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Planning with Uncertainty in Action Outcomes as Linear Programming Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5518))

Abstract

Planning is a difficult computational problem. One way to increase efficiency of searching for a solution may be a transformation of a problem to another problem and then search for a solution of the transformed problem. In this work a transformation of STRIPS planning problem with uncertainty of operators outcomes to linear programming is shown. The transformation from planning to Linear Programming is based on mapping of conditions and operators in each plan step to variables. Exemplary simulation shows properties of proposed approach.

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References

  1. Baral, C., Kreinovich, V., Trejo, R.: Computational complexity of planning and approxi-mate planning in presence of incompleteness. Artificial Intelligence 122, 241–267 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  2. Blythe, J.: An Overview of Planning Under Uncertainty. Pre-print from AI Magazine 20(2), 37–54 (Summer 1999)

    MathSciNet  Google Scholar 

  3. Bylander, T.: The computational complexity of propositional STRIPS planning. Artificial Intelligence 69, 165–204 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bylander, T.: A linear programming heuristic for optimal planning. In: Proceedings of the 14th National Conference on Artificial Intelligence, pp. 694–699 (1997)

    Google Scholar 

  5. Chaczijan, L.G.: A polynomial algorithm for linear programming. Dokl. Akad. Nauk SSSR 244, 1093–1096 (1979)

    MathSciNet  Google Scholar 

  6. Cocosco, C.A.: A review of STRIPS: A new approach to the application of theorem proving to problem solving by R.E. Fikes, N.J. Nillson, 1971. For 304-526B Artificial Intelligence (1998)

    Google Scholar 

  7. Galuszka, A., Swierniak, A.: Translation STRIPS Planning in Multi-robot Environment to Linear Programming. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS, vol. 3070, pp. 768–773. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  8. Gupta, N., Nau, D.S.: On the complexity of Blocks World planning. Artificial Intelligence 56(2-3), 223–254 (1992)

    Article  MathSciNet  MATH  Google Scholar 

  9. Jain, A., Hong, L., Pankanti, S.: Biometric identification. Communications of the ACM 43(2), 91–98 (2000)

    Article  Google Scholar 

  10. Kim, K.H., Hong, G.-P.: A heuristic rule for relocating blocks. Computers & Operations Research 33, 940–954 (2006)

    Article  MATH  Google Scholar 

  11. Koehler, J., Schuster, K.: Elevator Control as a Planning Problem. In: The Fifth International Conference on Artificial Intelligence Planning and Scheduling Systems Breckenridge, CO, April 15-19, pp. 331–338 (2000)

    Google Scholar 

  12. Nillson, N.J., Fikes, R.E.: STRIPS: A new approach to the application of theorem proving to problem solving. Technical Note 43, SRI Project 8259, Artificial Intelligence Group, Stanford Research Institute (1970)

    Google Scholar 

  13. Optimization Toolbox, Matlab® V7.4 (R2007a) user’s guide, www.mathworks.com

  14. Slaney, J., Thiebaux, S.: Block World revisited. Artificial Intelligence 125, 119–153 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  15. Weld, D.S.: Recent Advantages in AI Planning. AI Magazine 20(2) (1999)

    Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Galuszka, A., Holdyk, A. (2009). Planning with Uncertainty in Action Outcomes as Linear Programming Problem. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_62

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  • DOI: https://doi.org/10.1007/978-3-642-02481-8_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02480-1

  • Online ISBN: 978-3-642-02481-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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