Clustering-Based Online Player Modeling

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.

[1]  Anne Sullivan,et al.  An inclusive view of player modeling , 2011, FDG.

[2]  Julian Togelius,et al.  Evolving personas for player decision modeling , 2014, 2014 IEEE Conference on Computational Intelligence and Games.

[3]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[4]  Babak Esfandiari,et al.  A Case-Based Reasoning Approach to Imitating RoboCup Players , 2008, FLAIRS.

[5]  Victor Huang,et al.  Contrails: Crowd-Sourced Learning of Human Models in an Aircraft Landing Game , 2013 .

[6]  Gilbert L. Peterson,et al.  A function-to-task process model for adaptive automation system design , 2014, Int. J. Hum. Comput. Stud..

[7]  Julian Togelius,et al.  Procedural Personas as Critics for Dungeon Generation , 2015, EvoApplications.

[8]  David G. Lowe,et al.  Fast Approximate Nearest Neighbors with Automatic Algorithm Configuration , 2009, VISAPP.

[9]  Julian Togelius,et al.  Generative agents for player decision modeling in games , 2014, FDG.

[10]  Julian Togelius,et al.  Making Racing Fun Through Player Modeling and Track Evolution , 2006 .

[11]  Hong Yu,et al.  Personalized Interactive Narratives via Sequential Recommendation of Plot Points , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[12]  Risto Miikkulainen,et al.  Human-Like Combat Behaviour via Multiobjective Neuroevolution , 2012, Believable Bots.

[13]  Santiago Ontañón,et al.  A Survey of Real-Time Strategy Game AI Research and Competition in StarCraft , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[14]  Brett Browning,et al.  Learning by demonstration with critique from a human teacher , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[15]  Mike Preuss,et al.  Making Diplomacy Bots Individual , 2012, Believable Bots.

[16]  David Gamez,et al.  A Neurally Controlled Computer Game Avatar With Humanlike Behavior , 2013, IEEE Transactions on Computational Intelligence and AI in Games.

[17]  Gilbert L. Peterson,et al.  Trajectory Generation with Player Modeling , 2015, Canadian Conference on AI.