Learning Approximate Preconditions for Methods in Hierarchical Plans

Abstract

A significant challenge in developing planning systems for practical applications is the difficulty of acquiring the domain knowledge needed by such systems. One method for acquiring this knowledge is to learn it from plan traces, but this method typically requires a huge number of plan traces to converge. In this paper, we show that the problem with slow convergence can be circumvented by having the learner generate solution plans even before the planning domain is completely learned. Our empirical results show that these improvements reduce the size of the training set that is needed to find correct answers to a large percentage of planning problems in the test set.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2005
Accession Number
ADA448076

Entities

People

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

Organizations

  • University of Maryland

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computer Science
  • Computers
  • Convergence
  • Elimination
  • Hierarchies
  • Information Operations
  • Learning
  • Military Doctrine
  • Military Operations
  • Military Personnel
  • Observation
  • Precision
  • Random Variables
  • Test Sets
  • Training
  • Universities

Fields of Study

  • Computer science

Readers

  • Joint Military Operations and Doctrine.
  • Mathematical Modeling and Probability Theory.
  • Systems Analysis and Design