Monitoring deployed agent teams
Proceedings of the fifth international conference on Autonomous agents
Collaborative Multi-robot Localization
KI '99 Proceedings of the 23rd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Combinatorial Auctions: A Survey
INFORMS Journal on Computing
Journal of Artificial Intelligence Research
Bidding languages for combinatorial auctions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Designing the HRTeam framework: lessons learned from a rough-and-ready human/multi-robot team
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
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We are exploring the use of auction mechanisms to assign roles within a team of agents operating in a dynamic environment. Depending on the degree of collaboration between the agents and the specific auction policies employed, we can obtain varying combinations of role assignments that can affect both the speed and the quality of task execution. In order to examine this extremely large set of combinations, we have developed a theoretical framework and an environment in which to experiment and evaluate the various options in policies and levels of collaboration. This paper describes our framework and experimental environment. We present results from examining a set of representative policies within our test domain — a high-level simulation of the RoboCup four-legged league soccer environment.