Multiagent systems
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Optimizing Production Manufacturing Using Reinforcement Learning
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Multi-Machine Scheduling - A Multi-Agent Learning Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Sequential optimality and coordination in multiagent systems
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Being Reactive by Exchanging Roles: An Empirical Study
Balancing Reactivity and Social Deliberation in Multi-Agent Systems, From RoboCup to Real-World Applications (selected papers from the ECAI 2000 Workshop and additional contributions)
Engineering Applications of Artificial Intelligence
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Social behaviour in intelligent agent systems is often considered to be achieved by deliberative, in-depth reasoning techniques. This paper shows, how a purely reactive multi-agent system can learn to evolve cooperative behaviour, by means of learning from previous experiences. In particular, we describe a learning multi agent approach to the problem of controlling power flow in an electrical power-grid. The problem is formulated within the framework of dynamic programming. Via a global optimization goal, a set of individual agents is forced to autonomously learn to cooperate and communicate. The ability of the purely reactive distributed systems to solve the global problem by means of establishing a communication mechanism is shown on two prototypical network configurations.