Role-Based Management and Matchmaking in Data-Mining Multi-Agent Systems

  • Ondřej Kazík
  • Roman Neruda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7607)


We present an application of concepts of agent, role and group to the hybrid intelligence data-mining tasks. The computational MAS model is formalized in axioms of description logic. Two key functionalities — matchmaking and correctness verification in the MAS — are provided by the role model together with reasoning techniques which are embodied in specific ontology agent. Apart from a simple computational MAS scenario, other configurations such as pre-processing, meta-learning, or ensemble methods are dealt with.


MAS role-based models data-mining computational intelligence description logic matchmaking closed-world assumption 


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  1. 1.
    Albashiri, K.A., Coenen, F.: Agent-Enriched Data Mining Using an Extendable Framework. In: Cao, L., Gorodetsky, V., Liu, J., Weiss, G., Yu, P.S. (eds.) ADMI 2009. LNCS, vol. 5680, pp. 53–68. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Baader, F., et al.: The description logic handbook: Theory, implementation, and applications. Cambridge University Press (2003)Google Scholar
  3. 3.
    Bellifemine, F., Caire, G., Greenwood, D.: Developing multi-agent systems with JADE. John Wiley and Sons (2007)Google Scholar
  4. 4.
    Berthold, M.R., et al.: KNIME: The konstanz information miner. In: Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization, pp. 319–326. Springer (2008)Google Scholar
  5. 5.
    Bonissone, P.: Soft computing: the convergence of emerging reasoning technologies. Soft Computing - A Fusion of Foundations, Methodologies and Applications, pp. 6–18 (1997)Google Scholar
  6. 6.
    Cabri, G., Ferrari, L., Leonardi, L.: Agent role-based collaboration and coordination: a survey about existing approaches. In: Proc. of the Man and Cybernetics Conf. (2004)Google Scholar
  7. 7.
    Cabri, G., Ferrari, L., Leonardi, L.: Supporting the Development of Multi-agent Interactions Via Roles. In: Müller, J.P., Zambonelli, F. (eds.) AOSE 2005. LNCS, vol. 3950, pp. 154–166. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Cao, L.: Data Mining and Multi-agent Integration. Springer (2009)Google Scholar
  9. 9.
    Cao, L., Gorodetsky, V., Mitkas, P.A.: Agent mining: The synergy of agents and data mining. IEEE Intelligent Systems 24, 64–72 (2009)Google Scholar
  10. 10.
    Ferber, J., Gutknecht, O., Michel, F.: From Agents to Organizations: An Organizational View of Multi-agent Systems. In: Giorgini, P., Müller, J.P., Odell, J.J. (eds.) AOSE 2003. LNCS, vol. 2935, pp. 214–230. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Gibert, K., et al.: On the role of pre and post-processing in environmental data mining. In: International Congress on Environmental Modelling and Software – 4th Biennial Meeting, pp. 1937–1958 (2008)Google Scholar
  12. 12.
    Gilat, A.: MATLAB: An Introduction with Applications, 2nd edn. John Wiley and Sons (2004)Google Scholar
  13. 13.
    Kazík, O., Pešková, K., Pilát, M., Neruda, R.: Implementation of parameter space search for meta learning in a data-mining multi-agent system. In: ICMLA, vol. 2, pp. 366–369. IEEE Computer Society (2011)Google Scholar
  14. 14.
    Martin, D., Paolucci, M., McIlraith, S.A., Burstein, M., McDermott, D., McGuinness, D.L., Parsia, B., Payne, T.R., Sabou, M., Solanki, M., Srinivasan, N., Sycara, K.: 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
  15. 15.
    Neruda, R.: Emerging Hybrid Computational Models. In: Huang, D.-S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS (LNAI), vol. 4114, pp. 379–389. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Neruda, R., Beuster, G.: Toward dynamic generation of computational agents by means of logical descriptions. International Transactions on Systems Science and Applications, 139–144 (2008)Google Scholar
  17. 17.
    Neruda, R., Kazík, O.: Role-based design of computational intelligence multi-agent system. In: MEDES 2010, pp. 95–101 (2010)Google Scholar
  18. 18.
    Prud’hommeaux, E., Seaborne, A.: SPARQL query language for RDF. Tech. rep., W3C (2006)Google Scholar
  19. 19.
    Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical OWL-DL reasoner. Web Semantics: Science, Services and Agents on the World Wide Web 5(2), 51–53 (2007)CrossRefGoogle Scholar
  20. 20.
    Sirin, E., Tao, J.: Towards integrity constraints in OWL. In: OWLED. CEUR Workshop Proceedings, vol. 529 (2009)Google Scholar
  21. 21.
    Soares, C., Brazdil, P.B.: Zoomed Ranking: Selection of Classification Algorithms Based on Relevant Performance Information. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 126–135. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  22. 22.
    Teetor, P.: R Cookbook. O’Reilly (2011)Google Scholar
  23. 23.
    Weiss, G. (ed.): Multiagent Systems. MIT Press (1999)Google Scholar
  24. 24.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for search. Tech. rep., Santa Fe Institute (1995)Google Scholar
  25. 25.
    Wooldridge, M., Jennings, N.R., Kinny, D.: The Gaia methodology for agent-oriented analysis and design. Journal of Autonomous Agents and Multi-Agent Systems 3(3), 285–312 (2000)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ondřej Kazík
    • 1
  • Roman Neruda
    • 2
  1. 1.Faculty of Mathematics and PhysicsCharles UniversityPragueCzech Republic
  2. 2.Institute of Computer ScienceAcademy of Sciences of the Czech RepublicPragueCzech Republic

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