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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)

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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D'Avila Garcez, A., Broda, K., & Gabbay, D. (2002) Neural-symbolic learning systems: Foundations and applications. Springer-Verlag.
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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.
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.
Towell, G. & Shavlik, J. (1994) Knowledge-based artificial neural networks. Artificial Intelligence 70(1–2):119–65.
Valiant, L (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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