Expressive Response Curves: Testing Expressive Game Feel with A*

Abstract

Designing AI models for expressive character behavior is a considerable challenge. Such models represent a massive possibility space of individual behaviors and sequences of different character expressions. Iterating on designs of such models is complex because the possibility spaces they afford are challenging to understand in their entirety and map intuitively onto a meaningful experience for a user. Automated playtesting has primarily been focused on the physical spaces of game levels and the ability of AI players to enact personas and complete tasks within those levels. However, core principles of automated playtesting can be applied to expressive models to expose information about their expressive possibility space. We propose a new approach to automated playtesting for AI character behaviors: Expressive Response Curves (ERC). ERC allows us to map specific actions taken by a player to perform a particular expression to understand the affordances of an expressive possibility space. We present a case study applying ERC to Puppitor rulesets. We show that using this method we can compile paths through Puppitor rulesets to map them and further understand the nature of the expressive spaces afforded by the system. We argue that by using ERC, it is possible to give designers more nuanced information and guidance to create better and more expressive AI characters.