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- W2067348515 abstract "A cooperative team of agents may perform many tasks better than isolated agents. The question is how coopera- tion among self-interested agents may be achieved. It is impor- tant that, while we encourage cooperation among agents to form a team, we maintain autonomy of individual agents as much as possible, so as to maintain flexibility and generality. This paper presents an approach toward this goal, based on bidding utiliz- ing reinforcement values acquired through reinforcement learn- ing. The result is a simple and straightforward method that is generic and works in a variety of task domains. We further apply evolutionary computation to enhance cooperation among agents of a team, through selecting and reproducing those teams that are able to cooperate. We tested and analyzed this ap- proach, MARLBS, in a variety of task domains, and demon- strated that a team of self-interested agents indeed performed better than the best single agent as well as the average of the single agents. In particular, Backgammon players trained us- ing this approach outperformed PubEval (a publicly available benchmark player). These results validated our approach." @default.
- W2067348515 created "2016-06-24" @default.
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- W2067348515 date "2005-01-01" @default.
- W2067348515 modified "2023-10-16" @default.
- W2067348515 title "MARLBS: Team Cooperation through Bidding" @default.
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- W2067348515 doi "https://doi.org/10.5019/j.ijcir.2005.22" @default.
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