An Open Platform for Customized Corpus Browsing Using Agents and Artifacts Approach

  • Zina EL GuedriaEmail author
  • Laurent Vercouter
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9662)


Document research in a digital corpus can be considered as a browsing process driven by some information needs. Such browses require the use of traditional information retrieval tools to select relevant documents based on a query. But they can be improved by the use of customization and adaptation mechanisms in order to refine the representation of information needs. Several factors are useful to influence this customization: user profiles, browsing profiles, semantic proximity of documents, recommendations from other similar users, ... Existing multiagent approaches for document research have proposed an agent model of resources and processes of a given system. We propose in this article a general model for agent-based document research decoupling browsing management and customization or recommendantion decisions. We follow a stigmergic approach in which agents implement different customization factors and modify their shared environment to influence the browsing results. The openness of this architecture is shown by presenting several variants that can be obtained by the dynamic addition of agents or resource artifacts.


Customized document research Agent and Artifact Stigmergic approach Information need 



The work carried out in this article receives funding from the Grand Réseau de Recherche: Logistique, Mobilité, Numérique High-Normandy Region (PlaIR 2.0 project 2013-2016).


  1. 1.
    Nodine, M., Fowler, J., Ksiezyk, T., Perry, B., Taylor, M., Unruh, A.: Active information gathering in infosleuthTM. Int. J. Coop. Inf. Syst. 9(01n02), 3–27 (2000)CrossRefGoogle Scholar
  2. 2.
    Durfee, E.H., Kiskis, D.L., Birmingham, W.P.: The agent architecture of the University of Michigan digital library. IEE Proc. Softw. 144(1), 61–71 (1997)CrossRefGoogle Scholar
  3. 3.
    Elayeb, B., Evrard, F., Zaghdoud, M., Ahmed, M.B.: A multiagent possibilistic system for web information retrieval. In: IKE, pp. 72–78 (2007)Google Scholar
  4. 4.
    The Institute of International Transport Law (IDIT).
  5. 5.
    Sycara, K., Decker, K., Pannu, A., Williamson, M., Zeng, D.: Distributed intelligent agents. IEEE Expert 11, 36–46 (1996)CrossRefGoogle Scholar
  6. 6.
    Grey D.J., Dunne G., Ferguson, R.I.: A mobile agent architecture for searching the WWW. In: Proceedings of Workshop on Agents in Industry, 4th International Conference of Autonomous Agents, Barcelona (2000)Google Scholar
  7. 7.
    Lemouzy, S.: Systèmes interactifs auto-adaptatifs par systèmes multiagents auto-organisateurs,: application à la personnalisation de l’accès à l’information. Ph.D thesis in computer science, Paul Sabatier University - Toulouse III, Toulouse (France) (2011)Google Scholar
  8. 8.
    Omicini, A., Ricci, A., Viroli, M.: Artifacts in the A & A meta-model for multiagent systems. Auton. Agents Multiagent Syst. 17(3), 432–456 (2008)CrossRefGoogle Scholar
  9. 9.
    Ricci, A., Viroli, M., Omicini, A.: CArtAgO: an infrastructure for engineering computational environments in MAS. In: Weyns, D., Parunak, H.V.D., Michel, F. (eds.) Proceedings of E4MAS, Hakodate, Japan, pp. 102–119 (2006)Google Scholar
  10. 10.
    Grassé, P.-P.: La reconstruction du nid et les coordinations interindividuelles chez Bellicositermes natalensis et Cubitermes sp. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insectes sociaux 6(1), 41–80 (1959)MathSciNetCrossRefGoogle Scholar
  11. 11.
    El Guedria, Z., Vercouter, L.: Customized document research by a stigmergic approach using agents and artifacts. In: Rovatsos, M., Vouros, G., Julian, V. (eds.) EUMAS 2015. LNCS, vol. 9571, pp. 50–64. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-33509-4_4 CrossRefGoogle Scholar
  12. 12.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40(3), 56–58 (1997)CrossRefGoogle Scholar
  13. 13.
    Burke, R.: Survey and experiments. User Model. User-Adap. Inter. 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  14. 14.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  15. 15.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: An open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186 (1994)Google Scholar
  16. 16.
    Pelleg, D., Moore, A.W., et al.: X-means: extending K-means with efficient estimation of the number of clusters. In: ICML 2000, pp. 727–734 (2000)Google Scholar
  17. 17.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 210–217. ACM Press/Addison-Wesley Publishing Co, May 1995Google Scholar
  18. 18.
    Ricci, A., Omicini, A., Viroli, M., Gardelli, L., Oliva, E.: Cognitive stigmergy: towards a framework based on agents and artifacts. In: Weyns, D., Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 124–140. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.INSA Rouen, LITISNormandie UnivRouenFrance

Personalised recommendations