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Learning and Solving Many-Player Games through a Cluster-Based Representation

Learning and Solving Many-Player Games through a Cluster-Based Representation

This video was recorded at 24th Conference on Uncertainty in Artificial Intelligence (UAI), Helsinki 2008. 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... Show More

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