Handbook of logic in artificial intelligence and logic programming (vol. 3)
Knowledge-based artificial neural networks
Artificial Intelligence
On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural-Symbolic Learning System: Foundations and Applications
Neural-Symbolic Learning System: Foundations and Applications
The Connectionist Inductive Learning and Logic Programming System
Applied Intelligence
Permission and authorization in normative multiagent systems
ICAIL '05 Proceedings of the 10th international conference on Artificial intelligence and law
Normative framework for normative system change
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Emergence of norms through social learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Neural symbolic architecture for normative agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
A game theoretic approach to contracts in multiagent systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper we provide a neural-symbolic framework to model, reason about and learn norms in multi-agent systems. To this purpose, we define a fragment of Input/Output (I/O) logic that can be embedded into a neural network. We extend d'Avila Garcez et al. Connectionist Inductive Learning and Logic Programming System (CILP) to translate an I/O logic theory into a Neural Network (NN) that can be trained further with examples: we call this new system Normative-CILP (N-CILP). We then present a new algorithm to handle priorities between rules in order to cope with normative issues like Contrary to Duty (CTD), Priorities, Exceptions and Permissions. We illustrate the applicability of the framework on a case study based on RoboCup rules: within this working example, we compare the learning capacity of a network built with N-CILP with a non symbolic neural network, we explore how the initial knowledge impacts on the overall performance, and we test the NN capacity of learning norms, generalizing new Contrary to Duty rules from examples.