Bibliografia

[1] P. Morris, Introduction to games theory, springer, (1994).

[2] Von Neumann, J. and Morgenstern, O., Theory of Games and Economic Behavior (60th Anniversary Commemorative Edition), Princeton University Press, (2007).

[3] F. Barrichelo, http://www.teoriadosjogos.net/teoriadosjogos/list-trechosimprime.asp?id=29, consultado pela ultima vez dia 6/09/2014

[4] Lanctot, M., Lisy, V., and Bowling, M., Search in Imperfect Information Games using Online Monte Carlo Counterfactual Regret Minimization. In AAAI Workshop on Computer Poker and Imperfect Information.(2014).

[5] Billings, D. Algorithms and assessment in computer poker, PhD thesis, University of Alberta, 2006.

[6] Wikipédia, a enciclopédia livre. http://pt.wikipedia.org/wiki/John_von_Neumann, consultado pela ultima vez dia 6/09/2014

[7] Lanctot,M.; Neller, T.(2013). An Introduction to Counterfactual Regret Minimization. http://cs.gettysburg.edu/~tneller/modelai/2013/cfr/index.html, consultado pela ultima vez dia 6/09/2014

[8] Risk, N. and Szafron, D. Using counterfactual regret minimization to create competitive multiplayer poker agents, In Proceeding AAMAS '10 Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1, 2010, Pages 159-166.

[9] Lancot, M.; Waugh, K; Zinkevich, M.; and Bowling, M. (2009).Monte Carlo sampling for regret minimization in extensive games. In Proc. of NIPS, volume 22, 1078-1086.

[10] Johanson, M.; Bard,N.; Lanctot,M; Gibson, R. and Bowling, M.; Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization, Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, 2012.

[11] Davis, T.; Burch, N. and Bowling, M. Using Response Function to Measure Strategy Strength, Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.

[12] Johanson, M., Bard, N., Burch, N., and Bowling, M. (2012). Finding Optimal Abstract Strategies. In Extensive-Form Games. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 1371–1379.

[13] Teofilo, L. F.; Reis, L. P. and Cardoso H. L., Speeding-up Poker Game Abstraction Computation: Average Rank Strength, in Computer Poker and Imperfect Information: Papers from the AAAI 2013 Workshop, 2013, pp. 59-64.

[14] K. Glocer and M. Deckert, (2007) Opponent Modeling in Poker.

[15] S. Ganzfried and T. Sandholm, (2010). Game theory-based opponent modeling in large imperfect-information games. In Proceeding AAMAS '11 The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2, Pages 533-540.

[16]H. Park and K. Kim, Opponent modeling with incremental active learning: A case study of Iterative Prisoner’s Dilemma. In Computational Intelligence in Games (CIG), 2013 IEEE Conference. 2013. Pages 1-2.

[17]M. Johanson,(2013) Measuring the size of large no-limit poker games.

[18]Zinkevich, M.; Johanson, M.; Bowling, M.; and Piccione,C. (2008). Regret minimization in games with incomplete information. In Advances in Neural Information Processing Systems 20 (NIPS 2007).