Automated Service Composition Using Heuristic Search

  • Harald Meyer
  • Mathias Weske
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4102)


Automated service composition is an important approach to automatically aggregate existing functionality. While different planning algorithms are applied in this area, heuristic search is currently not used. Lacking features like the creation of compositions with parallel or alternative control flow are preventing its application. The prospect of using heuristic search for composition with quality of service properties motivated the extension of existing heuristic search algorithms.

In this paper we present a heuristic search algorithm for automated service composition. Based on the requirements for automated service composition, shortcomings of existing algorithms are identified, and solutions for them presented.


Processes and service composition Process planning and flexible workflow 


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  1. 1.
    Zeng, L., Benatallah, B., Lei, H., Ngu, A., Flaxer, D., Chang, H.: Flexible Composition of Enterprise Web Services. Electronic Markets – Web Services 13, 141–152 (2003)Google Scholar
  2. 2.
    Pistore, M., Barbon, F., Bertoli, P., Shaparau, D., Traverso, P.: Planning and monitoring web service composition. In: Workshop on Planning and Scheduling for Web and Grid Services, held in conjunction with The 14th International Conference on Automated Planning and Scheduling, pp. 70–71 (2004)Google Scholar
  3. 3.
    Sirin, E., Parsia, B., Wu, D., Hendler, J., Nau, D.: HTN planning for web service composition using shop2. Journal of Web Semantics 1, 377–396 (2004)CrossRefGoogle Scholar
  4. 4.
    Berardi, D., Calvanese, D., Giacomo, G.D., Mecella, M.: Composition of services with nondeterministic observable behaviour. In: Benatallah, B., Casati, F., Traverso, P. (eds.) ICSOC 2005. LNCS, vol. 3826, pp. 520–526. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Hoffmann, J.: Metric-FF planning system: Translating ”ignoring delete lists” to numeric state variables. Journal Of Artificial Intelligence Research 20, 291–341 (2003)zbMATHGoogle Scholar
  6. 6.
    Meyer, H., Kuropka, D.: Requirements for automated service composition. In: Eder, J., Dustdar, S. (eds.) BPM Workshops 2006. LNCS, vol. 4103, pp. 447–458. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Boddy, M.: Imperfect match: PDDL 2.1 and real applications. Journal Of Artificial Intelligence Research 20, 133–137 (2003)zbMATHGoogle Scholar
  8. 8.
    W3C: Web Services Description Language (WSDL) 1.1 (2001)Google Scholar
  9. 9.
    Hoffmann, J., Nebel, B.: The FF planning system: Fast plan generation through heuristic search. Journal of Artificial Intelligence Research 14, 253–302 (2001)zbMATHGoogle Scholar
  10. 10.
    Chapman, D.: Planning for conjunctive goals. Artificial Intelligence 32, 333–377 (1987)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Erol, K., Nau, D.S., Subrahamnian, V.: Complexity, decidability and undecidability results for domain-independent planning. Artificial Intelligence 76, 75–88 (1995)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Blum, A., Furst, M.: Fast planning through planning graph analysis. Artificial Intelligence 90, 281–300 (1997)CrossRefzbMATHGoogle Scholar
  13. 13.
    Gerevini, A., Saetti, A., Serina, I.: Planning through stochastic local search and temporal action graphs. Journal of Artificial Intelligence Research 20, 239–290 (2003)zbMATHGoogle Scholar
  14. 14.
    Do, M., Kambhampati, S.: Sapa: A multi-objective metric temporal planner. Journal Of Artificial Intelligence Research 20, 155–194 (2003)zbMATHGoogle Scholar
  15. 15.
    Brafman, R., Hoffmann, J.: Conformant planning via heuristic forward search: A new approach. In: Koenig, S., Shlomo Zilbe Koenig, S.Z. (eds.) Proceedings of the 14th International Conference on Automated Planning and Scheduling (ICAPS 2004), pp. 355–364. Morgan-Kaufmann, San Francisco (2004)Google Scholar
  16. 16.
    Hoffmann, J., Brafman, R.: Contingent planning via heuristic forward search with implicit belief states. In: Proceedings of the 15th International Conference on Automated Planning and Scheduling (ICAPS 2005). Morgan Kaufmann, San Francisco (2005)Google Scholar
  17. 17.
    McDermott, D.: A heuristic estimator for means-ends analysis in planning. In: Proceedings of the International Conference on Artificial Intelligence Planning Systems, pp. 142–149 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Harald Meyer
    • 1
  • Mathias Weske
    • 1
  1. 1.Hasso-Plattner-Institute for IT-Systems-Engineering at the University of PotsdamPotsdamGermany

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