Abstract
Monte-Carlo Tree Search, and in particular with the Upper Confidence Bounds formula, has provided large improvements for AI in numerous games, particularly in Go, Hex, Havannah, Amazons and Breakthrough. In this work we study this algorithm on a more complex game, the game of “7 Wonders”. This card game gathers together several known challenging properties, such as hidden information, multi-player and stochasticity. It also includes an inter-player trading system that induces a combinatorial search to decide which decisions are legal. Moreover, it is difficult to hand-craft an efficient evaluation function since the card values are heavily dependent upon the stage of the game and upon the other player decisions. We show that, in spite of the fact that “7 Wonders” is apparently not so related to classic abstract games, many known results still hold.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arneson, B., Hayward, R.B., Henderson, P.: Monte-Carlo tree search in Hex. IEEE Transactions on Computational Intelligence and AI in Games 2(4), 251–258 (2010)
Bjarnason, R., Fern, A., Tadepalli, P.: Lower bounding Klondike Solitaire with Monte-Carlo planning. In: ICAPS (2009)
Browne, C.B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A survey of Monte-Carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games 4(1), 1–43 (2012)
Cazenave, T.: Monte-carlo kakuro. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 45–54. Springer, Heidelberg (2010)
Chaslot, G., Fiter, C., Hoock, J.-B., Rimmel, A., Teytaud, O.: Adding expert knowledge and exploration in monte-carlo tree search. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 1–13. Springer, Heidelberg (2010)
Chaslot, G.M.J.-B., Winands, M.H.M., van den Herik, H.J., Uiterwijk, J.W.H.M., Bouzy, B.: Progressive strategies for Monte-Carlo tree search. New Mathematics and Natural Computation 4(3), 343–357 (2008)
Coulom, R.: Computing “ELO ratings” of move patterns in the game of Go. ICGA Journal 30(4), 198–208 (2007)
Coulom, R.: Efficient selectivity and backup operators in monte-carlo tree search. In: van den Herik, H.J., Ciancarini, P., Donkers, H.H.L.M(J.) (eds.) CG 2006. LNCS, vol. 4630, pp. 72–83. Springer, Heidelberg (2007)
Enzenberger, M., Müller, M., Arneson, B., Segal, R.: Fuego–An open-source framework for board games and Go engine based on Monte Carlo tree search. IEEE Transactions on Computational Intelligence and AI in Games 2(4), 259–270 (2010)
Gardiner, A.: proposal for an agent that plays 7 Wonders. Tech. rep., Willamette University (2012), http://www.willamette.edu/~bgardine/Thesis_Files/ben_gardiner_proposal_final.pdf
Gelly, S., Hoock, J.B., Rimmel, A., Teytaud, O., Kalemkarian, Y., et al.: On the parallelization of Monte-Carlo planning. In: ICINCO (2008)
Gelly, S., Silver, D.: Combining online and offline knowledge in UCT. In: Proceedings of the 24th International Conference on Machine Learning, pp. 273–280. ACM (2007)
Ginsberg, M.L.: Gib: Imperfect information in a computationally challenging game. J. Artif. Intell. Res. (JAIR) 14, 303–358 (2001)
Kocsis, L., Szepesvári, C.: Bandit based Monte-Carlo planning. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282–293. Springer, Heidelberg (2006)
Lorentz, R.J.: Amazons discover monte-carlo. In: van den Herik, H.J., Xu, X., Ma, Z., Winands, M.H.M. (eds.) CG 2008. LNCS, vol. 5131, pp. 13–24. Springer, Heidelberg (2008)
Pfeiffer, M.: Reinforcement learning of strategies for Settlers of Catan. In: 5th International Conference on Computer Games: Artificial Intelligence, Design and Education, pp. 384–388 (2004)
Szita, I., Chaslot, G., Spronck, P.: Monte-Carlo tree search in Settlers of Catan. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 21–32. Springer, Heidelberg (2010)
Teytaud, F., Teytaud, O.: Creating an upper-confidence-tree program for havannah. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 65–74. Springer, Heidelberg (2010)
Wang, Y., Audibert, J.Y., Munos, R., et al.: Infinitely many-armed bandits. Advances in Neural Information Processing Systems (2008)
Whitehouse, D., Powley, E.J., Cowling, P.I.: Determinization and information set Monte-Carlo tree search for the card game Dou Di Zhu. In: 2011 IEEE Conference on Computational Intelligence and Games (CIG), pp. 87–94. IEEE (2011)
Winands, M.H.M., Björnsson, Y.: Evaluation function based Monte-Carlo LOA. In: van den Herik, H.J., Spronck, P. (eds.) ACG 2009. LNCS, vol. 6048, pp. 33–44. Springer, Heidelberg (2010)
Winder, R.K.: Methods for approximating value functions for the Dominion card game. Evolutionary Intelligence (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Robilliard, D., Fonlupt, C., Teytaud, F. (2014). Monte-Carlo Tree Search for the Game of “7 Wonders”. In: Cazenave, T., Winands, M.H.M., Björnsson, Y. (eds) Computer Games. CGW 2014. Communications in Computer and Information Science, vol 504. Springer, Cham. https://doi.org/10.1007/978-3-319-14923-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-14923-3_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14922-6
Online ISBN: 978-3-319-14923-3
eBook Packages: Computer ScienceComputer Science (R0)