Decentralised dynamic task allocation: a practical game: theoretic approach

This paper reports on a novel decentralised technique for planning agent schedules in dynamic task allocation problems. Specifically, we use a Markov game formulation of these problems for tasks with 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 decentralised solution method for 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. Our results show that our technique performs comparably to a centralised task scheduler (within 6% on average), and also, unlike its centralised counterpart, it is robust to restrictions on the agents’ communication and observation range. Categories and Subject Descriptors I.2.8 [Problem Solving, Control Methods, and Search]: Scheduling; I.2.11 [Distributed Artificial Intelli...

• Type: Conference
• Year: 2009
• Where: ATAL
• Authors: Archie C. Chapman, Rosa Anna Micillo, Ramachandra Kota, Nicholas R. Jennings
• Keywords: Artificial Intelligence, ATAL 2009, Centralised Task Scheduler, Dynamic Task Allocation, Task Allocation Problem