Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
Learning and reasoning about norms using neural-symbolic systems
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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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.