Improving Heuristics On-the-fly for Effective Search in Plan Space

  • Shashank ShekharEmail author
  • Deepak Khemani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9324)


The design of domain independent heuristic functions often brings up experimental evidence that different heuristics perform well in different domains. A promising approach is to monitor and reduce the error associated with a given heuristic function even as the planner solves a problem. We extend this single-step-error adaptation to heuristic functions from Partial Order Causal Link (POCL) planning. The goal is to allow a partial order planner to observe the effective average-step-error during search. The preliminary evaluation shows that our approach improves the informativeness of the state-of-the-art heuristics. Our planner solves more problems by using the improved heuristics as compared to when it uses current heuristics in the selected domains.


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  1. 1.
    Arfaee, S.J., Zilles, S., Holte, R.C.: Learning heuristic functions for large state spaces. Artificial Intelligence 175(16), 2075–2098 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bercher, P., Biundo, S.: Encoding partial plans for heuristic search. In: Proceedings of the 4th Workshop on Knowledge Engineering for Planning and Scheduling (KEPS 2013), pp. 11–15. Citeseer (2013)Google Scholar
  3. 3.
    Bercher, P., Geier, T., Biundo, S.: Using state-based planning heuristics for partial-order causal-link planning. In: Timm, I.J., Thimm, M. (eds.) KI 2013. LNCS, vol. 8077, pp. 1–12. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  4. 4.
    Blum, A.L., Furst, M.L.: Fast planning through planning graph analysis. Artificial Intelligence 90(1), 281–300 (1997)CrossRefzbMATHGoogle Scholar
  5. 5.
    Bonet, B., Geffner, H.: Planning as heuristic search. Artificial Intelligence 129(1), 5–33 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Bonet, B., Loerincs, G., Geffner, H.: A robust and fast action selection mechanism for planning. In: AAAI/IAAI. pp. 714–719 (1997)Google Scholar
  7. 7.
    Haslum, P., Geffner, H.: Admissible heuristics for optimal planning. In: AIPS, pp. 140–149. Citeseer (2000)Google Scholar
  8. 8.
    Hoffmann, J.: FF: The fast-forward planning system. AI Magazine 22(3), 57 (2001)Google Scholar
  9. 9.
    Jesús Virseda, J., Borrajo, D., Alcázar, V.: Learning heuristic functions for cost-based planning. Planning and Learning, 6 (2013)Google Scholar
  10. 10.
    Joslin, D., Pollack, M.E.: Least-cost flaw repair: A plan refinement strategy for partial-order planning (1994)Google Scholar
  11. 11.
    McAllester, D., Rosenblatt, D.: Systematic nonlinear planning (1991)Google Scholar
  12. 12.
    McDermott, D.: Using regression-match graphs to control search in planning. Artificial Intelligence 109(1), 111–159 (1999)CrossRefzbMATHGoogle Scholar
  13. 13.
    Nguyen, X., Kambhampati, S.: Extracting effective and admissible state space heuristics from the planning graph. In: AAAI/IAAI, pp. 798–805 (2000)Google Scholar
  14. 14.
    Nguyen, X., Kambhampati, S.: Reviving partial order planning. In: IJCAI, vol. 1, pp. 459–464 (2001)Google Scholar
  15. 15.
    Penberthy, J.S., Weld, D.S.: UCPOP: A sound, complete, partial order planner for ADL, pp. 103–114. Morgan Kaufmann (1992)Google Scholar
  16. 16.
    Samadi, M., Felner, A., Schaeffer, J.: Learning from multiple heuristics. In: AAAI, pp. 357–362 (2008)Google Scholar
  17. 17.
    Schattenberg, B.: Hybrid Planning And Scheduling. Ph.D. thesis, Ulm University, Institute of Artificial Intelligence (2009). URN: urn:nbn:de:bsz:289-vts-68953Google Scholar
  18. 18.
    Schubert, L., Gerevini, A.: Accelerating partial order planners by improving plan and goal choices. In: Proceedings of the Seventh International Conference on Tools with Artificial Intelligence, pp. 442–450. IEEE (1995)Google Scholar
  19. 19.
    Thayer, J.T., Dionne, A.J., Ruml, W.: Learning inadmissible heuristics during search. In: ICAPS (2011)Google Scholar
  20. 20.
    Weld, D.S.: AAAI-10 classic paper award: Systematic nonlinear planning a commentary. AI Magazine 32(1), 101 (2011)Google Scholar
  21. 21.
    Younes, H.L., Simmons, R.G.: On the role of ground actions in refinement planning. In: AIPS, pp. 54–62 (2002)Google Scholar
  22. 22.
    Younes, H.L., Simmons, R.G.: Versatile Heuristic Partial Order Planner. J. Artif. Intell. Res. (JAIR) 20, 405–430 (2003)zbMATHGoogle Scholar

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© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyChennaiIndia

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