Explanation-Based Learning with Plausible Inferencing

Abstract

This paper represents a synthesis of ideas from qualitative reasoning and explanation-based learning. Taken together they form a novel approach to planning that relies on plausible inferencing and applies to continuously varying rather than discrete world states. Interestingly, the frame problem skirted and the approach admits some forms of planning under uncertainty. Planning in a domain is very efficient, although learning about the domain can be time consuming. The approach possess a kind of natural reactivity. Keywords: Explanation based learning, Planning, Learning to plan, Continuous domains, Knowledge level learning.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1990
Accession Number
ADA220931

Entities

People

  • Gerald Dejong

Organizations

  • University of Illinois Urbana–Champaign

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  • Air Platforms
  • Autonomy
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Aircrafts
  • Artificial Intelligence
  • Classification
  • Cognitive Science
  • Computer Science
  • Computers
  • Gas Flow
  • Gases
  • Illinois
  • Lisp Programming Language
  • Machine Learning
  • Numerical Analysis
  • Reasoning
  • Security
  • Uncertainty
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  • Education

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  • Artificial Intelligence