Advances in Computer Games: 13th International Conference, by Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe

By Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe (auth.), H. Jaap van den Herik, Aske Plaat (eds.)

This e-book constitutes the completely refereed post-conference complaints of the thirteenth Advances in desktop video games convention, ACG 2011, held in Tilburg, The Netherlands, in November 2011. The 29 revised complete papers provided have been conscientiously reviewed and chosen from various submissions. The papers conceal quite a lot of themes equivalent to Monte-Carlo tree seek and its enhancement, temporal distinction studying, optimization, fixing and looking, research of a online game attribute, new ways, and severe games.

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This has two motivations — first, with multiple interesting sequences available on the board, we want to ensure the tree does not swap between situations randomly but instead reads each sequence properly. Second, this is well rooted in the traditional Go strategy where a large portion of moves is indeed “sente”, requiring a local reply. – We give joseki moves twice the default ε (using the joseki dictionary described below). This currently has no performance impact. 28 P. -l. Gailly Table 3. Elo performance of some playout heuristics on 19 × 19 Heuristic Low-end Mid-end High-end w/o Capture −563 ± 106 −700 −949 w/o 2-lib.

Amazons Discover Monte-Carlo. M. ) CG 2008. LNCS, vol. 5131, pp. 13–24. Springer, Heidelberg (2008) 8. : Fuego at the Computer Olympiad in Pamplona 2009: a Tournament Report. Technical report, University of Alberta, Dept. of Computing Science, TR09-09 (May 2009) 9. : Grouping Nodes for Monte-Carlo Tree Search. In: Computer Games Workshop 2007, pp. 276–283 (2007) 10. : Modifications of UCT and Sequence-Like Simulations for Monte-Carlo Go. In: IEEE Symposium on Computational Intelligence and Games, pp.

7 had an overall performance between the other two experiments and therefore similar conclusions apply. 5 decreased the performance of all groups and basic UCB. 5 had little effect with the largest deviation being 3%. 3 Grouping in Games with Large Branching Factor In order to check whether the best performing groups would have a similar structure with a larger number of children, the experiment was slightly modified for 60 nodes and 10 move groups of 6 nodes each. 2%. 4%. The number of simulations was scaled up to 65536.

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