M.A. Khan, D. Turgut, and L. Bölöni. Optimizing coalition formation for tasks with dynamically evolving rewards and nondeterministic action effects. In Proceedings of International Workshop on Optimisation in Multi-Agent Systems (OptMas08), in conjunction with the Seventh Joint Conference on Autonomous and Multi-Agent Systems (AAMAS 2008), pp. 69–76, May 2008.
We consider a problem domain where coalitions of agents are formed in order to execute tasks. Each task is assigned at most one coalition of agents, and the coalition can be reorganized during execution. Executing a task means bringing it into one of the desired terminal states, which might take several time steps. The state of the task evolves even if no coalition is assigned to its execution and depends nondeterministically on the cumulative actions of the agents in the coalition. Furthermore, we assume that the reward obtained for executing a task evolves in time: typically, the more delay in the execution, the lesser the reward. We exemplify this class of problems by the allocation of firefighters to fires in a disaster rescue environment. We describe a practical methodology through which the aspects of this problem can be encoded as a Markov Decision Process. An experimental study involving the Robocup Rescue simulator shows that a coalition formation policy developed following our methodology outperforms heuristic approaches.
@inproceedings{Khan-2008-OptMAS,
author = "M.A. Khan and D. Turgut and L. B{\"o}l{\"o}ni",
title = "Optimizing coalition formation for tasks with dynamically evolving
rewards and nondeterministic action effects",
booktitle = "Proceedings of International Workshop on Optimisation in
Multi-Agent Systems (OptMas08), in conjunction with the Seventh Joint
Conference on Autonomous and Multi-Agent Systems (AAMAS 2008)",
pages = "69-76",
month = "May",
year = "2008",
abstract = {
We consider a problem domain where coalitions of agents are formed in
order to execute tasks. Each task is assigned at most one coalition of
agents, and the coalition can be reorganized during execution. Executing a
task means bringing it into one of the desired terminal states, which
might take several time steps. The state of the task evolves even if no
coalition is assigned to its execution and depends nondeterministically on
the cumulative actions of the agents in the coalition. Furthermore, we
assume that the reward obtained for executing a task evolves in time:
typically, the more delay in the execution, the lesser the reward. We
exemplify this class of problems by the allocation of firefighters to
fires in a disaster rescue environment. We describe a practical
methodology through which the aspects of this problem can be encoded as a
Markov Decision Process. An experimental study involving the Robocup
Rescue simulator shows that a coalition formation policy developed
following our methodology outperforms heuristic approaches.
},
}
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