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dc.contributor.authorRam, Ashwin
dc.contributor.authorArkin, Ronald C.
dc.contributor.authorClark, Russell J.
dc.contributor.authorMoorman, Kenneth
dc.date.accessioned2008-06-09T17:50:01Z
dc.date.available2008-06-09T17:50:01Z
dc.date.issued1992
dc.identifier.urihttp://hdl.handle.net/1853/22445
dc.description.abstractThis 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.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectAdaptive controlen_US
dc.subjectBehavior assemblagesen_US
dc.subjectCase-based reasoningen_US
dc.subjectReactive controlen_US
dc.titleCase-Based Reactive Navigation: A Cased Based Method for On-Line Selection and Adaptation of Reactive Control Parameters in Autonomous Robotics Systemsen_US
dc.typePaperen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing


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  • Mobile Robot Laboratory Publications [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.
  • Mobile Robot Laboratory [187]
    Papers, pre/post-prints, and presentations by faculty and students in the Georgia Tech Mobile Robot Laboratory.

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