Value-based argumentation frameworks as neural-symbolic learning systems

A S Avila Garcez, D M Gabbay, L C Lamb

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)

Abstract

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
Original languageEnglish
Pages (from-to)1041 - 1058
Number of pages18
JournalJournal of Logic and Computation
Volume15
Issue number6
DOIs
Publication statusPublished - Dec 2005

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