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Opponent Modeling in Bayesian Poker: Strategies for Adaptive Gameplay

This research explores opponent modeling in Bayesian Poker, focusing on creating adaptive opponent models to improve gameplay. It covers Texas Hold'em, Bayesian Networks, and the Bayesian Poker Program (BPP). The study includes initial and final opponent models, performance testing, and recommendations for further work.

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Opponent Modeling in Bayesian Poker: Strategies for Adaptive Gameplay

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  1. Outline • Background • What is Texas Hold'em? • What are Bayesian Networks? • What is BPP? • Aims • Initial opponent model • Adaptive opponent model • Performance testing • Further Work • Conclusion

  2. Opponent Modeling in Bayesian Poker Brendon Taylor (BSE)‏ http://www.allposters.com/-sp/Poker-Pups-II-Posters_i1611677_.htm Supervisors: Ann Nicholson Kevin Korb

  3. What is Texas Hold'em?

  4. Poker Hands From strongest to weakest

  5. Poker Bayesian Network

  6. What is BPP? • Bayesian Poker Program • 1993: Initial version (Jitnah)‏ • 1999: First publication (Korb, Nicholson, Jitnah)‏ • 2000: Decision network (Carlton)‏ • 2003: Adapted to Texas Hold'em (Boulton)‏

  7. Personality Types • Aggressive behaviour • More likely to bet/raise • Conservative behaviour • More likely to fold/check/call

  8. AAAI 2006 Results - Bankroll Hyperborean (U Alberta)‏ Bluffbot (Finland)‏ Monash (Monash U)‏ Teddy (USA)‏ Hyperborean (U Alberta)‏ 0.0514 ±0.0171 0.7227 ±0.0161 0.4067 ±0.0247 Bluffbot (Finland)‏ -0.0514 ±0.0171 0.5271 ±0.0197 -0.1895 ±0.0289 Monash (Monash U)‏ -0.7227 ±0.0161 -0.5271 ±0.0197 1.1678 ±0.0427 Teddy (USA)‏ -0.4067 ±0.0247 0.1895 ±0.0289 -1.1678 ± 0.0427

  9. Initial opponent model AGGRESSIVE CONSERVATIVE

  10. New Network Structure New node

  11. Final opponent model

  12. Generating different opponentsusing Betting Curves Aggressive Conservative Adapted from Carlton (2006)‏

  13. Results - Opponent Type

  14. Further Work • BPP's game play • Improved bluffing strategy. • Adding sand bagging. • Avoiding predictable game play • Network structure • Adding a OppTight node to the network. • Adding a OppBluff node to the network. • Adding a BppBehaviour node to the network.

  15. Conclusion • BPP is an ongoing research project and still requires further work. • The improved opponent model has improved BPP's ability to adapt to an opponent. • This project has been challenging and taken me outside my comfort zone.

  16. References • AAAI Computer Poker Competition (2006). http://www.cs.ualberta.ca/~pokert/2006/index.html • Aces High Casino Parties and Rentals San Antonio Texas (2007). http://www.aceshighcasinoparties.com • Carlton, J. (2000). Bayesian poker, Honours thesis, School of Computer Science and Software Engineering, Monash University. • Poker Pups II Prints by Jenny Newland at AllPosters.com (2007). http://www.allposters.com/-sp/Poker-Pups-II-Posters_i1611677_.htm • Taylor, B. (2007). Opponent Modeling in Bayesian Poker, Honours Thesis, School of Computer Science and Software Engineering, Monash University.

  17. Aggressive opponent model

  18. Conservative opponent model

  19. Lessons Learnt • Honours is more challenging than under-graduate units. • Artificial Intelligence and decision making. • Machine learning and structures. • How to effectively research a topic. • What to expect if I was to undertake further post-graduate studies.

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