In the first post related to flexibility in behaviour trees I argued that behaviour trees and state machines can accomplish similar things, and that there were certain benefits to using each implementation method. I recently watched a GDC 2017 talk by Bobby Anguelov that redefined my perspective on these issues.
The picture is a limited access link to video of "AI Arborist: Proper Cultivation and Care for Your Behavior Trees" Bobby starts at 20 min.
Much of what I proposed in previous camera experiments models cyclic behaviour on cameras with behaviour trees, and I did not know why that should not be done until watching this talk. In fact, he suggests state machines are clearly better than behaviour trees at high level decision making. He provides examples where the acyclic data structure of behaviour trees complicate implementation and make it inefficient. However, Bobby does not think behaviour trees are all bad.
The main takeaway from his talk is to use behaviour trees only after high level decisions have already been made, where their ability for parallel execution of tasks can supplement the cyclic logic of state machines. The rest of this post describes how his approach would integrate with previous camera implementations I have discussed, and applies this new knowledge to guidance of future experiments with behaviour trees on cameras.
The main takeaway from his talk is to use behaviour trees only after high level decisions have already been made, where their ability for parallel execution of tasks can supplement the cyclic logic of state machines. The rest of this post describes how his approach would integrate with previous camera implementations I have discussed, and applies this new knowledge to guidance of future experiments with behaviour trees on cameras.
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