Using symbolic descriptions to explain similarity on CBR
作者： Eva Armengol , Enric Plaza
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摘要: The explanation of the results is a key point automatic problem solvers. CBR systems solve new by assessing its similarity with already solved cases and they commonly show user set that have been assessed as most similar to problem. Using notion symbolic similarity, our proposal description makes explicit what has in common retrieved cases. In particular, we use anti-unification build this description.
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