Hostname: page-component-5cf477f64f-tx7qf Total loading time: 0 Render date: 2025-04-07T15:00:45.427Z Has data issue: false hasContentIssue false
  • English
  • Français

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. [email protected]://www.soi.city.ac.uk/[email protected]://www.soi.city.ac.uk/[email protected]://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. [email protected]://www.soi.city.ac.uk/[email protected]://www.soi.city.ac.uk/[email protected]://www.inf.ufrgs.br/~lamb
Get access Rights & Permissions [Opens in a new window]

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

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

D'Avila Garcez, A., Broda, K., & Gabbay, D. (2002) Neural-symbolic learning systems: Foundations and applications. Springer-Verlag.CrossRefGoogle Scholar
Elman, J. L. (1990) Finding structure in time. Cognitive Science 14(2):179–211.CrossRefGoogle Scholar
Lamb, L., Borges, R. V. & d'Avila Garcez, A. (2007) A connectionist cognitive model for temporal synchronisation and learning. In: Proceedings of the Twenty-second AAAI Conference on Artificial Intelligence, pp. 827–32. AAAI Press.Google Scholar
Shastri, L. & Ajjanagadde, V. (1993) From simple associations to systematic reasoning: A connectionist representation of rules, variables, and dynamic bindings using temporal synchrony. Behavioral and Brain Sciences 16:417–51.CrossRefGoogle Scholar
Towell, G. & Shavlik, J. (1994) Knowledge-based artificial neural networks. Artificial Intelligence 70(1–2):119–65.CrossRefGoogle Scholar
Valiant, L (2003) Three problems in computer science. Journal of the ACM 50(1):96–99.CrossRefGoogle Scholar