• DocumentCode
    742303
  • Title

    Player Preference and Style in a Leading Mobile Card Game

  • Author

    Cowling, Peter I. ; Devlin, Sam ; Powley, Edward J. ; Whitehouse, Daniel ; Rollason, Jeff

  • Author_Institution
    Department of Computer Science and York Centre for Complex Systems Analysis, University of York, UK
  • Volume
    7
  • Issue
    3
  • fYear
    2015
  • Firstpage
    233
  • Lastpage
    242
  • Abstract
    Tuning game difficulty prior to release requires careful consideration. Players can quickly lose interest in a game if it is too hard or too easy. Assessing how players will cope prior to release is often inaccurate. However, modern games can now collect sufficient data to perform large scale analysis postdeployment and update the product based on these insights. AI Factory Spades is currently the top rated Spades game in the Google Play store. In collaboration with the developers, we have collected gameplay data from 27 592 games and statistics regarding wins/losses for 99 866 games using Google Analytics. Using the data collected, this study analyses the difficulty and behavior of an Information Set Monte Carlo Tree Search player we developed and deployed in the game previously. The methods of data collection and analysis presented in this study are generally applicable. The same workflow could be used to analyze the difficulty and typical player or opponent behavior in any game. Furthermore, addressing issues of difficulty or nonhuman-like opponents postdeployment can positively affect player retention.
  • Keywords
    Artificial intelligence; Data collection; Games; Google; Land mobile radio; Monte Carlo methods; Production facilities; Artificial intelligence; Monte Carlo tree search; data mining; game analytics;
  • fLanguage
    English
  • Journal_Title
    Computational Intelligence and AI in Games, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1943-068X
  • Type

    jour

  • DOI
    10.1109/TCIAIG.2014.2357174
  • Filename
    6895268