Techniques for Plan Recognition
User Modeling and User-Adapted Interaction
Player modeling using self-organization in tomb raider: underworld
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Mining game statistics from web services: a World of Warcraft armory case study
Proceedings of the Fifth International Conference on the Foundations of Digital Games
Player performance prediction in massively multiplayer online role-playing games (MMORPGs)
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Virtual postcards: multimodal stories of online play
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Theoretical and methodological challenges (and opportunities) in virtual worlds research
Proceedings of the International Conference on the Foundations of Digital Games
Mastering the art of war: how patterns of gameplay influence skill in Halo
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A large-scale, longitudinal study of user profiles in world of warcraft
Proceedings of the 22nd international conference on World Wide Web companion
Skill-based Mission Generation: A Data-driven Temporal Player Modeling Approach
Proceedings of the The third workshop on Procedural Content Generation in Games
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In this paper, we present a data-driven technique for designing models of user behavior. Previously, player models were designed using user surveys, small-scale observation experiments, or knowledge engineering. These methods generally produced semantically meaningful models that were limited in their applicability. To address this, we have developed a purely data-driven methodology for generating player models based on past observations of other players. Our underlying assumption is that we can accurately predict what a player will do in a given situation if we examine enough data from former players that were in similar situations. We have chosen to test our method on achievement data from the MMORPG World of Warcraft. Experiments show that our method greatly outperforms a baseline algorithm in both precision and recall, proving that this method can create accurate player models based solely on observation data.