Massively Parallel Case-Based Reasoning with
Probabilistic Similarity Metrics

Petri Myllymki and Henry Tirri

University of Helsinki, Department of Computer Science*
P.O.Box 26, FIN-00014 University of Helsinki, Finland
email: Petri.Myllymaki@cs.Helsinki.FI, Henry.Tirri@cs.Helsinki.FI



Abstract. We propose a probabilistic case-space metric for the case
matching and case adaptation tasks. Central to our approach is a probability 
propagation algorithm adopted from Bayesian reasoning systems,
which allows our case-based reasoning system to perform theoretically
sound probabilistic reasoning. The same probability propagation mechanism 
actually offers a uniform solution to both the case matching and
case adaptation problems. We also show how the algorithm can be implemented 
as a connectionist network, where efficient massively parallel
case retrieval is an inherent property of the system. We argue that using
this kind of an approach, the difficult problem of case indexing can be
completely avoided.
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