Active and Interactive Discovery of Goal Selection Knowledge

Abstract

If given manually-crafted goal selection knowledge, goal reasoning agents can dynamically determine which goals they should achieve in complex environments. These agents should instead learn goal selection knowledge through expert interaction. We describe T-ARTUE, a goal reasoning agent that performs case-based active and interactive learning to discover goal selection knowledge. We also report tests of its performance in a complex environment. We found that, under some conditions, T-ARTUE can quickly learn goal selection knowledge.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA559929

Entities

People

  • David W. Aha
  • Jay Powell
  • Matthew Molineaux

Organizations

  • Indiana University Bloomington

Tags

Communities of Interest

  • Autonomy
  • Counter WMD
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Autonomous Agents
  • Autonomy
  • Computer Science
  • Computers
  • Demographic Cohorts
  • Distance Learning
  • Environment
  • Errors
  • Generators
  • Learning
  • Military Research
  • Navy
  • Reasoning
  • Simulators
  • Time Intervals

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Psychometric Testing or Psychological Assessment.