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• W2017590745 abstract "This paper reports on a novel decentralized technique for planning agent schedules in dynamic task allocation problems. Specifically, we use a stochastic game formulation of these problems in which tasks have varying hard deadlines and processing requirements. We then introduce a new technique for approximating this game using a series of static potential games, before detailing a decentralized method for solving the approximating games that uses the distributed stochastic algorithm. Finally, we discuss an implementation of our approach to a task allocation problem in the RoboCup Rescue disaster management simulator. The results show that our technique performs comparably to a centralized task scheduler (within 6% on average), and also, unlike its centralized counterpart, it is robust to restrictions on the agents’ communication and observation ranges." @default.
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• W2017590745 date "2010-03-12" @default.
• W2017590745 modified "2023-10-18" @default.
• W2017590745 title "Decentralized Dynamic Task Allocation Using Overlapping Potential Games" @default.
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• W2017590745 doi "https://doi.org/10.1093/comjnl/bxq023" @default.
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