Keywords
artificial intelligence, learning, reasoning
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.
Original Publication Citation
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.
BYU ScholarsArchive Citation
Giraud-Carrier, Christophe G. and Martinez, Tony R., "An Integrated Framework for Learning and Reasoning" (1995). Faculty Publications. 1159.
https://scholarsarchive.byu.edu/facpub/1159
Document Type
Peer-Reviewed Article
Publication Date
1995-08-01
Permanent URL
http://hdl.lib.byu.edu/1877/2414
Publisher
IEEE
Language
English
College
Physical and Mathematical Sciences
Department
Computer Science
Copyright Status
© 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://lib.byu.edu/about/copyright/