Uncertainty in Artificial Intelligence
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Learning and Solving Many-Player Games through a Cluster-Based Representation
Sevan Ficici, David Parkes, Avi Pfeffer
Abstract:
In addressing the challenge of exponential scaling with the number of agents we adopt a cluster-based representation to approximately solve asymmetric games of very many players. A cluster groups together agents with a similar "strategic view" of the game. We learn the clustered approximation from data consisting of strategy profiles and payoffs, which may be obtained from observations of play or access to a simulator. Using our clustering we construct a reduced "twins" game in which each cluster is associated with two players of the reduced game. This allows our representation to be individually- responsive because we align the interests of every individual agent with the strategy of its cluster. Our approach provides agents with higher payoffs and lower regret on average than model-free methods as well as previous cluster-based methods, and requires only few observations for learning to be successful. The "twins" approach is shown to be an important component of providing these low regret approximations.
Keywords:
Pages: 188-195
PS Link:
PDF Link: /papers/08/p188-ficici.pdf
BibTex:
@INPROCEEDINGS{Ficici08,
AUTHOR = "Sevan Ficici and David Parkes and Avi Pfeffer",
TITLE = "Learning and Solving Many-Player Games through a Cluster-Based Representation",
BOOKTITLE = "Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-08)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2008",
PAGES = "188--195"
}


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