TY - JOUR
T1 - Automated playtesting with procedural personas through MCTs with evolved heuristics
AU - Holmgård, Christoffer
AU - Green, Michael Cerny
AU - Liapis, Antonios
AU - Togelius, Julian
N1 - Publisher Copyright:
© 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
PY - 2019/12
Y1 - 2019/12
N2 - This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo tree search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas, we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different playstyles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.
AB - This paper describes a method for generative player modeling and its application to the automatic testing of game content using archetypal player models called procedural personas. Theoretically grounded in psychological decision theory, procedural personas are implemented using a variation of Monte Carlo tree search (MCTS) where the node selection criteria are developed using evolutionary computation, replacing the standard UCB1 criterion of MCTS. Using these personas, we demonstrate how generative player models can be applied to a varied corpus of game levels and demonstrate how different playstyles can be enacted in each level. In short, we use artificially intelligent personas to construct synthetic playtesters. The proposed approach could be used as a tool for automatic play testing when human feedback is not readily available or when quick visualization of potential interactions is necessary. Possible applications include interactive tools during game development or procedural content generation systems where many evaluations must be conducted within a short time span.
KW - Agent controllers
KW - Automated playtesting
KW - Play persona
KW - Player modeling
UR - http://www.scopus.com/inward/record.url?scp=85089098499&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85089098499&partnerID=8YFLogxK
U2 - 10.1109/TG.2018.2808198
DO - 10.1109/TG.2018.2808198
M3 - Article
AN - SCOPUS:85089098499
SN - 2475-1502
VL - 11
SP - 352
EP - 362
JO - IEEE Transactions on Games
JF - IEEE Transactions on Games
IS - 4
M1 - 2808198
ER -