[en] In this paper we propose a neural-symbolic architecture to represent and reason with norms in multi-agent systems. On the one hand, the architecture contains a symbolic knowledge base to represent norms and on the other hand it contains a neural network to reason with norms. The interaction between the symbolic knowledge and the neural network is used to learn norms. We describe how to handle normative reasoning issues like contrary to duties, dilemmas and exceptions by using a priority-based ordering between the norms in a neural-symbolic architecture.
VAN DER TORRE, Leon ; University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
External co-authors :
yes
Language :
English
Title :
Neural Symbolic Architecture for Normative Agents
Publication date :
2011
Event name :
Artificial Agents and Multi-Agents Systems 2011
Event date :
May 2-6 2011
Audience :
International
Main work title :
In Proceedings of The Seventh International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2011). May, 2011
