Game theory-based opponent modeling in large imperfect-information games

10th International Conference on Autonomous Agents and Multiagent Systems 2011, AAMAS 2011

Volumn 1, Issue , 2011, Pages 497-504

  Ganzfried, Sam ^a   Sandholm, Tuomas ^a  

^a Computer Science Department Carnegie Mellon University ^*  (United States)

Author keywords

Game theory; Multiagent learning

Indexed keywords

ALGORITHMS; AUTONOMOUS AGENTS; GAME THEORY; MULTI AGENT SYSTEMS;

DOMAIN SPECIFIC; EQUILIBRIUM STRATEGY; EXTENSIVE-FORM GAMES; HISTORICAL DATA; IMPERFECT INFORMATION; MULTI-AGENT LEARNING; OPPONENT MODELING; PRIOR DISTRIBUTION;

COMPUTER GAMES;

EID: 84899449920     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Darse Billings, Neil Burch, Aaron Davidson, Robert Holte, Jonathan Schaeffer, Terence Schauenberg, and Duane Szafron. Approximating game-theoretic optimal strategies for full-scale poker. IJCAI, 2003.
2. Doran Chakraborty and Peter Stone. Convergence, targeted optimality, and safety in multiagent learning. ICML, 2010.
3. Aaron Davidson, Darse Billings, Jonathan Schaeffer, and Duane Szafron. Improved opponent modeling in poker. IJCAI, 2000.
4. Andrew Gilpin and Tuomas Sandholm. A competitive Texas Hold'em poker player via automated abstraction and real-time equilibrium computation. AAAI, 2006.
5. Andrew Gilpin, Tuomas Sandholm, and Troels Bjerre Sorensen. Potential-aware automated abstraction of sequential games, and holistic equilibrium analysis of Texas Hold'em poker. AAAI, 2007.
6. Andrew Gilpin, Samid Hoda, Javier Pena, and Tuomas Sandholm. Gradient-based algorithms for finding Nash equilibria in extensive form games. WINE, 2007 Extended version in Math, of OR, 2010.
7. Bret Hoehn, Finnegan Southey, Robert C. Holte, and Valeriy Bulitko. Effective short-term opponent exploitation in simplified poker. AAAI, 2005.
8. Michael Johanson and Michael Bowling. Data biased robust counter strategies. AISTATS, 2009.
9. Michael Johanson, Martin Zinkevich, and Michael Bowling. Computing robust counter-strategies. NIPS, 2007.
10. Daphne Roller, Nimrod Megiddo, and Bernhard von Stengel. Efficient computation of equilibria for extensive two-person games. GEB, 1996.
11. Marc Lanctot, Kevin Waugh, Martin Zinkevich, and Michael Bowling. Monte Carlo sampling for regret minimization in extensive games. COLT workshop on Online Learning with Limited Feedback, 2009.
12. Peter McCracken and Michael Bowling. Safe strategies for agent modelling in games. AAAI Fall Symposium on Artificial Multi-agent Learning, 2004.
13. Marc Ponsen, Marc Lanctot, and Steven de Jong. MCRNR: Fast computing of restricted Nash responses by means of sampling. AAAI workshop on Interactive Decision Theory and Game Theory Workshop, 2010.
14. Marc Ponsen, Jan Ramon, Tom Croonenborghs, Kurt Driessens, and Karl Tuyls. Bayes-relational learning of opponent models from incomplete information in no-limit poker. AAAI, 2008.
15. Tuomas Sandholm. Perspectives on multiagent learning. Artificial Intelligence, 2007.
16. Finnegan Southey, Michael Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, and Chris Rayner. Bayes' bluff: Opponent modelling in poker. UAI, 2005.
17. Martin Zinkevich, Michael Bowling, Michael Johanson, and Carmelo Piccione. Regret minimization in games with incomplete information. NIPS, 2007.