TY - GEN
T1 - Learning player behaviors in real time strategy games from real data
AU - Ng, P. H.F.
AU - Shiu, Chi Keung Simon
AU - Wang, H.
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
KW - Case-based Reasoning (CBR)
KW - Dynamic Bayesian Network (DBN)
KW - Junction Tree
KW - Real Time Strategy (RTS) Games
UR - http://www.scopus.com/inward/record.url?scp=76649083313&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-10646-0_39
DO - 10.1007/978-3-642-10646-0_39
M3 - Conference article published in proceeding or book
SN - 3642106455
SN - 9783642106453
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 321
EP - 327
BT - Rough Sets, Fuzzy Sets, Data Mining and Granular Computing - 12th International Conference, RSFDGrC 2009, Proceedings
T2 - 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009
Y2 - 15 December 2009 through 18 December 2009
ER -