Being the sole AI guy on my capstone project (Remote Shepherd) has had me buried in finite state machines and behavior trees for the past 17 weeks. Obviously there are differences in the two approaches, both have pros and cons, but as I was working out the behaviors of a particular NPC for Remote Shepherd (specifically, the Mob Agent) I came across something interesting and useful: the differences in the way each implementation handles spinning on a behavior. The short version is that when an FSM spins on a state it only cares about that single state, when a behavior tree spins on a node it still checks the relevance functions of every node with higher priority. For the long version I'll start with describing the behavior I was trying to model that led to this realization.
