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Summary: American Association for Artificial Intelligence, Case-based reasoning integrations; Papers from the AAAI workshop. David Aha, Jody J. Daniels (eds.).
Technical Report WS-98-15. AAAI Press, Menlo Park, 1998. ISBN 1-57735-068-5. pp 1-6.
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Integrating Bayesian Networks into Knowledge-Intensive CBR
Agnar Aamodt & Helge Langseth
Norwegian University of Science and Technology Norwegian University of Science and Technology
Department of Computer and Information Science Department of Mathematical Sciences
N-7034 Trondheim, Norway N-7034 Trondheim, Norway
Agnar.Aamodt@idi.ntnu.no Helge.Langseth@stat.ntnu.no
Abstract
In this paper we propose an approach to knowledge
intensive CBR, where explanations are generated from a
domain model consisting partly of a semantic network and
partly of a Bayesian network (BN). The BN enables learning
within this domain model based on the observed data. The
domain model is used to focus the retrieval and reuse of past
cases, as well as the indexing when learning a new case.
Essentially, the BN-powered submodel works in parallel
with the semantic network model to generate a statistically
sound contribution to case indexing, retrieval and
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