Value-based argumentation frameworks as neural-symbolic learning systems

Garcez, A., Gabbay, D. M. & Lamb, L. C. (2005). Value-based argumentation frameworks as neural-symbolic learning systems. Journal of Logic and Computation, 15(6), pp. 1041-1058. doi: 10.1093/logcom/exi057

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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.

Item Type: Article
Uncontrolled Keywords: neural-symbolic systems, value-based argumentation frameworks, hybrid system
Subjects: B Philosophy. Psychology. Religion > BC Logic
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Informatics > Department of Computing

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