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Behavior selection is a problem that AI development has faced for years, whether it be in video games or crowd-simulation software. While video game studios use methods like hierarchical task network (HTN) planners to control the behaviors of their non-playable characters (NPCs), different crowd-simulation software implement various forms of crowd model, some even recently proposing using utility-based behavior models. In this paper, I propose a framework for designing complex behavior structures that can be adapted for individual NPCs, groups of NPCs, or crowds of other AI agents that more closely mimic how people would naturally behave in almost any situation. This framework is achieved through combining the ability to compute the possibility of a future event with the ability to decompose abstract tasks into a plan of action through the use of HTN planners.
