Participants: 2012 - 3-player Limit Texas Hold'em

3-player Limit Texas Hold'em

dcubot

  • Team Name: dcubot
  • Team Leader: Neill Sweeney
  • Team Members: Neill Sweeney, David Sinclair
  • Affiliation: School of Computing, Dublin City University
  • Location: Dublin 9, Ireland.
  • Technique:
    The bot uses 4 seperate connectionist strutures for each betting round. Ten input features describe the state of the betting after each legal decision and there are over 300 basic features describing the visible cards. Reading opponent hands is dealt with by maximum likelihood fitting a hidden markov model to the play with the cards hidden. A belief vector over the hidden variable is then used as an additional input.

    This year we have increased the size of the structure by doubling the hidden layer.

Hyperborean3p

  • Team Name: Hyperborean3p
  • Team Leader: Michael Bowling
  • Team Members: Michael Bowling, Duane Szafron, Rob Holte, Chris Archibald, Michael Johanson, Nolan Bard, Johnny Hawkin, Richard Gibson, Neil Burch, Parisa Mazrooei, Josh Davidson
  • Affiliation: University of Alberta
  • Location: Edmonton, Alberta, Canada
  • Technique:
    Our 3-player program is built using the External Sampling (ES) [2] variant of Counterfactual Regret Minimization [3]. ES is applied to an abstract game constructed from two different card abstractions of Texas Hold'em, producing a dynamic expert strategy [1]. The first card abstraction is a very fine and allows our program to distinguish between many different possible hands on each round, whereas our second card abstraction is much coarser and merges many different hands into the same information set. The first abstraction is applied to the "important" parts of the betting tree, where importance is determined by the potsize and the frequency at which our program reached the betting sequence in last year's competition. The second, coarser abstraction is applied elsewhere.
  • References and related papers:
    • Richard Gibson and Duane Szafron. On strategy stitching in large extensive form multiplayer games. In NIPS 2011..
    • Marc Lanctot, Kevin Waugh, Martin Zinkevich, and Michael Bowling. Monte Carlo sampling for regret minimization in extensive games. In NIPS 2009.
    • Martin Zinkevich, Michael Johanson, Michael Bowling, and Carmelo Piccione. Regret minimization in games with incomplete information. In NIPS 2008.

LittleRock

  • Team Name: LittleRock
  • Team Leader: Rod Byrnes
  • Team Members: Rod Byrnes
  • Affiliation: Independent
  • Location: Lismore, Australia
  • Technique:
    LittleRock uses an external sampling monte carlo CFR approach with imperfect recall. Additional RAM was available for training the agent entered into this year's competition, which allowed for a more fine grained card abstraction, but the algorithm is otherwise largely unchanged. One last-minute addition this year is a no-limit agent.

    The no-limit agent has 4,491,849 information sets, the heads-up limit agent has 11,349,052 information sets and the limit 3-player agent has 47,574,530 information sets. In addition to card abstractions, the 3-player and no-limit agents also use a form of state abstraction to make the game size manageable.
  • References and related papers:
    • 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. 10781086, 2009.

Neo Poker Bot

  • Team Name: Neo Poker Laboratory
  • Team Leader: Alexander Lee
  • Team Members: Alexander Lee
  • Affiliation: Independent
  • Location: Spain
  • Technique:
    Our range of computer players was developed to play against humans. The AI was trained on top poker rooms real money hand history logs. 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.
    Our computer players have been tested against humans and demonstrated great results over 100 mln hands. The AI was not optimized to play against computer players.

 

Sartre3P

  • Team Name: Sartre
  • Team Leader: Jonathan Rubin
  • Team Members: Jonathan Rubin, Ian Watson
  • Affiliation: University of Auckland
  • Location: Auckland, New Zealand
  • Technique:
    Sartre3P uses a case-based approach to play Texas Hold'em. AAAI hand history data from both three-player and two-player matches are encoded into separate case-bases. When a playing decision is required, a case with the current game state information is created. If no opponents have folded, Sartre3P will search the three-player case-base for similar game scenarios for a solution. On the other hand, if an opponent has folded, Sartre3P will search the two-player case-base and switch to a heads-up strategy if it is possible to map the three-player betting sequence to an appropriate two-player sequence.
  • References and related papers:
    • Jonathan Rubin and Ian Watson. Case-Based Strategies in Computer Poker, AI Communications, Volume 25, Number 1: 19-48, March 2012.