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dc.contributor.authorRam, Ashwin
dc.contributor.authorSantamaria, Juan Carlos
dc.description.abstractMany techniques for speedup learning and knowledge compilation focus on the learning and optimization of macro-operators or control rules in task domains that can be characterized using a problem-space search paradigm. However, such a characterization does not fit well the class of task domains in which the problem solver is required to perform in a continuous manner. For example, in many robotic domains, the problem solver is required to monitor real-valued perceptual inputs and vary its motor control parameters in a continuous, on-line manner to successfully accomplish its task. In such domains, discrete symbolic states and operators are difficult to define. To improve its performance in continuous problem domains, a problem solver must learn, modify, and use “continuous operators” that continuously map input sensory information to appropriate control outputs. Additionally, the problem solver must learn the contexts in which those continuous operators are applicable. We propose a learning method that can compile sensorimotor experiences into continuous operators, which can then be used to improve performance of the problem solver. The method speeds up the task performance as well as results in improvements in the quality of the resulting solutions. The method is implemented in a robotic navigation system, which is evaluated through extensive experimentation.en_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectCase-based reasoningen_US
dc.subjectContinuous operatorsen_US
dc.subjectReinforcement learningen_US
dc.subjectSpeedup learningen_US
dc.titleKnowledge Compilation and Speedup Learning in Continuous Task Domainsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing

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

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