Accession Number:

ADA439204

Title:

A Relational Representation for Procedural Task Knowledge

Descriptive Note:

Technical rept.

Corporate Author:

MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER SCIENCE

Report Date:

2005-01-01

Pagination or Media Count:

7.0

Abstract:

This paper proposes a methodology for learning joint probability estimates regarding the effect of sensorimotor features on the predicated quality of desired behavior. These relationships can the be used to choose actions that will most likely produce success. Relational dependency networks are used to learn statistical models of procedural task knowledge. An example task expert for picking up objects is learned through actual experience with a humanoid robot. The authors believe that this approach is widely applicable and has great potential to allow a robot to autonomously determine which features in the world are salient and should be used to recommend policy for action.

Subject Categories:

  • Cybernetics
  • Human Factors Engineering and Man Machine Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE