Type of Document Master's Thesis Author Li, Ou URN etd-12072006-144122 Title STRUCTURE LEARNING OF A BEHAVIOR NETWORK FOR CONTEXT DEPENDENT ADAPTABILITY Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Dr. Xiaolin Hu Committee Chair Dr. Raj Sunderraman Committee Member Dr. Ying Zhu Committee Member Keywords
- Structure learning
- Mutual Inhibition Behavior Network
Date of Defense 2006-11-20 Availability unrestricted Abstract 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.Files
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