On social laws for artificial agent societies: off-line design
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Multi-agent system adaptation in a peer-to-peer scenario
Proceedings of the 2009 ACM symposium on Applied Computing
A framework for monitoring agent-based normative systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Automated norm synthesis in an agent-based planning environment
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Integrating organizational control into multi-agent learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Role model based mechanism for norm emergence in artificial agent societies
COIN'07 Proceedings of the 2007 international conference on Coordination, organizations, institutions, and norms in agent systems III
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Exploiting domain knowledge to improve norm synthesis
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Norm diversity and emergence in tag-based cooperation
COIN@AAMAS'10 Proceedings of the 6th international conference on Coordination, organizations, institutions, and norms in agent systems
Evaluating the effectiveness of exploration and accumulated experience in automatic case elicitation
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Both human and multi-agent societies are prone to best function with the inclusion of regulations. Human societies have developed jurisprudence as the theory and philosophy of law. Within it, utilitarianism has the view that laws should be crafted so as to produce the best consequences. Following this same objective, we propose an approach to enhance a multi-agent system with a regulatory authority that generates new regulations -norms- based on the outcome of previous experiences. These regulations are learned by applying a machine learning technique (based on Case-Based Reasoning) that uses previous experiences to solve new problems. As a scenario to evaluate this innovative proposal, we use a simplified version of a traffic simulation scenario, where agents move within a road junction. Gathered experiences can then be easily mapped into regular traffic rules that, if followed, happen to be effective in avoiding undesired situations --and promoting desired ones. Thus, we can conclude that our approach can be successfully used to create new regulations for those multi-agent systems that accomplish two general conditions: to be able to continuously gather and evaluate experiences from its regular functioning; and to be characterized in such a way that similar social situations require similar regulations.