In this paper we propose a neural-symbolic architecture to representand reason with norms in multi-agent systems. On the one hand,the architecture contains a symbolic knowledge base to representnorms and on the other hand it contains a neural network to rea-son with norms. The interaction between the symbolic knowledgeand the neural network is used to learn norms. We describe howto handle normative reasoning issues like contrary to duties, dilem-mas and exceptions by using a priority-based ordering between thenorms in a neural-symbolic architecture.

Neural symbolic architecture for normative agents

BOELLA, Guido;COLOMBO TOSATTO, SILVANO;Genovese, Valerio;IENCO, Dino;
2011

Abstract

In this paper we propose a neural-symbolic architecture to representand reason with norms in multi-agent systems. On the one hand,the architecture contains a symbolic knowledge base to representnorms and on the other hand it contains a neural network to rea-son with norms. The interaction between the symbolic knowledgeand the neural network is used to learn norms. We describe howto handle normative reasoning issues like contrary to duties, dilem-mas and exceptions by using a priority-based ordering between thenorms in a neural-symbolic architecture.
Autonomous Agents and Multiagent Systems (AAMAS) 2011
Taipei, Taiwan
02/05/11-06/05/11
10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011), Taipei, Taiwan, May 2-6, 2011, Volume 1-3
Liz Sonenberg, Peter Stone, Kagan Tumer, Pinar Yolum
1-3
1203
1204
9780982657157
Norms; Computational architectures for learning; Emergent behav-ior; Logic-based approaches and methods
Guido Boella; Silvano Colombo Tosatto; Artur S. d'Avila Garcez; Valerio Genovese; Dino Ienco; Leendert van der Torre:
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/98464
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