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Bayesian Inference driven Behavior-Network Architecture for Intelligent Agent to Avoid Collision with Moving Obstacles  

민현정 (연세대학교 컴퓨터학과)
조성배 (연세대학교 컴퓨터산업공학부)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.8, 2004 , pp. 1073-1082 More about this Journal
Abstract
This paper presents a technique for an agent to adaptively behave to unforeseen and dynamic circumstances. Since the traditional methods utilized the information about an environment to control intelligent agents, they were robust but could not behave adaptively in a complex and dynamic world. A behavior-based method is suitable for generating adaptive behaviors within environments, but it is necessary to devise a hybrid control architecture that incorporates the capabilities of inference, learning and planning for high-level abstract behaviors. This Paper proposes a 2-level control architecture for generating adaptive behaviors to perceive and avoid dynamic moving obstacles as well as static obstacles. The first level is behavior-network for generating reflexive and autonomous behaviors, and the second level is to infer dynamic situation of agents. Through simulation, it has been confirmed that the agent reaches a goal point while avoiding static and moving obstacles with the proposed method.
Keywords
behavior network; Bayesian network; avoiding moving obstacles; behavior-based AI;
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