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 court cases 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 rigorously reviewed and chosen from quite a few submissions. The papers hide quite a lot of themes 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 critical 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
Of course, the perfect move group will be deﬁned by the underlying payoﬀs of the children. Therefore, RQ2 is to ﬁnd whether there is a common structure among good move groups so that this might be applied to other problems. The third question (RQ3) is regarding the eﬃcacy of completely random move groups. RQ3: If we know absolutely nothing about the underlying nodes, will a random move grouping perform better than none at all? 4 Experimental Framework Given the questions to be answered, an artiﬁcial game called the ‘Multi-armed Bandit Game with Move Groups’ was created.
4%. The number of simulations was scaled up to 65536. Since there would be too many groups to test them exhaustively, 20 random groups as well Revisiting Move Groups in Monte-Carlo Tree Search 19 as two pre-deﬁned groups were generated. The ﬁrst pre-deﬁned group contained the nodes “in order,” that is, the best 6 children in one group, the next best 6 children in another group, and so on. The second pre-deﬁned group was the “distributed” group where the best 10 nodes were split among the groups.
All experiments were run 5000 times with average results shown. 1 Baseline Group Performance The ﬁrst experiment exhaustively tried every possible grouping of nine nodes into three groups of three, 280 combinations in total. This allowed for detailed comparison of groups with diﬀerent payoﬀs, and values of C and F P U . Each grouping along with standard UCB was run for 8192 simulations. 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.