A Relational Representation for Procedural Task Knowledge

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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA439204

Entities

People

  • David Jensen
  • Roderic Grupen
  • Stephen Hart

Organizations

  • University of Massachusetts Amherst

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Computational Science
  • Computer Science
  • Computer Vision
  • Computers
  • Data Mining
  • Databases
  • Experimental Data
  • Information Science
  • Machine Learning
  • Models
  • Organizational Structure
  • Probabilistic Models
  • Probability
  • Psychology
  • Robotics

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Database Systems and Applications
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • Autonomy