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A neural-symbolic perspective on analogy

Published online by Cambridge University Press:  29 July 2008

Rafael V. Borges ,
Artur S. d'Avila Garcez  and
Luis C. Lamb
Rafael V. Borges
Affiliation:
Department of Computing, City University London, Northampton Square, London, EC1V 0HB, United Kingdom Institute of Informatics, Federal University of Rio Grande do Sul, Brazil, Porto Alegre, RS, 91501-970, Brazil. Rafael.Borges.1@soi.city.ac.ukhttp://www.soi.city.ac.uk/~Rafael.Borges.1aag@soi.city.ac.ukhttp://www.soi.city.ac.uk/~aagLuisLamb@acm.orghttp://www.inf.ufrgs.br/~lamb
Artur S. d'Avila Garcez
Affiliation:
Department of Computing, City University London, Northampton Square, London, EC1V 0HB, United Kingdom
Luis C. Lamb
Affiliation:
Institute of Informatics, Federal University of Rio Grande do Sul, Brazil, Porto Alegre, RS, 91501-970, Brazil. Rafael.Borges.1@soi.city.ac.ukhttp://www.soi.city.ac.uk/~Rafael.Borges.1aag@soi.city.ac.ukhttp://www.soi.city.ac.uk/~aagLuisLamb@acm.orghttp://www.inf.ufrgs.br/~lamb
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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.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 31 , Issue 4 , August 2008 , pp. 379 - 380
Copyright
Copyright © Cambridge University Press 2008

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