(Georgia Institute of Technology, 1992)
Ram, Ashwin; Arkin, Ronald C.; Clark, Russell J.; Moorman, Kenneth
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.