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Journal of Logic and Computation 2005 15(6):1041-1058; doi:10.1093/logcom/exi057
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Vol. 15 No. 6, © The Author, 2005. Published by Oxford University Press. All rights reserved.

Original Articles

Value-based Argumentation Frameworks as Neural-symbolic Learning Systems

Artur S. D'Avila Garcez1, Dov M. Gabbay2 and Luis C. Lamb3

1 Department of Computing, City University London, London EC1V 0HB, UK. Email: aag{at}soi.city.ac.uk, 2 Department of Computer Science, King's College London, Strand, London WC2R 2LS, UK. Email: dg{at}dcs.kcl.ac.uk, 3 Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, 91501-970, Brazil. Email: LuisLamb{at}acm.org

While neural networks have been successfully used in a number of machine learning applications, logical languages have been the standard for the representation of argumentative reasoning. In this paper, we establish a relationship between neural networks and argumentation networks, combining reasoning and learning in the same argumentation framework. We do so by presenting a new neural argumentation algorithm, responsible for translating argumentation networks into standard neural networks. We then show a correspondence between the two networks. The algorithm works not only for acyclic argumentation networks, but also for circular networks, and it enables the accrual of arguments through learning as well as the parallel computation of arguments.

Keywords: Neural-symbolic systems, value-based argumentation frameworks, hybrid systems

Received 6 October 2004.


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