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Clustering-Based Online Player Modeling

  • Jason M. Bindewald
  • Gilbert L. PetersonEmail author
  • Michael E. Miller
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 705)

Abstract

Being able to imitate individual players in a game can benefit game development by providing a means to create a variety of autonomous agents and aid understanding of which aspects of game states influence game-play. This paper presents a clustering and locally weighted regression method for modeling and imitating individual players. The algorithm first learns a generic player cluster model that is updated online to capture an individual’s game-play tendencies. The models can then be used to play the game or for analysis to identify how different players react to separate aspects of game states. The method is demonstrated on a tablet-based trajectory generation game called Space Navigator.

Keywords

State Cluster Trajectory Generator Game State Cluster Population Individual Player 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG (outside the US) 2017

Authors and Affiliations

  • Jason M. Bindewald
    • 1
  • Gilbert L. Peterson
    • 1
    Email author
  • Michael E. Miller
    • 1
  1. 1.Air Force Institute of TechnologyWright-Patterson AFBUSA

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