Outline Background What is Texas Hold'em? What are Bayesian Networks? What is BPP? Aims Initial...
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![Page 1: Outline Background What is Texas Hold'em? What are Bayesian Networks? What is BPP? Aims Initial opponent model Adaptive opponent model Performance.](https://reader035.fdocuments.in/reader035/viewer/2022062511/551b437b550346dd1a8b57ce/html5/thumbnails/1.jpg)
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
![Page 2: Outline Background What is Texas Hold'em? What are Bayesian Networks? What is BPP? Aims Initial opponent model Adaptive opponent model Performance.](https://reader035.fdocuments.in/reader035/viewer/2022062511/551b437b550346dd1a8b57ce/html5/thumbnails/2.jpg)
Opponent Modeling in Bayesian Poker
Brendon Taylor (BSE)
Supervisors: Ann Nicholson Kevin Korb
http://www.allposters.com/-sp/Poker-Pups-II-Posters_i1611677_.htm
![Page 3: Outline Background What is Texas Hold'em? What are Bayesian Networks? What is BPP? Aims Initial opponent model Adaptive opponent model Performance.](https://reader035.fdocuments.in/reader035/viewer/2022062511/551b437b550346dd1a8b57ce/html5/thumbnails/3.jpg)
What is Texas Hold'em?
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Poker Hands
From strongest to weakest
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Poker Bayesian Network
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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)
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Personality Types
Aggressive behaviour
More likely to bet/raise
Conservative behaviour
More likely to fold/check/call
![Page 8: Outline Background What is Texas Hold'em? What are Bayesian Networks? What is BPP? Aims Initial opponent model Adaptive opponent model Performance.](https://reader035.fdocuments.in/reader035/viewer/2022062511/551b437b550346dd1a8b57ce/html5/thumbnails/8.jpg)
AAAI 2006 Results - Bankroll
-1.1678
± 0.0427
0.1895
±0.0289
-0.4067
±0.0247
Teddy
(USA)
1.1678
±0.0427
-0.5271
±0.0197
-0.7227
±0.0161
Monash
(Monash U)
-0.1895
±0.0289
0.5271
±0.0197
-0.0514
±0.0171
Bluffbot
(Finland)
0.4067
±0.0247
0.7227
±0.0161
0.0514
±0.0171
Hyperborean
(U Alberta)
Teddy
(USA)
Monash
(Monash U)
Bluffbot
(Finland)
Hyperborean
(U Alberta)
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Initial opponent modelAGGRESSIVECONSERVATIVE
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New Network Structure
New node
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Final opponent model
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Generating different opponentsusing Betting Curves
Adapted from Carlton (2006)
Aggressive Conservative
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Results - Opponent Type
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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.
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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.
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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.
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Aggressive opponent model
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Conservative opponent model
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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.