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
Decentralised Structural Adaptation in Agent Organisations
Organized Adaption in Multi-Agent Systems
A multiagent approach to autonomous intersection management
Journal of Artificial Intelligence Research
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
A Comprehensive Survey of Multiagent Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Using experience to generate new regulations
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Humans usually use information about previous experiences to solve new problems. Following this principle, we propose an approach to enhance a multi-agent system by including an authority that generates new regulations whenever new conflicts arise. The authority uses a unsupervised version of classical Case-Based Reasoning to learn from previous similar situations and generate regulations that solve the new problem. The scenario used to illustrate and evaluate our proposal is a simulated traffic intersection where agents are traveling cars. A traffic authority observes the scenario and generates new regulations when collisions or heavy traffic are detected. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity in order to generate a set of regulations that, if followed, improve system performance. Empirical evaluation shows that the traffic authority succeeds in avoiding conflicting situations by automatically generating a reduced set of traffic rules.