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

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.