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Heuristics for Planning with Action Costs

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Book cover Current Topics in Artificial Intelligence (CAEPIA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4788))

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Abstract

We introduce a non-admissible heuristic for planning with action costs, called the set-additive heuristic, that combines the benefits of the additive heuristic used in the HSP planner and the relaxed plan heuristic used in FF. The set-additive heuristic \(h^s_a\) is defined mathematically and handles non-uniform action costs like the additive heuristic h a , and yet like FF’s heuristic \(h_{\textrm{\scriptsize FF}}\), it encodes the cost of a specific relaxed plan and is therefore compatible with FF’s helpful action pruning and its effective enforced hill climbing search. The definition of the set-additive heuristic is obtained from the definition of the additive heuristic, but rather than propagating the value of the best supports for a precondition or goal, it propagates the supports themselves, which are then combined by set-union rather than by addition. We report then empirical results on a planner that we call FF(\(h^s_a\)) that is like FF except that the relaxed plan is extracted from the set-additive heuristic. The results show that FF(\(h^s_a\)) adds only a slight time overhead over FF but results in much better plans when action costs are not uniform.

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References

  1. Bonet, B., Geffner, H.: Planning as heuristic search. Artificial Intelligence 129(1-2), 5–33 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  2. Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302 (2001)

    MATH  Google Scholar 

  3. Sapena, O., Onaindia, E.: Handling numeric criteria in relaxed planning graphs. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 114–123. Springer, Heidelberg (2004)

    Google Scholar 

  4. Fuentetaja, R., Borrajo, D., Linares, C.: Improving relaxed planning graph heuristics for metric optimization. In: Proc. 2006 AAAI Workshop on Heuristic Search, Memory Based Heuristics and its Applications, pp. 79–86 (2006)

    Google Scholar 

  5. Blum, A., Furst, M.: Fast planning through planning graph analysis. In: Proceedings of IJCAI 1995, pp. 1636–1642. Morgan Kaufmann, San Francisco (1995)

    Google Scholar 

  6. Haslum, P., Geffner, H.: Admissible heuristics for optimal planning. In: AIPS-2000. Proc. of the Fifth International Conference on AI Planning Systems, pp. 70–82 (2000)

    Google Scholar 

  7. Bonet, B., Loerincs, G., Geffner, H.: A robust and fast action selection mechanism for planning. In: Proceedings of AAAI 1997, pp. 714–719. MIT Press, Cambridge (1997)

    Google Scholar 

  8. Do, M.B., Kambhampati, S.: Sapa: A domain-independent heuristic metric temporal planner. In: Proc. ECP 2001, pp. 82–91 (2001)

    Google Scholar 

  9. Smith, D.E.: Choosing objectives in over-subscription planning. In: Proc. ICAPS 2004, pp. 393–401 (2004)

    Google Scholar 

  10. Bertsekas, D.: Linear Network Optimization: Algorithms and Codes. MIT Press, Cambridge (1991)

    MATH  Google Scholar 

  11. Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge (1989)

    Google Scholar 

  12. Liu, Y., Koenig, S., Furcy, D.: Speeding up the calculation of heuristics for heuristic search-based planning. In: Proc AAAI 2002, pp. 484–491 (2002)

    Google Scholar 

  13. Blizard, W.D.: Multiset theory. Notre Dame J. Formal Logic 30(1), 36–66 (1988)

    Article  MathSciNet  Google Scholar 

  14. Hoffmann, J.: The metric-ff planning system: Translating ”ignoring delete lists” to numeric state variables. J. Artif. Intell. Res (JAIR) 20, 291–341 (2003)

    MATH  Google Scholar 

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Daniel Borrajo Luis Castillo Juan Manuel Corchado

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© 2007 Springer-Verlag Berlin Heidelberg

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Keyder, E., Geffner, H. (2007). Heuristics for Planning with Action Costs. In: Borrajo, D., Castillo, L., Corchado, J.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2007. Lecture Notes in Computer Science(), vol 4788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75271-4_15

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  • DOI: https://doi.org/10.1007/978-3-540-75271-4_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75270-7

  • Online ISBN: 978-3-540-75271-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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