Decentralized Dynamic Task Allocation Using Overlapping Potential Games

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.