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Titlebook: Computers and Games; 9th International Co Aske Plaat,Walter Kosters,Jaap van den Herik Conference proceedings 2016 Springer International P

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樓主: inroad
31#
發(fā)表于 2025-3-26 23:00:00 | 只看該作者
https://doi.org/10.1007/978-3-642-40817-5of these algorithms, namely rectangular algorithms (fully parallel) and bandit algorithms (faster in a sequential setup). We check the performance on several board games and card games. In addition, in the case of Go, we check the methodology when the opponent is completely distinct to the one used in the training.
32#
發(fā)表于 2025-3-27 05:02:48 | 只看該作者
Conference proceedings 2016s), theoretical aspects and complexity (five papers) and cognition model (one paper). The paper Using Partial Tablebases in Breakthrough by Andrew Isaac and Richard Lorentz received the Best Paper Award..
33#
發(fā)表于 2025-3-27 05:47:43 | 只看該作者
0302-9743 (seven papers), concrete games (seven papers), theoretical aspects and complexity (five papers) and cognition model (one paper). The paper Using Partial Tablebases in Breakthrough by Andrew Isaac and Richard Lorentz received the Best Paper Award..978-3-319-50934-1978-3-319-50935-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
34#
發(fā)表于 2025-3-27 11:08:10 | 只看該作者
35#
發(fā)表于 2025-3-27 16:11:02 | 只看該作者
Pruning Playouts in Monte-Carlo Tree Search for the Game of Havannah,Thus, the method evaluates the estimated “good” moves more precisely. We have tested our improvement for the game of Havannah and compared it to several classic improvements. Our method outperforms the classic version of MCTS (with the RAVE improvement) and the different playout policies of MCTS that we have experimented.
36#
發(fā)表于 2025-3-27 21:40:47 | 只看該作者
37#
發(fā)表于 2025-3-28 00:41:02 | 只看該作者
38#
發(fā)表于 2025-3-28 03:20:55 | 只看該作者
A Class Grammar for General Games,rce code, from the constructors and associated parameters found along its class hierarchy, to give a context-free grammar that provides access to the underlying code while hiding its implementation details.
39#
發(fā)表于 2025-3-28 09:55:24 | 只看該作者
https://doi.org/10.1007/978-3-642-40817-5 in the search tree in which neural networks are used. All strategies are assessed using the freely available cuDNN library. We compare our strategies against an optimal upper bound which can be estimated by removing timing constraints. We show that the best strategies are only 50 ELO points worse than this upper bound.
40#
發(fā)表于 2025-3-28 12:05:41 | 只看該作者
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