Substructure Discovery of Macro-Operators

Abstract

This paper describes an implemented system, PLAND (Plan Discovery), for discovering substructures in observed action sequences. The goal is to show how a system can learn useful macro-operators by observing a task being performed. An intelligent robot using this system could learn how to perform new tasks by watching tasks being performed by someone else, even if the robot does not possess a complete understanding of the actions being observed. Macro-operators are discovered within a specific context that provides the types of generalizations allowed in the discovery process and uses the previously proposed macro-operators to build new ones. Background knowledge is used to determine which generalizations are appropriate and to control search. The system can discover syntactic structures (grammars) without background knowledge, but more meaningful and useful structures are discovered when background knowledge is incorporated into the process. The foundations of PLAND are in similarity-difference-based (SDBL) learning systems that perform conceptual clustering; however unline most SDBL systems, a large amount of background knowledge can be incorporated to improve learning effectiveness.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1988
Accession Number
ADA197549

Entities

People

  • Bradley L. Whitehall

Organizations

  • University of Illinois Urbana–Champaign

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  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Acquisition
  • Algorithms
  • Applied Computer Science
  • Artificial Intelligence
  • Classification
  • Clustering
  • Computer Science
  • Electrical Engineering
  • Engineering
  • Illinois
  • Learning
  • Machine Learning
  • Military Research
  • Security
  • Sequences
  • Universities

Fields of Study

  • Computer science

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  • Artificial Intelligence

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • Autonomy