By Aske Plaat, Jaap van den Herik, Walter Kosters
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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
Intell. AI Games 4(1), 1–43 (2012) 3. : Nested Monte-Carlo search. In: Boutilier, C. ) IJCAI, pp. 456– 461 (2009) 4. : Sequential halving applied to trees. IEEE Trans. Comput. Intell. AI Games 7(1), 102–105 (2015) 5. : Discounting and pruning for nested playouts in general game playing. GIGA at IJCAI (2015) 6. : Revisiting Monte-Carlo tree search on a normal form game: NoGo. , et al. ) EvoApplications 2011, Part I. LNCS, vol. 6624, pp. 73–82. Springer, Heidelberg (2011) 7. : Eﬃcient selectivity and backup operators in Monte-Carlo tree search.
The diﬀerence with UCT is that in the playouts PPA has a weight for each possible move and chooses randomly between possible moves proportionally to the exponential of the weight. 22 T. 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.
Why would fewer random moves in a playout hinder performance? Observational evidence suggests it boils down to a trade oﬀ between the advantages of a deep evaluation and disadvantages of losing information from the randomness of a playout. In general, an evaluation near the end of the game is more reliable than one earlier on but after too many random moves a position may lose the essence of the starting position. We search for a happy medium where a few random moves take us closer to the end of the game, without having the random moves degrade the information too much.