Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms
Deadline Scheduling for Real-Time Systems: Edf and Related Algorithms
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
An Application of Automated Negotiation to Distributed Task Allocation
IAT '07 Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology
A multi-agent simulation system for prediction and scheduling of aero engine overhaul
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Decentralized control of cooperative systems: categorization and complexity analysis
Journal of Artificial Intelligence Research
Sequential bundle-bid single-sale auction algorithms for decentralized control
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Benchmarking hybrid algorithms for distributed constraint optimisation games
Autonomous Agents and Multi-Agent Systems
The Knowledge Engineering Review
Local coordination in online distributed constraint optimization problems
EUMAS'11 Proceedings of the 9th European conference on Multi-Agent Systems
RMASBench: benchmarking dynamic multi-agent coordination in urban search and rescue
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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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.