Value-based Argumentation Frameworks as Neural-symbolic Learning Systems Export

@article{citeulike:434086, 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. 10.1093/logcom/exi057}, author = {D'Avila Garcez, Artur S. and Gabbay, Dov M. and Lamb, Luis C.}, citeulike-article-id = {434086}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/logcom/exi057}, citeulike-linkout-1 = {http://logcom.oxfordjournals.org/cgi/content/abstract/15/6/1041}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/oup/logcom/2005/00000015/00000006/art01041}, day = {1}, doi = {10.1093/logcom/exi057}, issn = {0955-792X}, journal = {J Logic Computation}, keywords = {argumentation-games, argumentation-networks, argumentation-systems, argumentative-frameworks, argumentative-games, argumentative-structures, argumentative-systems, argumentative-technologies, argumenting, logical-frameworks, logical-structures, neural-networks, neural-symbolic, neuronal-structures}, month = {December}, number = {6}, pages = {1041--1058}, posted-at = {2009-08-03 07:41:27}, priority = {2}, publisher = {Oxford University Press}, title = {Value-based Argumentation Frameworks as Neural-symbolic Learning Systems}, url = {http://dx.doi.org/10.1093/logcom/exi057}, volume = {15}, year = {2005} }

TY - JOUR ID - citeulike:434086 L3 - citeulike-article-id:434086 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. 10.1093/logcom/exi057 IS - 6 JF - J Logic Computation SN - 0955-792X EP - 1058 TI - Value-based Argumentation Frameworks as Neural-symbolic Learning Systems PB - Oxford University Press VL - 15 SP - 1041 KW - argumentation-games KW - argumentation-networks KW - argumentation-systems KW - argumentative-frameworks KW - argumentative-games KW - argumentative-structures KW - argumentative-systems KW - argumentative-technologies KW - argumenting KW - logical-frameworks KW - logical-structures KW - neural-networks KW - neural-symbolic KW - neuronal-structures AU - D'Avila Garcez, Artur AU - Gabbay, Dov AU - Lamb, Luis PY - 2005/12/01/ UR - http://dx.doi.org/10.1093/logcom/exi057 ER -