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 booklet constitutes the completely refereed post-conference lawsuits of the thirteenth Advances in desktop video games convention, ACG 2011, held in Tilburg, The Netherlands, in November 2011. The 29 revised complete papers offered have been conscientiously reviewed and chosen from a variety of submissions. The papers disguise a variety of issues reminiscent of 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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Extra resources for Advances in Computer Games: 13th International Conference, ACG 2011, Tilburg, The Netherlands, November 20-22, 2011, Revised Selected Papers

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Used disjoint groups composed first of moves within two points of the last move, then moves on the border of the game board, and lastly a group with all other moves [9]. Besides finding an increase in playing performance, they note that their player seems to shift focus better and play non-local moves when appropriate. Given the need for a general enhancement when working with UCT, this paper explores move groups in detail. After analyzing the results from several experiments, an overall structure of move groups that perform well is found.

5. Strength variation with handicap Fig. 6. Strength variation with komi 800 400 600 200 400 Elo Elo 200 0 0 -200 -200 vs. self n/2 cores vs. Fuego -400 -600 1 2 4 8 16 22 -400 vs. self n/2 sims vs. 5 125 250 500 cores playouts (thousands) Fig. 7. Self-play scalability (fixed time) Fig. 8. Self-play scalability (fixed sims) Fig. , the increase in playing time needed to achieve identical strength play [6,20]. The tests are done using the “high-end” scenario, but Pachi uses a variable number of cores.

159–174. Kluwer Academic (2003) 3. pdf 4. : Bandit algorithms for tree search. In: Proc. of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI 2007), pp. 67–74. AUAI press (2007) 5. : Computing Elo ratings of move patterns in the game of Go. ICGA Journal 30(4), 198–208 (2007) 6. : Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search. ) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007) 7. : Fuego - an open-source framework for board games and Go engine based on Monte-Carlo tree search.

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