By Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe (auth.), H. Jaap van den Herik, Aske Plaat (eds.)
This publication constitutes the completely refereed post-conference court cases of the thirteenth Advances in laptop video games convention, ACG 2011, held in Tilburg, The Netherlands, in November 2011. The 29 revised complete papers offered have been rigorously reviewed and chosen from various submissions. The papers hide a variety of issues resembling Monte-Carlo tree seek and its enhancement, temporal distinction studying, optimization, fixing and looking out, research of a online game attribute, new techniques, and critical 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
A dynamic way of determining n may make the improvement more pronounced in the future. 5 Parallelization Pachi supports both shared memory parallelization and cluster parallelization. It is highly scalable with more time or more threads, and scales relatively well with more cluster nodes. Fig. 4 shows the general scaling of Pachi. In all the distributed experiments and most single machine experiments, Fuego and Pachi both use 16 threads per machine and a ﬁxed number of playouts. The measurements are done on a 19 × 19 board.
The best performing groups had the second structure more often Revisiting Move Groups in Monte-Carlo Tree Search 17 Table 1. 9. 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.
In section 2, we brieﬂy describe the Pachi software. In section 3, we detail the MCTS algorithm and the implementation and heuristics used in Pachi. Section 4 contains a summary of our original extensions to the MCTS algorithm — an adaptive time control algorithm (Sec. 1), the dynamic komi method (Sec. 2) and our usage of the criticality statistic (Sec. 3). In section 5, we detail our scaling improvements, especially the strategy behind our distributed game tree search. Then section 6 summarizes Pachi’s performance in the context of the whole Computer Go ﬁeld.