Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Genetic Programming And Multi-agent Layered Learning By Reinforcements
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
breve: a 3D environment for the simulation of decentralized systems and artificial life
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Evolving teamwork and coordination with genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Heterogeneity in the coevolved behaviors of mobile robots: the emergence of specialists
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Emergence of collective behavior in evolving populations of flying agents
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
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Evolutionary models typically rely on a single level of evolution for training a team of cooperating agents. I present a model that evolves at two levels—an "organizational" level and the more traditional "individual" level. Each organization contains an embedded agent population that goes through a full evolutionary process every organizational time-step. The organization's genetic code is essentially a policy that specifies the training process for its embedded agents. It also defines the creation of a representative team that is compiled after each organizational time-step. An organization's fitness is based on the performance of this representative team.