Participants: 2013 - 3-player Limit Texas Hold'em

3-player Limit Texas Hold'em

HITSZ_CS_13

  • Team Name: HITSZ_CS_13
  • Team Leader: Xuan Wang
  • Team Members: Xuan Wang, Jiajia Zhang, Song Wu
  • Affiliation: School of Computer Science and Technology HIT
  • Location: Shenzhen, Guangdon province, China
  • Technique:
    Our program makes decision accoding to current hand strength and a set of precomputed probabilities, at the same time it tries to modeling the opponent. After the opponent model is built, the program will take advantage of the model when making decision.

Hyperborean3pl.iro

  • Team Name: Univeristy of Alberta
  • Team Leader: Michael Bowling
  • Team Members: Richard Gibson, Joshua Davidson, Michael Johanson, Nolan Bard, Neil Burch, John Hawkin, Trevor Davis, Christopher Archibald, Michael Bowling, Duane Szafron, Rob Holte
  • Affiliation: University of Alberta
  • Location: Edmonton, Alberta, Canada
  • Technique:
    Counterfactual Regret Minimization (CFR) [Zinkevich et al., NIPS 2007] was the main technique used to build this agent. Because 3-player hold'em is too large a game to apply CFR to directly, we employed an abstract game that merges card deals into "buckets" to create a game of manageable size [Gilpin & Sandholm, AAMAS 2007].

    To create our abstract game, we first partitioned the betting sequences into two parts: an "important" part, and an "unimportant" part. Importance was determined according to the frequency with which our 3-player programs from the 2011 and 2012 ACPCs were faced with a decision at that betting sequence, as well as according to the number of chips in the pot. Then, we employed two different granularities of abstraction, one for each part of this partition. The unimportant part used 169, 180,000, 18,630, and 875 buckets per betting round respectively, while the important part used 169, 1,348,620, 1,530,000, and 2,800,000 buckets per betting round respectively. Buckets were calculated according to public card textures and k-means clustering over hand strength distriubtions [Johanson et al., AAMAS 2013] and yielded an imperfect recall abstract game by forgetting previous card information and rebucketing on every round [Waugh et al., SARA 2009]. The agent plays the "current strategy profile" computed from approximately 303.6 billion iterations of the "Pure CFR" variant of CFR [Richard Gibson, PhD thesis, in preparation] applied to this abstract game. This type of strategy is also known as a "dynamic expert strategy" [Gibson & Szafron, NIPS 2011].

Hyperborean3pl.tbr

  • Team Name: Univeristy of Alberta
  • Team Leader: Michael Bowling
  • Team Members: Michael Bowling, Duane Szafron, Rob Holte, Chris Archibald, Michael Johanson, Nolan Bard, John Hawkin, Richard Gibson, Neil Burch, Josh Davidson, Trevor Davis
  • Affiliation: University of Alberta
  • Location: Edmonton, Alberta, Canada
  • Technique:
    Hyperborean is a data biased response to aggregate data of ACPC competitors from the 2010 and 2011 3-player limit competitions [4]. The strategy was generated using the Counterfactual Regret Minimization (CFR) algorithm [1] with imperfect recall abstractions. Buckets were calculated according to public card textures and k-means clustering over hand strength distributions [3] and yielded an imperfect recall abstract game by forgetting previous card information and rebucketing on every round [2]. The agent plays the "current strategy profile" generated after 20 billion iterations of external sampled CFR [5]. The abstraction uses 169, 10000, 5450, and 500 buckets on each round of the game, respectively.
  • References and related papers:
    1. Martin Zinkevich, Michael Johanson, Michael Bowling, and Carmelo Piccione. "Regret minimization in games with incomplete information" In NIPS 2008.
    2. Kevin Waugh, Martin Zinkevich, Michael Johanson, Morgan Kan, David Schnizlein, and Michael Bowling. "A Practical Use of Imperfect Recall". Proceedings of the Eighth Symposium on Abstraction, Reformulation and Approximation (SARA), 2009.
    3. Michael Johanson, Neil Burch, Richard Valenzano, and Michael Bowling. "Evaluating State-Space Abstractions in Extensive-Form Games". In Proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2013.
    4. Michael Johanson and Michael Bowling. "Data Biased Robust Counter Strategies". In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS), 2009.
    5. Marc Lanctot, Kevin Waugh, Martin Zinkevich, and Michael Bowling. "Monte Carlo Sampling for Regret Minimization in Extensive Games". In Proceedings of the Twenty-Third Conference on Neural Information Processing Systems (NIPS), 2009.

KEmpfer

  • Team Name: KEmpfer
  • Team Leader: Eneldo Loza Mencia
  • Team Members: Eneldo Loza Mencia, Tomek Gasiorowski, Peter Glockner, Julian Prommer
  • Affiliation: Knowledge Engineering Group, Technische Universitat Darmstadt
  • Location: Darmstadt, Germany
  • Technique:
    The agent implements a list of expert rules and follows these. Additional opponent statistics are collected and these are used in the rules, but these rules are currently disabled. The backup strategy if no expert rule is found is to play according to the expected hand strength.

LIACC

  • Team Name:LIACC
  • Team Leader: Luis Filipe Teofilo
  • Team Members: Luis Filipe Teofilo
  • Affiliation: University of Porto, Artificial Intelligence and Computer Science Laboratory
  • Location: Porto, Portugal
  • Technique: Expected value maximization with game partition

Little Rock

  • Team Name: Little Rock
  • Team Leader: Rod Byrnes
  • Team Members: Rod Byrnes
  • Affiliation: Independent
  • Location: Goonellabah, NSW, Australia
  • Technique:
    Little Rock uses an external sampling monte carlo CFR approach with imperfect recall. All agents in this year's competition use the same card abstraction, which has 8192 buckets on each of the flop, turn and river, which are created by clustering all possible hands using a variety of metrics from the current and previous rounds. The 2 player limit agent uses no action abstrations. The other two agents use what I call a "cross-sectional" approach which abstracts aspects of the current game state rather than translating individual actions (which is what I call a "longitudinal" approach).
  • References and related papers:
    1. Monte Carlo Sampling for Regret Minimization in Extensive Games. Marc Lanctot, Kevin Waugh, Martin Zinkevich, and Michael Bowling. In Advances in Neural Information Processing Systems 22 (NIPS), pp. 1078-1086, 2009.

Neo Poker Bot

  • Team Name: Neo Poker Laboratory
  • Team Leader: Alexander Lee
  • Team Members: Alexander Lee
  • Affiliation: Independent
  • Location:
  • Technique:
    The AI logic employs different combinations of Neural networks, Regret Minimization and Gradient Search Equilibrium Approximation, Decision Trees, Recursive Search methods as well as expert algorithms from top players in different games of poker. The AI was not optimized to play against computer players.