Simulating strategy and dexterity for puzzle games

Aaron Isaksen, Drew Wallace, Adam Finkelstein, Andy Nealen

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

    Abstract

    We examine the impact of strategy and dexterity on video games in which a player must use strategy to decide between multiple moves and must use dexterity to correctly execute those moves. We run simulation experiments on variants of two popular, interactive puzzle games: Tetris, which exhibits dexterity in the form of speed-accuracy time pressure, and Puzzle Bobble, which requires precise aiming. By modeling dexterity and strategy as separate components, we quantify the effect of each type of difficulty using normalized mean score and artificial intelligence agents that make human-like errors. We show how these techniques can model and visualize dexterity and strategy requirements as well as the effect of scoring systems on expressive range.

    Original languageEnglish (US)
    Title of host publication2017 IEEE Conference on Computational Intelligence and Games, CIG 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages142-149
    Number of pages8
    ISBN (Electronic)9781538632338
    DOIs
    StatePublished - Oct 23 2017
    Event2017 IEEE Conference on Computational Intelligence and Games, CIG 2017 - New York, United States
    Duration: Aug 22 2017Aug 25 2017

    Publication series

    Name2017 IEEE Conference on Computational Intelligence and Games, CIG 2017

    Other

    Other2017 IEEE Conference on Computational Intelligence and Games, CIG 2017
    CountryUnited States
    CityNew York
    Period8/22/178/25/17

    Keywords

    • AI-assisted game design
    • Automated play testing
    • Dexterity
    • Difficulty
    • Strategy

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Human-Computer Interaction
    • Media Technology

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