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A Request Language for Web-Services Based on Planning and Constraint Satisfaction

  • M. Aiello
  • M. Papazoglou
  • J. Yang
  • M. Carman
  • M. Pistore
  • L. Serafini
  • P. Traverso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2444)

Abstract

One of the most challenging problems that web-service enabled e-marketplaces face is the lack of support for appropriate service request languages that retrieve and aggregate services relevant to a business problem. We present an architectural framework for web-service interaction based on planning and constraint satisfaction, and a webservice request language (WSRL) developed on the basis of this framework. This framework is capable of performing planning under uncertainty on the basis of refinement and revision as new service-related information is accumulated (via interaction withth e user or UDDI) and as execution circumstances necessitate change.

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References

  1. 2.
    U. Dal Lago, M. Pistore, and P. Traverso. Planning witha language for extended goals. In 18 th National Conference on Artificial Intelligence (AAAI-02), 2002.Google Scholar
  2. 3.
    J. Yang and M. Papazoglou. Web component: A substrate for web service reuse and composition. In 14 th Int. Conf. on Advanced Information Systems Engineering CAiSE02, 2002.Google Scholar
  3. 4.
    S. Smith, D. Hildum, and D.R. Crimm. Toward the design of web-based planning and scheduling services. In Int. Workshop on Automated Planning and Scheduling Technologies in New Methods of Electronic, Mobile and Collaborative Work, 2001.Google Scholar
  4. 5.
    D. McDermott. Estimated-regression planning for interactions withWeb Services. In 6 th Int. Conf. on AI Planning and Scheduling. AAAI Press, 2002.Google Scholar
  5. 6.
    C. A. Knoblock, S. Minton, J. L. Ambite, N. Ashish, I. Muslea, A. G. Philpot, and S. Tejada. The ariadne approach to web-based information integration. International the Journal on Cooperative Information Systems, 2002. Special Issue on Intelligent Information Agents: Theory and Applications, Forthcoming.Google Scholar
  6. 7.
    C. A. Knoblock, K. Lerman, S. Minton, and I. Muslea. Accurately and reliably extracting data from the web: A machine learning approach. Data Engineering Bulletin, 2002. To appear.Google Scholar
  7. 8.
    E. A. Emerson. Temporal and modal logic. In J. van Leeuwen, editor, Handbook of Theoretical Computer Science, Volume B. Elsevier, 1990.Google Scholar
  8. 9.
    P. Van Hentenryck and V.J. Saraswat, editors. Principles and Practice of Constraint Programming. MIT Press, 1995.Google Scholar
  9. 10.
    M. Ghallab, A. Howe, C. Knoblock, D. McDermott, A. Ram, M. Veloso, D. Weld, and D. Wilkins. PDDL—The Planning Domain Definition Language. In R. Simmons, M. Veloso, and S. Smith, editors, Int. Conf. AIPS98, 1998.Google Scholar
  10. 11.
    P. Bertoli, A. Cimatti, M. Pistore, M. Roveri, and P. Traverso. MBP: A Model Based Planner. In n Proc. IJCAI’01 Workshop on Planning under Uncertainty and Incomplete Information, 2001.Google Scholar
  11. 12.
    ECLIPSE. Eclipse Constraint Logic Programming System, 2002. http://www-icparc.doc.ic.ac.uk/eclipse.
  12. 13.
    M. Iwaihara. Supporting dynamic constraints for commerce negotiations. In WECWIS 2000. IEEE, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • M. Aiello
    • 1
  • M. Papazoglou
    • 1
    • 2
  • J. Yang
    • 2
  • M. Carman
    • 3
  • M. Pistore
    • 3
  • L. Serafini
    • 3
  • P. Traverso
    • 3
  1. 1.DITTrentoItaly
  2. 2.INFOLABUniversity of TilburgLE Tilburg
  3. 3.ITC-IRSTTrentoItaly

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