Analogy has a rich history in Western civilization. Over the centuries, it has become reified in that analogical reasoning has sometimes been regarded as a fundamental cognitive process. In addition, it has become identified with a particular expressive format. The limitations of the modern view are illustrated by monster analogies, which show that analogy need not be regarded as something having a single form, format, or semantics. Analogy clearly does depend on the human ability to create and use well-defined or analytic formats for laying out propositions that express or imply meanings and perceptions. Beyond this dependence, research in cognitive science suggests that analogy relies on a number of genuinely fundamental cognitive capabilities, including semantic flexibility, the perception of resemblances and of distinctions, imagination, and metaphor. Extant symbolic models of analogical reasoning have various sorts of limitation, yet each model presents some important insights and plausible mechanisms. I argue that future efforts could be aimed at integration. This aim would include the incorporation of contextual information, the construction of semantic bases that are dynamic and knowledge rich, and the incorporation of multiple approaches to the problems of inference constraint.

Similarity in Cognition: A Review of Similarity and Analogical Reasoning

Analogical although analogy can help, as note that although still in its infancy reasoning is thus achieved in such well as hamper, learning. The role of and somewhat simplistic in character, systems by mainly keeping the analogy in learning is discussed by connectionist research might prove abstract relational microfeatures. Ann Brown and by Rand Spiro et al., to have an edge in tackling these Rumelhart proposes another way and the role of analogy in knowledge problems. The research described in for achieving analogical reasoning, acquisition is discussed by Brian Ross this book presents a grand challenge that is, "soft clamp," in which input and by John Bransford et al.; Stella and a future prospect for AI clamps can be overridden, and the Vosniadou studies the developmental researchers (traditional or connectionistic) rule of thumb is that the more concrete change in the use of analogy. Because in their endeavor to find a a feature is, the easier it can be part 3 of the book is of marginal better and more cognitively plausible overridden. The system finds the interest to AI, I do not discuss it any representation scheme.

This paper describes the structure-mapping engine (SME), a program for studying analogical processing. SME has been built to explore Gentner's structure-mapping theory of analogy, and provides a “tool kit” for constructing matching algorithms consistent with this theory. Its flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SME is very efficient, making it a useful component in machine learning systems as well. We review the structure-mapping theory and describe the design of the engine. We analyze the complexity of the algorithm, and demonstrate that most of the steps are polynomial, typically bounded by O(N2).

In IJCAI-87, Proc. Tenth Intl. Joint Conference on Artificial Intelligence, Milan, Italy: Morgan Kaufmann, 1987, Vol. 1, pp. 264-270.