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Generating Abstraction Hierarchies

An Automated Approach to Reducing Search in Planning

  • Craig A. Knoblock

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Craig A. Knoblock
    Pages 1-10
  3. Craig A. Knoblock
    Pages 11-22
  4. Craig A. Knoblock
    Pages 23-52
  5. Craig A. Knoblock
    Pages 53-84
  6. Craig A. Knoblock
    Pages 85-106
  7. Craig A. Knoblock
    Pages 107-118
  8. Craig A. Knoblock
    Pages 119-131
  9. Back Matter
    Pages 133-168

About this book

Introduction

Generating Abstraction Hierarchies presents a completely automated approach to generating abstractions for problem solving. The abstractions are generated using a tractable, domain-independent algorithm whose only inputs are the definition of a problem space and the problem to be solved and whose output is an abstraction hierarchy that is tailored to the particular problem. The algorithm generates abstraction hierarchies that satisfy the `ordered monotonicity' property, which guarantees that the structure of an abstract solution is not changed in the process of refining it. An abstraction hierarchy with this property allows a problem to be decomposed such that the solution in an abstract space can be held invariant while the remaining parts of a problem are solved. The algorithm for generating abstractions is implemented in a system called ALPINE, which generates abstractions for a hierarchical version of the PRODIGY problem solver. Generating Abstraction Hierarchies formally defines this hierarchical problem solving method, shows that under certain assumptions this method can reduce the size of a search space from exponential to linear in the solution size, and describes the implementation of this method in PRODIGY. The abstractions generated by ALPINE are tested in multiple domains on large problem sets and are shown to produce shorter solutions with significantly less search than problem solving without using abstraction. Generating Abstraction Hierarchies will be of interest to researchers in machine learning, planning and problem reformation.

Keywords

STRIPS algorithms learning machine learning problem solving robot

Authors and affiliations

  • Craig A. Knoblock
    • 1
  1. 1.University of Southern CaliforniaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-3152-4
  • Copyright Information Kluwer Academic Publishers 1993
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-6380-4
  • Online ISBN 978-1-4615-3152-4
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site