A hierarchical architecture for behavior-based robots
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
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Previous research has used behavior hierarchies to address the problem of coordinating large numbers of behaviors. However, behavior hierarchies scale poorly since they require the state information of low-level behaviors. Abstracting this state information into priorities has recently been introduced to resolve this problem. In this work, we evaluate both the quality of priority-based behavior hierarchies and their ease of development. This is done by using grammatical evolution to learn how to coordinate low-level behaviors to accomplish a task. We show that not only do priority-based behavior hierarchies perform just as well as standard hierarchies but that they promote faster learning of solutions that are better suited as components in larger hierarchies.