By Junichi Hashimoto, Akihiro Kishimoto, Kazuki Yoshizoe (auth.), H. Jaap van den Herik, Aske Plaat (eds.)
This publication constitutes the completely refereed post-conference lawsuits 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 offered have been conscientiously reviewed and chosen from various submissions. The papers conceal a variety of themes 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 critical games.
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Additional resources for Advances in Computer Games: 13th International Conference, ACG 2011, Tilburg, The Netherlands, November 20-22, 2011, Revised Selected Papers
To test the eﬀect of prior knowledge, groups were created initially given that the best three nodes are known. 0. Two grouping methods were tested – separating the best three nodes and grouping them together. 0. Initially, they performed worse than basic UCB. After 64 simulations, 22 G. Van Eyck and M. M¨ uller they started performing signiﬁcantly better. 0 often switched to selecting the second best node rather than the best node at higher simulations. 8% at 2048 simulations. Putting the best nodes in the same group had more consistent performance, but never better than basic UCB.
1 Prior Values When a node is expanded, child nodes for all the possible followup moves are created and pre-initialized in order to kick-start exploration within the node. 5 with the weight of several virtual simulations, we have observed this to be important for RAVE stability. The value is further adjusted by various heuristics, each contributing ε ﬁxed-result virtual simulations (“equivalent experience” ε = 20 on 19 × 19, ε = 14 on 9 × 9). ) 3 4 5 The default policy is called “UCT”, but this is only a traditional name; the UCT exploration term is not used by default anymore.
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. 5. 2 Cluster Parallelization The MCTS cluster parallelization is still far from being clearly solved. Pachi features elaborate support for distributed computations with information exchange between the nodes, but it still scales much slower when multiplying the number of nodes rather than processors with a low-latency shared tree.