Learning Player Behaviors in Real Time Strategy Games from Real Data

Authors:
P. H. Ng;S. C. Shiu;H. Wang
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P.R. China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P.R. China;Department of Computing, The Hong Kong Polytechnic University, Hong Kong, P.R. China
Venue:
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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Abstract

This paper illustrates our idea of learning and building player behavioral models in real time strategy (RTS) games from replay data by adopting a Case-Based Reasoning (CBR) approach. The proposed method analyzes and cleans the data in RTS games and converts the learned knowledge into a probabilistic model, i.e., a Dynamic Bayesian Network (DBN), for representation and predication of player behaviors. Each DBN is constructed as a case to represent a prototypical player's behavior in the game, thus if more cases are constructed the simulation of different types of players in a multi-players game is made possible. Sixty sets of replay data of a prototypical player is chosen to test our idea, fifty cases for learning and ten cases for testing, and the experimental result is very promising.