.;
Publisher	IEEE
Date Original	1995
Date Digital	2010-09-09
Language	English eng en
Publication Type	Peer-Reviewed Articles
Type	Text
Format	application/pdf
Owning Institution	Brigham Young University
BYU College	College of Physical and Mathematical Sciences
BYU Department	Computer Science
Copyright Status	(c) 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.;
Copyright Use Information	http://www.lib.byu.edu/generic_copyright.html

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