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Coherent Inference on Optimal Play in Games

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The search for an optimal path through a game tree is one of the oldest problems in computer science. Over the past years, Monte Carlo tree search has emerged as a surprisingly effective approach to this problem. I will present a probabilistic generative model for game trees that explains why MC tree search works at all. I will then move on to derive an approximate inference algorithm for this model, which can infer beliefs over the value of any node in the tree under optimal play, using random roll-out data from other parts in the tree. Somewhat surprisingly, this inference algorithm is of linear complexity, even though the exact search problem has exponential cost.

The work presented in this talk has just been published as P. Hennig, D. Stern, T. Graepel: “Coherent Inference on Optimal Play in Game Trees”, J Machine Learning Research, W&CP 9 (2010), 326-333.


This talk is part of the Inference Group series.

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