Generating Novice Heuristics for Post-Flop Poker

Fernando De Mesentier Silva, Julian Togelius, Frank Lantz, Andy Nealen

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    Agents now exist that can play Texas Hold'em Poker at a very high level, and simplified versions of the game have been solved. However, this does not directly translate to learning heuristics humans can use to play the game. We address the problem of learning chains of human-learnable heuristics for playing heads-up limit Texas Hold'em, focusing on the post-flop stages of the game. By restricting the policy space to fast and frugal trees, which are sequences of if-then-else rules, we can learn such heuristics using several methods including genetic programming. This work builds on our previous work on learning such heuristic rule set for Blackjack and pre-flop Texas Hold'em, but introduces a richer language for heuristics.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018
    PublisherIEEE Computer Society
    ISBN (Electronic)9781538643594
    StatePublished - Oct 11 2018
    Event14th IEEE Conference on Computational Intelligence and Games, CIG 2018 - Maastricht, Netherlands
    Duration: Aug 14 2018Aug 17 2018

    Publication series

    NameIEEE Conference on Computatonal Intelligence and Games, CIG
    ISSN (Print)2325-4270
    ISSN (Electronic)2325-4289


    Other14th IEEE Conference on Computational Intelligence and Games, CIG 2018


    • Beginner heuristics
    • Genetic algorithms
    • Poker

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction
    • Software

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