Structure Learning of a Behavior Network for Context Dependent Adaptability

One mechanism for an intelligent agent to adapt to substantial environmental changes is to change the structure of its behavior network. In earlier work, we developed a context-dependent behavior selection architecture that uses structure change as the main mechanism to generate different behavior patterns according to different behavioral contexts. This paper investigates how the structure of such a behavior network can be learned. We present a structure learning method based on generic algorithm (GA). The results show that given a particular dynamic environment, consistent and robust structure can be learned to allow an agent to behave adaptively.