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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
Their work also showed that even simple evaluation functions can lead to better performance. However, they found that this technique did not work on every game. They ran experiments on Chinese checkers and the card game Heart but obtained no signiﬁcant improvement. Our Work. For CDC, we use as the heuristic evaluation function the weighted material sum, namely the diﬀerence of the total piece weights between the player and the oppo‐ nent. The piece weights are listed in Table 1. The heuristic evaluations are then scaled to [–1, 1] by a sigmoid function.
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. New Math. Nat. Comput. 4(3), 343–357 (2008) 8. : Combining online and oﬄine knowledge in UCT.
In: AAAI, pp. 259–264 (2008) 11. : Learning simulation control in general game-playing agents. In: AAAI (2010) 12. : Mathematical games. Sci. Am. 230, 106–108 (1974) 13. : Monte-Carlo tree search and rapid action value estimation in computer go. Artif. Intell. 175(11), 1856–1875 (2011) 14. : General game playing: overview of the AAAI competition. AI Mag. 26(2), 62–72 (2005) 15. 0: a pattern-based MCTS hex player. In: Computers and Games - 8th International Conference, CG 2013, Yokohama, Japan, 13–15 August 2013, Revised Selected Papers, pp.
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