Learning by Experimentation

Abstract

This research note addresses the issue of learning by experimentation as an integral component of PRODIGY, a flexible planning system augmented with capabilities for execution monitoring, and dynamic replanning upon adverse feedback. A detailed example of integrated experiment formulation in presented as the basis for a systematic approach to extending an incomplete domain theory or correcting a potentially inaccurate one. Keywords: Artificial intelligence, Machine learning, Problem solving.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1987
Accession Number
ADA188912

Entities

People

  • Jaime Carbonell

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Acquisition
  • Aluminum
  • Climate Change
  • Computer Science
  • Environment
  • Expert Systems
  • Ground Zero
  • Intelligence Planning
  • Learning
  • Machines
  • Military Research
  • Polishes
  • Polishing
  • Social Sciences
  • Students
  • Telescopes
  • Vacuum Chambers

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms