Exploring Disjunctive Planning as a Means to Scale-Up Plan Synthesis

Abstract

In this research, we have concentrated on exploring the advantages of disjunctive plan representations in improving planning performance. The results of the research included: (1) development of a system called GP-CSP, which uses direct constraint satisfaction techniques to solve the planning graph of Graphplan; (2) development of a method where the distance heuristics that were originally developed as part of the heuristic state search community can be applied to the backward search of Graphplan, a disjunctive planner; and (3) evaluation of the utility of different types of mutex propagation routines in the context of SAT and CSP encodings of the automated planning problems. The results of this research have been disseminated to the planning community through three publications in the 5th International Conference on Artificial Intelligence Planning Scheduling and one publication in the 2000 National Conference on Artificial Intelligence.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2001
Accession Number
ADA398018

Entities

People

  • Sabbarao Kambhampati

Organizations

  • Arizona State University

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Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks
  • Space
  • Space - Spacecraft Maneuvers