Using Intelligent Agents in e-Government for Supporting Decision Making About Service Proposals

  • Pasquale De Meo
  • Giovanni Quattrone
  • Domenico Ursino
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


This paper aims at studying the possibility of exploiting the Intelligent Agent technology in e-government for supporting the decision making activity of government agencies. Specifically, it proposes a system to assist managers of a government agency, who plan to propose a new service, to identify those citizens that could gain the highest benefit from it. The paper illustrates the proposed system and reports some experimental results.


Government Agency User Profile Knapsack Problem Intelligent Agent User Agent 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Accenture Consulting. e-Government Leadership Rhetoric vs. Reality—Closing the Gap. Technical report (2001), Available at
  2. 2.
    Fay, A.: A fuzzy knowledge-based system for railway traffic control. Engineering Applications of Artificial Intelligence 13(6), 719–729 (2000)CrossRefMathSciNetGoogle Scholar
  3. 3.
    Goto, K., Kambayashi, Y.: A new passenger support system for public transport using mobile database access. In: Bressan, S., Chaudhri, A.B., Li Lee, M., Yu, J.X., Lacroix, Z. (eds.) CAiSE 2002 and VLDB 2002. LNCS, vol. 2590, pp. 908–919. Springer, Heidelberg (2003)Google Scholar
  4. 4.
    Gouscos, D., Mentzas, G., Georgiadis, P.: PASSPORT, a novel architectural model for the provision of seamless cross-border e-government services. In: Proc. of the International Workshop on Database and Expert Systems Applications (DEXA 2001), Munich, Germany, pp. 318–322. IEEE Computer Society, Los Alamitos (2001)CrossRefGoogle Scholar
  5. 5.
    Kellerer, H., Pferschy, U., Pisinger, D.: Knapsack Problems. Springer, Heidelberg (2004)MATHGoogle Scholar
  6. 6.
    Kobsa, A.: Generic user modeling systems. User Modeling and User-Adapted Interaction 11, 49–63 (2001)MATHCrossRefGoogle Scholar
  7. 7.
    Koutrika, G., Ioannidis, Y.: Personalized queries under a generalized preference model. In: Proc. of the IEEE International Conference on Data Engineering (ICDE 2005), Tokyo, Japan, pp. 841–852. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  8. 8.
    Lam, W., Mukhopadhyay, S., Mostafa, J., Palakal, M.: Detection of shifts in user interests for personalized information filtering. In: Proc. of ACM International Conference on Research and Development in Information Retrieval (SIGIR 1996), Zurich, Switzerland, pp. 317–325. ACM Press, New York (1996)Google Scholar
  9. 9.
    Marchionini, G., Samet, H., Brandt, L.: Introduction to the special issue on Digital Government. Communications of the ACM 46(1), 24–27 (2003)CrossRefGoogle Scholar
  10. 10.
    Sharrard, J., McCarthy, J.C., Tavilla, M.J., Stanley, J.: Sizing US government. Technical report, Forrester Research, Inc., Cambridge, Massachussets, USA (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Pasquale De Meo
    • 1
  • Giovanni Quattrone
    • 1
  • Domenico Ursino
    • 1
  1. 1.DIMETUniversità Mediterranea di Reggio CalabriaReggio CalabriaItaly

Personalised recommendations