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Behavior-based systems (BBS) have been effective in a variety of applications, but due to their limited use of representation they have not been applied much to more complex problems, such as ones involving temporal sequences, or hierarchical task representations. This paper presents an approach to implementing these AI-level concepts into BBS, without compromising BBS' key properties. We describe a Hierarchical Abstract Behavior Architecture that allows for the representation and execution of complex, sequential, hierarchically structured tasks within a behavior-based framework. The architecture, obtained by introducing the notion of abstract behaviors into BBS, also enables reusability of behaviors across different tasks. The basis for task representation is the behavior network construct which encodes complex, hierarchical plan-like strategies. The approach is validated in experiments on a Pioneer 2DX mobile robot.
Nicolescu et al. (2002) studied this question.