Paper Info
Title
Skill-based Mission Generation: A Data-driven Temporal Player Modeling Approach
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.
Year
DOI
Venue
2012
10.1145/2538528.2538534
PCG@FDG
Keywords
Field
DocType
data-driven player model,data-driven temporal player modeling,player difference,mission generation,skill-based game,skill-based event,automated mission generator,missing player data,skill-based mission generation,player performance,preferred player performance,player ability,optimization
Data-driven,Predictive power,Computer science,Combinatorial optimization,Artificial intelligence,Tensor factorization
Conference
Citations 
PageRank 
References 
12
0.58
22
Authors
4
Name
Order
Citations
PageRank
Alexander Zook111710.45
Stephen Lee-Urban218212.40
Michael R. Drinkwater3120.58
Mark Riedl41254127.14