Extending adaptive fuzzy behavior hierarchies to multiple levels of composite behaviors

We propose an extended version of adaptive fuzzy behavior hierarchies, termed Multiple Composite Levels (MCL), that allows for the proper modulation of composite behaviors over multiple levels of a behavior hierarchy, and demonstrate its effectiveness for a hybrid learning/reactive control system. Controllers using adaptive fuzzy behavior hierarchies have previously been shown to provide effective control for robots tasked with multiple concurrent tasks. However, when more complex hierarchies are used to provide control for tasks of increasing complexity, low-level reactive behaviors may not be properly weighted, resulting in sub-optimal control. Through experimental evaluation in which composite behaviors that coordinate lower behaviors are learned using reinforcement learning, we demonstrate that MCL provides effective control in a complex multi-agent task, whereas the original implementation of adaptive fuzzy behavior hierarchies does not.