Russell, S. J.

A quantitative analysis of analogy by similarity


Stuart J. Russell Department of Computer Science Stanford University Stanford, CA 94305 ABSTRACT In the absence of specific relevance information, the traditional assumption in the study of analogy has been that the most similar analogue is most likely to provide the correct solutions; a justification for this assumption has been lacking, as has any relation between the similarity measure used and the probability of correctness of the analogy. The predicted variation of the probability with source-target similarity corresponds closely to empirical analogy data obtained by Shepard for human and animal subjects for a wide variety of domains. What has been lacking in previous theories of analogy by similarity is any attempt to justify this assumption; the analysis in this paper hopes to rectify this situation. If a source matches the target on all relevant features, an analogy from that source is assumed to be correct.