Advances in Computer Games: 12th International Conference, by Guillaume Chaslot, Christophe Fiter, Jean-Baptiste Hoock

By Guillaume Chaslot, Christophe Fiter, Jean-Baptiste Hoock (auth.), H. Jaap van den Herik, Pieter Spronck (eds.)

This quantity constitutes the completely refereed post-conference complaints of the 12th Advances in desktop video games convention, ACG 2009, held in Pamplona, Spain, in may perhaps 2009. The 20 revised complete papers offered have been conscientiously reviewed and chosen from forty-one submissions for inclusion within the e-book. the subjects addressed comprise Monte-Carlo tree seek, Bayesian modeling, selective seek, brute strength, clash answer, fixing video games, optimization, suggestion discovery, incongruity conception, and information insurance.

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Additional info for Advances in Computer Games: 12th International Conference, ACG 2009, Pamplona Spain, May 11-13, 2009. Revised Papers

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The selection strategy selects the child k of the node p that satisfies Formula 1: k ∈ argmaxi∈I vi + C × ln np W × Pmc + ni ni + 1 , (1) where vi is the value of the node i, ni is the visit count of i, and np is the visit mc count of p. C is a coefficient, which must be tuned experimentally. Wn×P is i +1 the PB part of the formula. W is a constant, which must be set manually (here W = 10). Pmc is the transition probability of a move category mc [15]. , capture, blocking) the probability that a move belonging to that category will be played is determined.

It consists of building multiple MCTS trees in parallel, with one thread per tree. These threads do not share information with each other. When the available time is up, all the root children of the separate MCTS trees are merged with their corresponding clones. For each group of clones, the scores of all games played are added. M. Winands and Y. Bj¨ ornsson total, the best move is selected. This parallelization method only requires a minimal amount of communication between threads, so the parallelization is easy, even on a cluster.

Backpropagation is the procedure that propagates the result of a simulated game k back from the leaf node L, through the previously traversed node, all the way up to the root. The result is scored positively (Rk = +1) if the game is won, and negatively (Rk = −1) if the game is lost. Draws lead to a result Rk = 0. A backpropagation strategy is applied to the value vL of a node. , vL = ( k Rk )/nL . In addition to backpropagating the values {1,0,−1}, MCTS-Solver also propagates the game-theoretical values ∞ or −∞.

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