Skill-based Mission Generation: A Data-driven Temporal Player Modeling Approach

Authors:
Alexander Zook;Stephen Lee-Urban;Michael R. Drinkwater;Mark O. Riedl
Affiliations:
School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA;School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA;School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA;School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA
Venue:
Proceedings of the The third workshop on Procedural Content Generation in Games
Year:
2012

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Abstract

Games often interweave a story and series of skill-based events into a complete sequence---a mission. An automated mission generator for skill-based games is one way to synthesize designer requirements with player differences to create missions tailored to each player. We argue for the need for predictive, data-driven player models that meet the requirements of: (1) predictive power, (2) accounting for temporal changes in player abilities, (3) accuracy in the face of little or missing player data, (4) efficiency with large sets of data, and (5) sufficiency for algorithmic generation. We present a tensor factorization approach to modeling and predicting player performance on skill-based tasks that meets the above requirements and a combinatorial optimization approach to mission generation to interweave an author's preferred story structures and an author's preferred player performance over a mission---a kind of difficulty curve---with modeled player performance.