By Aske Plaat, Jaap van den Herik, Walter Kosters
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Additional info for Advances in Computer Games: 14th International Conference, ACG 2015, Leiden, The Netherlands, July 1-3, 2015, Revised Selected Papers
Our feeling is that Havannah programs cannot be expected to play at an elite level until they learn to play a more natural, human-like game. Towards this end, we have retooled Havbot to use EPT. Evidence is still inconclusive, and more details will be provided below, but we feel that its current style of play is more natural and has the potential to improve to noticeably higher levels of play. It currently beats the mini-max version of Havbot about 60 % of the time. Breakbot, like Havbot, was written initially using MCTS but we fully expected to transition to EPT.
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. : A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4(1), 1–43 (2012) 7. : Progressive strategies for Monte-Carlo tree search.
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. , Plaat, A.
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