Advances in Computer Games: 14th International Conference, by Aske Plaat, Jaap van den Herik, Walter Kosters

By Aske Plaat, Jaap van den Herik, Walter Kosters

This publication constitutes the completely refereed post-conference proceedings of the 14th foreign convention on Advances in computing device video games, ACG 2015, held in Leiden, The Netherlands, in July 2015. The 22 revised complete papers provided have been conscientiously reviewed and selected from 34 submissions. The papers conceal a variety of topics equivalent to Monte-Carlo Tree seek and its improvements;  theoretical points and complexity; research of online game features; seek algorithms; and laptop studying.

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Extra resources for Advances in Computer Games: 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015: Revised Selected Papers

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Intell. AI Games 2(4), 303–309 (2010) 3. : Effect of LGRF on the playing strength agains gnugo. Website 15 June 2012. org/msg05060. html. Accessed 09 March 2015 4. : PACHI: state of the art open source go program. , Plaat, A. ) ACG 2011. LNCS, vol. 7168, pp. 24–38. Springer, Heidelberg (2012) 5. : Stochastic gradient tricks. -R. ) Neural Networks, Tricks of the Trade, Reloaded. Lecture Notes in Computer Science, vol. 7700, pp. 430–445. Springer, Heidelberg (2012) Adaptive Playouts in Monte-Carlo Tree Search 11 6.

Cazenave In the beginning PPA starts with a uniform playout policy. All the weights are set to zero. Then, after each playout, it adapts the policy of the winner of the playout. The principle is the same as the adaptation of NRPA except that it only adapts the policy of the winner of the playout with the moves of the winner. The PPA-playout algorithm is given in Algorithm 1. It takes as parameters the board, the next player, and the playout policy. The playout policy is an array of real numbers that contains a number for each possible move.

Published a paper [8] about alpha-beta search in their CDC program, FLIPPER. Some more research work for CDC includes opening books [7], endgame databases [9, 10, 25], solving smaller CDC [6] and game variations [15]. Recently, MCTS has been incorporated into CDC game-playing programs [16, 17, 33]. MCTS [5] is a best-first search algorithm on top of a search tree, named UCT [18], using Monte-Carlo simulations as state evaluations. It has been successfully applied to several games, such as Go [11, 13, 14], General Game Playing (GGP) [3], Backgammon [29] and Phantom-Go [4].

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