By Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe (auth.), H. Jaap van den Herik, Aske Plaat (eds.)
This booklet constitutes the completely refereed post-conference complaints of the thirteenth Advances in computing device video games convention, ACG 2011, held in Tilburg, The Netherlands, in November 2011. The 29 revised complete papers provided have been rigorously reviewed and chosen from various submissions. The papers hide quite a lot of issues akin to Monte-Carlo tree seek and its enhancement, temporal distinction studying, optimization, fixing and looking out, research of a online game attribute, new ways, and severe games.
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Extra info for Advances in Computer Games: 13th International Conference, ACG 2011, Tilburg, The Netherlands, November 20-22, 2011, Revised Selected Papers
The measurements are done on a 19 × 19 board. 1 Shared Memory Parallelization Historically, various thread parallelization approaches for MCTS have been explored . In Pachi, we use the in-tree parallelization, with multiple threads performing both the tree search and simulations in parallel on a shared tree and performing lock-free tree updates . To allocate all children of a given node, Pachi does not use a per-thread memory pool, but instead a pre-allocated global machines threads/mach.
000 Table 2. Percentage of all groups that performed equally or better than basic UCB with the same parameters. An asterisk marks where basic UCB was selecting the best node 100% of the time. 15* than the ﬁrst. 66% for basic UCB. 9. This was to make the problem harder and decrease the occurrence of 100% success rates seen previously. Basic UCB’s performance is shown in Table 3. 0. When comparing move groups to basic UCB without changing parameters, the results are quite similar to those found for the previous experiment shown in Table 2.
Best Arm Identiﬁcation in Multi-Armed Bandits. In: COLT 2010, pp. 41–53 (2010) 2. : Finite-time Analysis of the Multiarmed Bandit Problem. In: Machine Learning, pp. 235–256 (2002) 3. : Progressive Strategies for Monte-Carlo Tree Search. New Mathematics and Natural Computation (NMNC) 4, 343–357 (2008) 4. : Transpositions and Move Groups in Monte Carlo Tree Search. , Barone, L. ) IEEE Symposium on Computational Intelligence and Games, pp. 389–395. IEEE (December 2008) 5. : Monte-Carlo Tree Search and Rapid Action Value Estimation in Computer Go.
Advances in Computer Games: 13th International Conference, ACG 2011, Tilburg, The Netherlands, November 20-22, 2011, Revised Selected Papers by Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe (auth.), H. Jaap van den Herik, Aske Plaat (eds.)