Read e-book online Advances in Computer Games: 14th International Conference, PDF

By Aske Plaat, Jaap van den Herik, Walter Kosters

ISBN-10: 3319279912

ISBN-13: 9783319279916

ISBN-10: 3319279920

ISBN-13: 9783319279923

This ebook constitutes the completely refereed post-conference lawsuits of the 14th foreign convention on Advances in laptop video games, ACG 2015, held in Leiden, The Netherlands, in July 2015.

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Read Online or Download Advances in Computer Games: 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015, Revised Selected Papers PDF

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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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We find the same is true with EPT. Rather than somehow use the value of the evaluation function, we have always obtained the best results 18 R. Lorentz by simply treating the evaluation as a boolean function, reporting either a win or a loss. In Sect. 1 mention was made of the fact that EPT, as well as MCTS, can get stuck on a bad move simply because there is not enough time for the refutation of this weak move to achieve sufficient visits. Even though this seems like a problem mainly for real-time play, we find the problem carries over even for turn-based play where we sometimes allow as much as 15 min of thinking time.

4(3), 343–357 (2008) 8. : Combining online and offline knowledge in UCT. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 273–280, New York (2007) 9. : Common fate graph patterns in Monte Carlo tree search for computer go. In: 2014 IEEE Conference on Computational Intelligence and Games (CIG), pp. 1–8, August 2014 10. : Monte-carlo simulation balancing in practice. , Plaat, A. ) CG 2010. LNCS, vol. 6515, pp. 81–92. Springer, Heidelberg (2011) 11. : Investigating the limits of Monte-Carlo tree search methods in computer go.

Also we do not compare with NMCS and NRPA since these algorithms are tailored to single-player games and perform poorly when applied directly to two-player games. We give results of 1,000 and 10,000 playouts per move. Results are given in Table 1. Each result is the outcome of a 500 games match, 250 playing first and 250 playing second. PPA has worse results than UCT in three games: Go, Knightthrough 5×5 and Nogo 8 × 8. For the other 17 games it improves over UCT. It is particularly good at misere games, a possible explanation is that it learns to avoid losing moves in the playouts and that it may be important for misere games that are waiting games.

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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


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