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Combining Analogies in Mental Models

  • Mark H. Burstein
Chapter
Part of the Synthese Library book series (SYLI, volume 197)

Abstract

In recent years, researchers in artificial intelligence and cognitive psychology have begun to focus more attention on the study of analogical reasoning and its role in learning and problem solving, particularly in scientific and technical domains. A number of these researchers (Collins and Gentner, 1982; Winston, 1982; Burstein, 1986; Gentner, 1983; Thagard and Holyoak, 1985; Carbonell, 1986) have independently converged on a model of analogical learning based on a kind of plausible hypothesis generation process. By this model, predictions and explanations of phenomena in unfamiliar domains can be hypothesized, given an analogy to a more familiar situation, by a structural mapping process that takes an explanation of the more familiar situation, and produces an explanation for the new domain. The mapped hypothesis must then be tested or verified by predicting observable effects, or, in the case of analogical planning, explaining how a goal can be satisfied, etc.

Keywords

Causal Model Causal Structure Partial Model Target Domain Assignment Statement 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1988

Authors and Affiliations

  • Mark H. Burstein
    • 1
  1. 1.Bolt Beranek and Newman LaboratoriesCambridgeUSA

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