HyPED: Modeling and Analyzing Action Games as Hybrid Systems

Abstract

Platformers and action-adventure games have high-dimensional state spaces with difficult, non-linear constraints on character movement; even worse, game environments often respond to the player in complex ways that can cause exponential expansion of the planning search space. Planning problems in these high-dimensional spaces generally require domain-specific knowledge and manually abstracted models of game rules to replicate the intuition of human designers or playtesters. In this work, we outline a system for modeling these complex games at a precise and low level in terms of hybrid automata. With this representation, standard incremental search algorithms can be used to answer reachable-region queries, taking advantage of the domain information embedded in the system.