By Aske Plaat, Jaap van den Herik, Walter Kosters
This ebook constitutes the completely refereed post-conference proceedings of the 14th foreign convention on Advances in desktop video games, ACG 2015, held in Leiden, The Netherlands, in July 2015. The 22 revised complete papers provided have been rigorously reviewed and selected from 34 submissions. The papers hide a variety of topics corresponding to Monte-Carlo Tree seek and its improvements; theoretical features and complexity; research of online game features; seek algorithms; and laptop studying.
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Additional resources for Advances in Computer Games: 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015: Revised Selected Papers
7 % against random playouts in Go. Move-Average Sampling Technique (MAST) is a technique used in the GGP program Cadia Player so as to bias the playouts with statistics on moves [10,11]. It consists of choosing a move in the playout proportionally to the exponential of its mean. MAST keeps the average result of each action over all simulations. Moves found to be good on average, independent of a game state, will get higher values. In the playout step, the action selections are biased towards selecting such moves.
1), implicit minimax backups in Subsect. 2), and quality-based rewards in Subsect. 3). 1 Early Playout Terminations For CDC, it is important to terminate playouts earlier for two reasons, speedup and accuracy of simulated results. The ﬁrst reason is obvious. The second is that it is more accurate to terminate playouts at the time when they are very likely to win, lose, or draw. For example, it is normally a draw in a situation, Gmm, where Red has only one guard and Black has two ministers. In the case of keeping playing in the playout, Black may lose accidentally, while it is supposed to be a draw since G cannot capture either m.
IEEE Trans. Comput. Intell. AI Games 6(4), 367–381 (2014) 27. : N-grams and the last-good-reply policy applied in general game playing. IEEE Trans. Comput. Intell. AI Games 4(2), 73–83 (2012) 28. : Perfectly solving domineering boards. , Lida, H. ) CGW 2013. Communications in Computer and Information Science, vol. 408, pp. 97–121. tw Abstract. Monte-Carlo tree search (MCTS) has been successfully applied to Chinese dark chess (CDC). In this paper, we study how to improve and analyze the playing strength of an MCTS-based CDC program, named DARKKNIGHT, which won the CDC tournament in the 17th Computer Olympiad.
Advances in Computer Games: 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015: Revised Selected Papers by Aske Plaat, Jaap van den Herik, Walter Kosters