Value-based argumentation frameworks as neural-symbolic learning systems

TY - JOUR

T1 - Value-based argumentation frameworks as neural-symbolic learning systems

AU - Avila Garcez, A S

AU - Gabbay, D M

AU - Lamb, L C

PY - 2005/12

Y1 - 2005/12

N2 - 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

AB - 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

U2 - 10.1093/logcom/exi057

DO - 10.1093/logcom/exi057

M3 - Article

SN - 1465-363X

VL - 15

SP - 1041

EP - 1058

JO - Journal of Logic and Computation

JF - Journal of Logic and Computation

IS - 6

ER -