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Download Computers and Games: 8th International Conference, CG 2013, by H. Jaap van den Herik, Hiroyuki Iida, Aske Plaat PDF

By H. Jaap van den Herik, Hiroyuki Iida, Aske Plaat

This publication constitutes the completely refereed post-conference lawsuits of the eighth foreign convention on desktops and video games, CG 2013, held in Yokohama, Japan, in August 2013, along with the seventeenth desktop and video games match and the 20 th global Computer-Chess Championship. The 21 papers awarded have been conscientiously reviewed and chosen for inclusion during this publication. They disguise quite a lot of subject matters that are grouped into 5 sessions: Monte Carlo Tree seek and its improvements; fixing and looking out; research of online game attribute; new methods; and severe games.

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Extra resources for Computers and Games: 8th International Conference, CG 2013, Yokohama, Japan, August 13-15, 2013, Revised Selected Papers

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Accordingly, the score range might become asymmetric. On Semeai Detection in Monte-Carlo Go 17 that achieved a final score of s ∈ S, hence s∈S H(s) = n. We denote the average score of n simulations by s = s∈S H(s)/n. Each element c of the set of score clusters C is itself an interval of scores, hence c ⊆ S. All clusters are disjunct in respect to the score sets they represent. We write c(s) for the single cluster to which a score s is assigned to. 2 Clustering As mentioned in Sect. 2 we use a mean-shift algorithm for mode seeking and clustering in score histograms that depends on the choice of a kernel and a parameter h that is called bandwidth.

J. van den Herik et al. ): CG 2013, LNCS 8427, pp. 39–48, 2014. -C. Huang and M. M¨ uller Four Classes and Three Research Questions Three main components influencing the strength of current Go programs are (a) their simulation policy, (b) their tree search, and their (c) other in-tree knowledge, such as patterns. If we are trying to evaluate Go programs indirectly, without analyzing their source code, we can use test positions, which can be classified as follows. 1. well-suited positions: MCTS programs can solve them with either a good search, or with a good policy, or both.

For each board, the probability P (mj ) that the move mj was played can be written as a function of the γ values: γi P (mj ) = f eature i ∈ mj ( γi ) (2) legal moves m f eature i ∈ m Then, we compute the γ values that maximize the following likelihood estin mator: L({γi }) = j=1 P (mj ). In our current implementation, we use a variation of a gradient ascent method to find the likely maximum, but other methods have been proposed, in particular minorization-maximization algorithms [3]. The global efficiency of the learned Bradley-Terry model can be checked on a set of games between professional players, by evaluating if the moves played 30 K.

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