One mechanism for an intelligent agent to adapt to substantial environmental changes is to change its decision making structure. Pervious work in this area has developed a context-dependent behavior selection architecture that uses structure change, i.e., changing the mutual inhibition structures of a behavior network, as the main mechanism to generate different behavior patterns according to different behavioral contexts. Given the important of network structure, this work investigates how the structure of a behavior network can be learned. We developed a structure learning method based on generic algorithm and applied it to a model crayfish that needs to survive in a simulated environment. The model crayfish is controlled by a mutual inhibition behavior network, whose structures are learned using the GA-based algorithm for different environment configurations. The results show that it is possible to learn robust and consistent network structures allowing intelligent agents to behave adaptively in a particular environment.

Modeling and simulation provides a powerful technology for engineers and managers to understand, design, and evaluate a system under development. Traditionally, simulation is only used in early stages of a system design. However, with the advances of hardware and software technology, it is now possible to extend simulation to late stages for supporting a full life cycle simulation-based development. Robot-in-the-loop simulation, where real robots work together with virtual ones, has been developed to support such a development process to bridge the gap between simulation and reality.