Abstract
The challenge in developing agents for incomplete information games resides in the fact that the maximum utility decision for given information set is not always ascertainable. For large games like Poker, the agents’ strategies require opponent modeling, since Nash equilibrium strategies are hard to compute. In light of this, simulation systems are indispensable for accurate assessment of agents’ capabilities. Nevertheless, current systems do not accommodate the needs of computer poker research since they were designed mainly as an interface for human players competing against agents. In order to contribute towards improving computer poker research, a new simulation system was developed. This system introduces scientifically unexplored game modes with the purpose of providing a more realistic simulation environment, where the agent must play carefully to manage its initial resources. An evolutionary simulation feature was also included so as to provide support for the improvement of adaptive strategies. The simulator has built-in odds calculation, an agent development API, other platform agents and several variants support and an agent classifier with realistic game indicators including exploitability estimation. Tests and qualitative analysis have proven this simulator to be faster and better suited for thorough agent development and performance assessment.
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
Billings, D.: Computer Poker. University of Alberta (1995)
Billings, D., et al.: Opponent modeling in poker. In: Proceedings of the National Conference on Artificial Intelligence, pp. 493–499. John Wiley & Sons Ltd. (1998)
Billings, D., et al.: Using selective-sampling simulations in poker. In: AAAI Syring Symposium Search Techniques for Problem Solving Under Uncertainty and Incomplete Information, pp. 1–6 (1999)
Van den Broeck, G., Driessens, K., Ramon, J.: Monte-Carlo Tree Search in Poker Using Expected Reward Distributions. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS, vol. 5828, pp. 367–381. Springer, Heidelberg (2009)
Campbell, M., et al.: Deep Blue. Artificial Intelligence 134(1-2), 57–83 (2002)
Chen, B., Ankenman, J.: The Mathematics of Poker. Conjelco (2006)
Davidson, A., et al.: Poker Academy Pro - The Ultimate Poker Software, http://www.poker-academy.com/
Epstein, R.A.: The Theory of Gambling and Statistical Logic. Academic Press Inc. (1995)
Gilpin, A., Sandholm, T.: Better automated abstraction techniques for imperfect information games, with application to Texas Hold’em poker. In: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2007, p. 1. ACM Press (2007)
Johanson, M.: Robust Strategies and Counter-Strategies: Building a Champion Level Computer Poker Player. University of Alberta (2007)
Johanson, M., Bowling, M.: Data biased robust counter strategies. In: Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 264–271 (2009)
Quek, H., et al.: Evolving Nash-optimal poker strategies using evolutionary computation. Frontiers of Computer Science in China 3(1), 73–91 (2009)
Rubin, J., Watson, I.: Case-based strategies in computer poker. AI Communications 25(1), 19–48 (2012)
Rubin, J., Watson, I.: Computer poker: A review. Artificial Intelligence 175(5-6), 958–987 (2011)
Schatzberg, D.: Open Meerkat Bot Simulation Testbed, http://code.google.com/p/opentestbed/
Sklansky, D.: The Theory of Poker: A Professional Poker Player Teaches You How to Think Like One. Two Plus Two (2007)
Teófilo, L.F., et al.: A Simulation System to Support Computer Poker Research. In: 13th International Workshop on Multi-Agent Based Simulation at AAMAS Workshop Proceedings, Valência, pp. 81–92 (2012)
Teófilo, L.F., Passos, N., Reis, L.P., Cardoso, H.L.: Adapting Strategies to Opponent Models in Incomplete Information Games: A Reinforcement Learning Approach for Poker. In: Kamel, M., Karray, F., Hagras, H. (eds.) AIS 2012. LNCS, vol. 7326, pp. 220–227. Springer, Heidelberg (2012)
Teófilo, L.F., et al.: Computer Poker Research at LIACC. In: Computer Poker Symposium. AAAI (2012)
Teófilo, L.F.: Estimating the Probability of Winning for Texas Hold’em Poker Agents. In: Proceedings of the 6th Doctoral Symposium on Informatics Engineering, pp. 129–140 (2011)
Teófilo, L.F., Reis, L.P.: Building a No Limit Texas Hold’em Poker Agent based on Game Logs using Supervised Learning. In: Kamel, M., Karray, F., Gueaieb, W., Khamis, A. (eds.) AIS 2011. LNCS (LNAI), vol. 6752, pp. 73–82. Springer, Heidelberg (2011)
Teófilo, L.F., Reis, L.P.: HoldemML: A framework to generate No Limit Hold’em Poker agents from human player strategies. In: 6th Iberian Conference on Information Systems and Technologies (CISTI 2011), pp. 755–760. IEEE (2011)
Zinkevich, M., et al.: A new algorithm for generating equilibria in massive zero-sum games. In: Proceedings of the 22nd National Conference on Artificial intelligence, AAAI 2007, vol. 1, pp. 788–793 (2007)
Zinkevich, M., Littman, M.L.: The 2006 AAAI Computer Poker Competition. Journal of International Computer Games Association 29, 166–167 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Teófilo, L.F., Rossetti, R., Reis, L.P., Cardoso, H.L., Nogueira, P.A. (2013). Simulation and Performance Assessment of Poker Agents. In: Giardini, F., Amblard, F. (eds) Multi-Agent-Based Simulation XIII. MABS 2012. Lecture Notes in Computer Science(), vol 7838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38859-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-38859-0_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38858-3
Online ISBN: 978-3-642-38859-0
eBook Packages: Computer ScienceComputer Science (R0)