Case-based reactive navigation: A case-based method for on-line selection and adaptation of reactive

This article presents a new line of research investigating on-line learning mechanisms for autonomous intelligent agents. We discuss a case-based method for dynamic selection and modi cation of behavior assemblages for a navigational system. The casebased reasoning module is designed as an addition to a traditional reactive control system, and provides more exible performance in novel environments without extensive high-level reasoning that would otherwise slow the system down. The method is implemented in the ACBARR (A Case-BAsed Reactive Robotic) system, and evaluated through empirical simulation of the system on several di erent environments, including \box canyon" environments known to be problematic for reactive control systems in

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