Skip to main content

Planning as Satisfiability with Relaxed \(\exists\)-Step Plans

  • Conference paper
Book cover AI 2007: Advances in Artificial Intelligence (AI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4830))

Included in the following conference series:

Abstract

Planning as satisfiability is a powerful approach to solving domain independent planning problems. In this paper, we consider a relaxed semantics for plans with parallel operator application based on \(\exists\)-step semantics. Operators can be applied in parallel if there is at least one ordering in which they can be sequentially executed. Under certain conditions, we allow them to be executed simultaneously in a state s even if not all of them are applicable in s. In this case, we guarantee that they are enabled by other operators that are applied at the same time point. We formalize the semantics of parallel plans in this setting, and propose an effective translation for STRIPS problems into the propositional logic. We finally show that this relaxed semantics yields an approach to classical planning that is sometimes much more efficient than the existing SAT-based planners.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kautz, H., Selman, B.: Pushing the Envelope: Planning, Propositional Logic, and Stochastic Search. In: Proceedings of the 13th National Conference on Artificial Intelligence, pp. 1194–1201 (1996)

    Google Scholar 

  2. Dimopoulos, Y., Nebel, B., Koehler, J.: Encoding Planning Problems in Nonmonotonic Logic Programs. In: Proceedings of the 4th European Conference on Planning, pp. 169–181 (1997)

    Google Scholar 

  3. Rintanen, J., Heljanko, K., Niemelä, I.: Planning as Satisfiability: Parallel Plans and Algorithms for Plan Search. Artificial Intelligence 170, 1031–1080 (2006)

    Article  MATH  MathSciNet  Google Scholar 

  4. Kautz, H., Selman, B., Hoffmann, J.: SatPlan: Planning as Satisfiability. In: Abstracts of the 5th International Planning Competition (2006)

    Google Scholar 

  5. Rintanen, J.: A Planning Algorithm not Based on Directional Search. In: Proceedings of the 6th International Conference on Principles of Knowledge Representation and Reasoning, pp. 617–624 (1998)

    Google Scholar 

  6. Tarjan, R.E.: Depth-First Search and Linear Graph Algorithms. SIAM Journal on Computing 1, 146–160 (1972)

    Article  MATH  MathSciNet  Google Scholar 

  7. Ryan, L.: Efficient Algorithms for Clause-Learning SAT Solvers. Master’s thesis, Simon Fraser University (2004)

    Google Scholar 

  8. Kautz, H., Selman, B.: Planning as Satisfiability. In: Proceedings of the 10th European Conference on Artificial Intelligence, pp. 359–363 (1992)

    Google Scholar 

  9. Kautz, H., Selman, B.: Unifying SAT-based and Graph-based Planning. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 318–325 (1999)

    Google Scholar 

  10. Blum, A.L., Furst, M.L.: Fast Planning through Planning Graph Analysis. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, pp. 1636–1642 (1995)

    Google Scholar 

  11. Cayrol, M., Régnier, P., Vidal, V.: Least Commitment in Graphplan. Artificial Intelligence 130, 85–118 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  12. van den Briel, M., Vossen, T., Kambhampati, S.: Reviving Integer Programming Approaches for AI Planning: A Branch-and-Cut Framework. In: Proceedings of the 15th International Conference on Automated Planning and Scheduling, pp. 310–319 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Mehmet A. Orgun John Thornton

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wehrle, M., Rintanen, J. (2007). Planning as Satisfiability with Relaxed \(\exists\)-Step Plans. In: Orgun, M.A., Thornton, J. (eds) AI 2007: Advances in Artificial Intelligence. AI 2007. Lecture Notes in Computer Science(), vol 4830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76928-6_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76928-6_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76926-2

  • Online ISBN: 978-3-540-76928-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics