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Outline Background  What is Texas Hold'em?  What are Bayesian Networks?  What is BPP? Aims  Initial opponent model  Adaptive opponent model  Performance.

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Presentation on theme: "Outline Background  What is Texas Hold'em?  What are Bayesian Networks?  What is BPP? Aims  Initial opponent model  Adaptive opponent model  Performance."— Presentation transcript:

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)‏ 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 ± ± ± Teddy (USA)‏ ± ± ± Monash (Monash U)‏ ± ± ± Bluffbot (Finland)‏ ± ± ± Hyperborean (U Alberta)‏ Teddy (USA)‏ Monash (Monash U)‏ Bluffbot (Finland)‏ Hyperborean (U Alberta)‏

9 Initial opponent model AGGRESSIVECONSERVATIVE

10 New Network Structure New node

11 Final opponent model

12 Generating different opponents using Betting Curves Adapted from Carlton (2006)‏ AggressiveConservative

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). Aces High Casino Parties and Rentals San Antonio Texas (2007). 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). Posters_i _.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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