Augmentation-Based Learning combining observations and user edits for Programming-by-Demonstration

  • Authors:
  • Vittorio Castelli;Daniel Oblinger;Lawrence Bergman

  • Affiliations:
  • IBM T.J. Watson Research Center, 1101 Kitchawan Road - Route 134, Yorktown Heights, NY 10598, USA;DARPA/IPTO, 3701 Fairfax Dr, Arlington, VA 22203, USA;IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA

  • Venue:
  • Knowledge-Based Systems
  • Year:
  • 2007

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Abstract

In this paper, we introduce a new approach to Programming-by-Demonstration in which the author is allowed to explicitly edit the procedure model produced by the learning algorithm while demonstrating the task. We describe Augmentation-Based Learning, a new algorithm that supports this approach by considering both demonstrations and edits as constraints on the hypothesis space, and resolving conflicts in favor of edits.