Proceedings of the Annual Meeting of the Cognitive Science Society

Analogical reasoning is the process of retrieving knowledge of a familiar problem (source analog) similar to the current problem (target) and transferring that knowledge to solve the problem. The power of an analogical reasoner thus comes in part from the ability to retrieve the "right" analog when a tai^et is specified. Indexing of analogs therefore is an important issue in analogical reasoning. This issue in fact has three different aspects: (i) indexing vocabulary, (ii) learning of the indices to a new analog, and (iii) use of indices for retrieving stored analogs. W e have been exploring the hypothesis that the reasoner's mental models of the analogs give rise to the answers to these issues. W e have tested this hypothesis in the context of analogical design of physical devices. In this paper, we describe how structure-behavior-function (SBF) models of devices help in addressing the indexing issues in analogical design. W e also describe how the IDEAL system implements and evaluates the model-based scheme to indexing and index learning.