Massively Parallel Case-Based Reasoning with Probabilistic Similarity Metrics
نویسندگان
چکیده
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 ooers 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 eecient massively parallel case retrieval is an inherent property of the system. We argue that using this kind of an approach, the diicult problem of case indexing can be completely avoided.
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