Information Technology Reference
In-Depth Information
8. Ballard, B.W.: The *-minimax search procedure for trees containing chance nodes.
Artifical Intelligence 21, 327-350 (1983)
9. Jouandeau, N., Cazenave, T.: Small and large MCTS playouts applied to Chinese
Dark Chess stochastic game. In: ECAI 3th Int. Computer Games Workshop 2014
(CGW 2014) (2014)
10. Childs, B.E., Brodeur, J.H., Kocsis, L.: Transpositions and move groups in Monte
Carlo tree search. In: IEEE Symp. On Computational Intelligence and Games
(CIG 2008), pp. 389-395 (2008)
11. Cowling, P.I., Powley, E.J., Whitehouse, D.: Information Set Monte Carlo Tree
Search. IEEE Trans. on Computational Intelligence and AI in Games 4(2),
120-143 (2012)
12. Van Eyck, G., Muller, M.: Revisiting move groups in monte-carlo tree search. In:
van den Herik, H.J., Plaat, A. (eds.) ACG 2011. LNCS, vol. 7168, pp. 13-23.
Springer, Heidelberg (2012)
13. Lanctot, M., Sadine, A., Veness, J., Archibald, C., Winands, M.: Monte Carlo
*-Minimax Search. In: 23rd Int. Joint Conf. on Artificial Intelligence (IJCAI 2013)
(2013)
14. Van Lishout, F., Chaslot, G., Uiterwijk, J.W.H.M.: Monte-Carlo Tree Search in
Backgammon. In:Int. Computer Games Workshop (CGW 2007), pp.175-184 (2007)
15. Sheppard, B.: World-championship-caliber Scrabble. Artificial Intelligence 134,
241-275 (2002)
16. Billings, D., Davidson, A., Schaeffer, J., Szafron, D.: The Challenge of Poker.
Artificial Intelligence 134, 201-240 (2002)
Search WWH ::
Custom Search