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 modification of behavior assemblages for a navigational system. The case-based reasoning module is designed as an addition to a traditional reactive control system, and provides more flexible 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 different environments, including “box canyon” environments known to be problematic for reactive control systems in general.
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Case-Based Reactive Navigation: A Case-Based Method for On-Line Selection and Adaptation of Reactive Control Parameters in Autonomous Robotic Systems
by Ashwin Ram, Ron Arkin, Kenneth Moorman, Russ Clark
IEEE Transactions on Systems, Man, and Cybernetics, 27B(3), 1997. Preliminary version published as Technical Report GIT-CC-92/57, College of Computing, Georgia Institute of Technology, Atlanta, GA, 1992www.cc.gatech.edu/faculty/ashwin/papers/git-cc-92-57.pdf
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Cognitive Computing 