Paper detail

Adaptive Artificial Intelligence in Games: Issues, Requirements, and a Solution through Behavlets-based General Player Modelling

We present the last of a series of three academic essays which deal with the question of how and why to build a generalized player model. We propose that a general player model needs parameters for subjective experience of play, including: player psychology, game structure, and actions of play. Based on this proposition, we pose three linked research questions: RQ1 what is a necessary and sufficient foundation to a general player model?; RQ2 can such a foundation improve performance of a computational intelligence- based player model?; and RQ3 can such a player model improve efficacy of adaptive artificial intelligence in games? We set out the arguments behind these research questions in each of the three essays, presented as three preprints. The third essay, in this preprint, presents the argument that adaptive game artificial intelligence will be enhanced by a generalised player model. This is because games are inherently human artefacts which therefore, require some encoding of the human perspective in order to effectively autonomously respond to the individual player. The player model informs the necessary constraints on the adaptive artificial intelligence. A generalised player model is not only more efficient than a per-game solution, but also allows comparison between games which makes it a useful tool for studying play in general. We describe the concept and meaning of an adaptive game. We propose requirements for functional adaptive AI, arguing from first principles drawn from the games research literature. We propose solutions to these requirements, based on a formal model approach to our existing 'Behavlets' method for psychologically-derived player modelling: Cowley, B., & Charles, D. (2016). Behavlets: a Method for Practical Player Modelling using Psychology-Based Player Traits and Domain Specific Features. User Modeling and User-Adapted Interaction, 26(2), 257-306.