By Aske Plaat, Jaap van den Herik, Walter Kosters
This ebook constitutes the completely refereed post-conference proceedings of the 14th overseas convention on Advances in machine video games, ACG 2015, held in Leiden, The Netherlands, in July 2015. The 22 revised complete papers awarded have been conscientiously reviewed and selected from 34 submissions. The papers disguise a variety of topics corresponding to Monte-Carlo Tree seek and its improvements; theoretical features and complexity; research of video game features; seek algorithms; and computing device studying.
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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
The main diﬀerences with NRPA are that it does not use nested levels nor a best sequence to learn. Instead it learns the moves of each playout for the winner of the playout. Future work include combining PPA with the numerous enhancements of UCT. Some of them may be redundant but others will probably be cumulative. For example combining PPA with RAVE could yield substantial beneﬁts in some games. A second line of research is understanding why PPA is good at many games and bad at other games such as Go.
Springer, Heidelberg (2008) 7. : Programming breakthrough. , Plaat, A. ) CG 2013. LNCS, vol. 8427, pp. 49–59. Springer, Heidelberg (2013) 8. : Experiments with monte-carlo tree search in the game of havannah. ICGA J. 34(3), 140–150 (2011) 9. : Evaluation function based monte-carlo LOA. , Spronck, P. ) ACG 2009. LNCS, vol. 6048, pp. 33–44. Springer, Heidelberg (2010) 10. jsp 11. id=249 12. Havannah#The Havannah Challenge. fr Abstract. Monte-Carlo Tree Search (MCTS) is the state of the art algorithm for General Game Playing (GGP).
This is sometimes referred to as the parity eﬀect. The evaluation function tends to heavily favor the player to move. To help stabilize EPT we can modify the random playouts so that they always terminate with the same player to move. In the case of Amazons we terminate the playout after either 5 or 4 moves, accordingly. This produces some small advantage in the case of Amabot, but in the cases of Havabot and Breakbot, where the evaluations do not display such a strong parity eﬀect, adjusting the playouts this way does not seem to have any eﬀect.