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A neural-symbolic perspective on analogy

  • Rafael V. Borges (a1) (a2), Artur S. d'Avila Garcez (a1) and Luis C. Lamb (a2)
  • DOI: http://dx.doi.org/10.1017/S0140525X08004482
  • Published online: 01 July 2008
Abstract
Abstract

The target article criticises neural-symbolic systems as inadequate for analogical reasoning and proposes a model of analogy as transformation (i.e., learning). We accept the importance of learning, but we argue that, instead of conflicting, integrated reasoning and learning would model analogy much more adequately. In this new perspective, modern neural-symbolic systems become the natural candidates for modelling analogy.

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This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

A. D'Avila Garcez , K. Broda , & D. Gabbay (2002) Neural-symbolic learning systems: Foundations and applications. Springer-Verlag.

J. L. Elman (1990) Finding structure in time. Cognitive Science 14(2):179211.

G. Towell & J. Shavlik (1994) Knowledge-based artificial neural networks. Artificial Intelligence 70(1–2):119–65.

L Valiant (2003) Three problems in computer science. Journal of the ACM 50(1):9699.

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Behavioral and Brain Sciences
  • ISSN: 0140-525X
  • EISSN: 1469-1825
  • URL: /core/journals/behavioral-and-brain-sciences
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