Generating heuristics for novice players

Fernando De Mesentier Silva, Aaron Isaksen, Julian Togelius, Andy Nealen

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


    We consider the problem of generating compact sub-optimal game-playing heuristics that can be understood and easily executed by novices. In particular, we seek to find heuristics that can lead to good play while at the same time be expressed as fast and frugal trees or short decision lists. This has applications in automatically generating tutorials and instructions for playing games, but also in analyzing game design and measuring game depth. We use the classic game Blackjack as a testbed, and compare condition induction with the RIPPER algorithm, exhaustive-greedy search in statement space, genetic programming and axis-aligned search. We find that all of these methods can find compact well-playing heuristics under the given constraints, with axis-aligned search performing particularly well.

    Original languageEnglish (US)
    Title of host publication2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
    PublisherIEEE Computer Society
    ISBN (Electronic)9781509018833
    StatePublished - Jul 2 2016
    Event2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 - Santorini, Greece
    Duration: Sep 20 2016Sep 23 2016

    Publication series

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


    Other2016 IEEE Conference on Computational Intelligence and Games, CIG 2016

    ASJC Scopus subject areas

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


    Dive into the research topics of 'Generating heuristics for novice players'. Together they form a unique fingerprint.

    Cite this