An examination of distributed planning in the world of air traffic control
Journal of Parallel and Distributed Computing
Coherent cooperation among communicating problem solvers
IEEE Transactions on Computers
Learning in embedded systems
The evolution of strategies for multiagent environments
Adaptive Behavior
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Simulation results for a new two-armed bandit heuristic
Proceedings of a workshop on Computational learning theory and natural learning systems (vol. 1) : constraints and prospects: constraints and prospects
PALO: a probabilistic hill-climbing algorithm
Artificial Intelligence
Coordination techniques for distributed artificial intelligence
Foundations of distributed artificial intelligence
Parallel, Distributed and Multiagent Production Systems
Parallel, Distributed and Multiagent Production Systems
Coordination of Distributed Problem Solvers
Coordination of Distributed Problem Solvers
On the Efficient Allocation of Resources for Hypothesis Evaluation: A Statistical Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiagent Coordination with Learning Classifier Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Evolving Beharioral Strategies in Predators and Prey
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Composer: A decision-theoretic approach to adaptive problem solving
Composer: A decision-theoretic approach to adaptive problem solving
Autonomous Agents that Learn to Better Coordinate
Autonomous Agents and Multi-Agent Systems
Learning procedural knowledge to better coordinate
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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A central issue in the design of cooperative multiagent systems is how to coordinate the behavior of the agents to meet the goals of thedesigner. Traditionally, this had been accomplished by hand-coding thecoordination strategies. However, this task is complex due to theinteractions that can take place among agents. Recent work in the areahas focused on how strategies can be learned. Yet, many of these systems suffer from convergence, complexity and performance problems.This paper presents a new approach for learning multiagentcoordination strategies that addresses these issues. The effectivenessof the technique is demonstrated using a synthetic domain and thepredator and prey pursuit problem.