Task decomposition on abstract states, for planning under nondeterminism

  • Authors:

  • Ugur Kuter;Dana Nau;Marco Pistore;Paolo Traverso

  • Affiliations:

  • Department of Computer Science and Institute of Systems Research and Institute of Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA;Department of Computer Science and Institute of Systems Research and Institute of Advanced Computer Studies, University of Maryland, College Park, MD 20742, USA;Institute for Scientific and Technological Research (IRST), Fondazione Bruno Kessler, Via Sommarive 18, Povo, 38050 Trento, Italy;Institute for Scientific and Technological Research (IRST), Fondazione Bruno Kessler, Via Sommarive 18, Povo, 38050 Trento, Italy

  • Venue:
  • Artificial Intelligence
  • Year:
  • 2009

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Abstract

Although several approaches have been developed for planning in nondeterministic domains, solving large planning problems is still quite difficult. In this work, we present a new planning algorithm, called Yoyo, for solving planning problems in fully observable nondeterministic domains. Yoyo combines an HTN-based mechanism for constraining its search and a Binary Decision Diagram (BDD) representation for reasoning about sets of states and state transitions. We provide correctness theorems for Yoyo, and an experimental comparison of it with MBP and ND-SHOP2, the two previously-best algorithms for planning in nondeterministic domains. In our experiments, Yoyo could easily deal with problem sizes that neither MBP nor ND-SHOP2 could scale up to, and could solve problems about 100 to 1000 times faster than MBP and ND-SHOP2.