The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Eugenic Evolution Utilizing A Domain Model
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Coordinated Reinforcement Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Coordinating multi-rover systems: evaluation functions for dynamic and noisy environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Robotics: Science and Systems I
Robotics: Science and Systems I
Robustness in cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Distributed evaluation functions for fault tolerant multi-rover systems
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Generative encoding for multiagent learning
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Efficient evaluation functions for evolving coordination
Evolutionary Computation
Analyzing and visualizing multiagent rewards in dynamic and stochastic domains
Autonomous Agents and Multi-Agent Systems
Enhancing MAS cooperative search through coalition partitioning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An integrated multilevel learning approach to multiagent coalition formation
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Dynamics of coalition formation in combinatorial trading
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Improving coevolutionary search for optimal multiagent behaviors
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
Agent fitness functions for evolving coordinated sensor networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Self-organization and specialization in multiagent systems through open-ended natural evolution
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Shaping fitness functions for coevolving cooperative multiagent systems
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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Evolving multiple robots so that each robot acting independently can contribute to the maximization of a system level objective presents significant scientific challenges. For example, evolving multiple robots to maximize aggregate information in exploration domains (e.g., planetary exploration, search and rescue) requires coordination, which in turn requires the careful design of the evaluation functions. Additionally, where communication among robots is expensive (e.g., limited power or computation), the coordination must be achieved passively, without robots explicitly informing others of their states/intended actions. Coevolving robots in these situations is a potential solution to producing coordinated behavior, where the robots are coupled through their evaluation functions. In this work, we investigate coevolution in three types of domains: (i) where precisely n homogeneous robots need to perform a task; (ii) where n is the optimal number of homogeneous robots for the task; and (iii) where n is the optimal number of heterogeneous robots for the task. Our results show that coevolving robots with evaluation functions that are locally aligned with the system evaluation significantly improve performance over robots evolving using the system evaluation function directly, particularly in dynamic environments.