CaMeL: Learning Method Preconditions for HTN Planning

Abstract

A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the domain knowledge that the system will need. We present a way to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system learn the HTN methods incrementally under supervision of an expert. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL's soundness, completeness, and convergence properties. We also report experimental results about its speed of convergence under different conditions. The experimental results suggest that CaMeL has the potential to be useful in real-world applications.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA448055

Entities

People

  • Dana S. Nau
  • David W. Aha
  • Hector Munoz-avila
  • Okhtay Ilghami

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Autonomy
  • Counter WMD

DTIC Thesaurus Topics

  • Abstracts
  • Acquisition
  • Air Force Research Laboratories
  • Algorithms
  • Artificial Intelligence
  • Computer Science
  • Computers
  • Convergence
  • Decomposition
  • Department Of Defense
  • Learning
  • Machine Learning
  • Military Research
  • Random Variables
  • Supervised Machine Learning
  • Training
  • Universities

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Virology (or Medical Virology).

Technology Areas

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