Automated Playtesting with Procedural Personas through MCTS with Evolved
  Heuristics
  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 play styles 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.
