By Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe (auth.), H. Jaap van den Herik, Aske Plaat (eds.)
This booklet constitutes the completely refereed post-conference court cases of the thirteenth Advances in machine 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 conceal a variety of issues corresponding 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 resources for Advances in Computer Games: 13th International Conference, ACG 2011, Tilburg, The Netherlands, November 20-22, 2011, Revised Selected Papers
At various intervals, the node that would have been selected was tracked. Performance was measured by the percentage of time that the best node was selected. In practice, knowing the exact value or ranking of nodes is unrealistic, but this experiment provides a baseline to study the potential of groups. 9. For comparison, results of UCB without move groups are given in Table 1. 22% for 75%. 0 at lower simulation counts and slightly worse at higher simulation counts. To look at the eﬀect of introducing move groups without changing any parameters, the performance of basic UCB was compared to all move groups with the same parameters.
Applying approaches to such games remains a topic for future work. Revisiting Move Groups in Monte-Carlo Tree Search 23 References 1. : 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.
RAVE has been used in many Go programs , including Fuego, MoGo, and Erica, to great successes. Using RAVE modiﬁes the original UCT algorithm to include an additional term that is based on statistics gathered for playing a stone at any point in the game, rather than just considering the next move. When looking for enhancements that are not game-speciﬁc, an example is iterative widening. With iterative widening, the goal is to begin searching in a subset of possible moves, then gradually widening the search window if time allows.