Best First Search Planning of Service Composition Using Incrementally Refined Context-Dependent Heuristics

  • Johannes Fähndrich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8076)


In oder to decide if a agent capability is helpful to achieve a goal, modern search algorithms in AI research use heuristics to narrow the search space by indicating which capability is the best to use. Considering the lack of information about pragmatic meaning, creating sound heuristics automatically out of capability descriptions asks too much of modern reasoning algorithms. Most approaches use semantics in oder to enable the reasoner to improve Word-sense disambiguation in their ontology matching tasks. As semantics are meant to be shared, the information is context independent and quite general. I postulate that context-dependent meaning can play an important role in describing the meaning of concepts used, as some meaning might change with the changes in context. The proposed thesis creates context-dependent heuristics by combining expert knowledge with machine learning. The PhD has the goal of structuring descriptions with a concept introduced in linguistics, introducing a description of domain knowledge and contextual information and thereby enable the automatic creation of context-dependent heuristics. Choosing from the many improvement points of agent planning, this work focuses on the improvement of capability descriptions.


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  1. 1.
    Bencomo, N., Cavallaro, L., Sawyer, P., Sykes, D., Issarny, V.: Satisfying requirements for pervasive service compositions. ACM, Innsbruck (2012)Google Scholar
  2. 2.
    Bouquet, P., Giunchiglia, F.: C-owl: Contextualizing ontologies (2003)Google Scholar
  3. 3.
    Chen, S., Wu, H., Han, X., Xiao, L.: Multi-step truncated q-learning algorithm. Machine Learning, 18–21 (2005)Google Scholar
  4. 4.
    Fähndrich, J., Ahrndt, S.: Towards self-explaining agents. In: PAAMS 2013 (2013)Google Scholar
  5. 5.
    Fern, A., Khardon, R., Tadepalli, P.: The first learning track of the international planning competition. Machine Learning 84(1-2), 81–107 (2011)CrossRefGoogle Scholar
  6. 6.
    Ghidini, C., Giunchiglia, F.: Local models semantics, or contextual reasoning = locality + compatibility. Artificial Intelligence, 1–41 (2001)Google Scholar
  7. 7.
    Giunchiglia, Maltese, Dutta: Domains and contest: Fiest stepes towards managing diversitz in knowledge. Journal fo Web Semantics 12-13, 53–63 (2012)CrossRefGoogle Scholar
  8. 8.
    Goddard, C. (ed.): Cross-linguistic semantics. John Benjamins Pub. Co. (2008)Google Scholar
  9. 9.
    Kaddoum, G., George, P.: Characterizing and evaluating problem solving self-* systems. In: Future Computing, pp. 137–145 (2009)Google Scholar
  10. 10.
    Klusch, M., Küster, U., Leger, A., Martin, D., Paolucci, M.: S3 (2012), (last visited: June 05, 2013)
  11. 11.
    Löbner, S.: Semantik. Eine Einführung (2003)Google Scholar
  12. 12.
    Martin, D., et al.: Bringing semantics to web services: The OWL-S approach. In: Cardoso, J., Sheth, A.P. (eds.) SWSWPC 2004. LNCS, vol. 3387, pp. 26–42. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Oaks, P., ter Hofstede, A.H.M., Edmond, D.: Capabilities: Describing what services can do. In: Orlowska, M.E., Weerawarana, S., Papazoglou, M.P., Yang, J. (eds.) ICSOC 2003. LNCS, vol. 2910, pp. 1–16. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    Polleres, A., Lausen, H., Lara, R.: Semantische beschreibung von web services. In: Pellegrini, T., Blumauer, A. (eds.) Semantic Web, pp. 505–524. Springer (2006)Google Scholar
  15. 15.
    Tamani, E., Evripidou, P.: Combining pragmatics and intelligence in semantic web service discovery. In: Meersman, R., Tari, Z. (eds.) OTM-WS 2007, Part II. LNCS, vol. 4806, pp. 824–833. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  16. 16.
    Yoon, S.W., Fern, A., Givan, R.: Ff-replan: A baseline for probabilistic planning. In: ICAPS, p. 352. AAAI (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Johannes Fähndrich
    • 1
  1. 1.DAI-LaborTechnische Universität BerlinGermany

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