A Decentralized Calendar System Featuring Sharing, Trusting and Negotiating

  • Yves Demazeau
  • Dimitri Melaye
  • Marie-Hélène Verrons
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4031)


This article presents a decentralized calendar system benefiting from the use of computational trust. In our system, each user is represented by an agent in charge of the scheduling of events, either tasks or meetings. Each event is characterized by two attributes: importance and urgency. These notions are subjective: each agent has its own priorities and its own view of what is important and urgent. Timetables can be shared with other agents according to the groups of the agents, thus facilitating the scheduling of a meeting within a group. Nevertheless, timetables do not have to be shared. So we introduce trust to support this absence of information. These mechanisms use a generic trust model permitting the calculation of trust from several sources. We stress on the importance of the different sources for the emergence of trust groups. When trust is not given between all participants in a meeting, negotiation is used to find a possible date.


Trust Model Constraint Satisfaction Problem Negotiation System Trust Network Negotiation Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Castelfranchi, C., Falcone, R., Pezzulo, G.: Trust in information sources as a source for trust: a fuzzy approach. In: AAMAS 2000 and AAMAS 2002, pp. 89–96. ACM Press, New York (2003)Google Scholar
  2. 2.
    Covey, S.R.: The 7 Habits of Highly Effective People. Simon & Schuster (1989)Google Scholar
  3. 3.
    Guha, R.: Open rating systems. Technical report, Stanford University, East Lansing, Michigan (2003)Google Scholar
  4. 4.
    Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M.: Automated negotiation: prospects, methods and challenges. International Journal of Group Decision and Negotiation 10(2), 199–215 (2001)CrossRefGoogle Scholar
  5. 5.
    Jonker, C.M., Treur, J.: Formal analysis of models for the dynamics of trust based on experiences. In: Garijo, F.J., Boman, M. (eds.) MAAMAW 1999. LNCS, vol. 1647, pp. 221–231. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  6. 6.
    Marsh, S.: Optimism and pessimism in trust. In: Ramirez, J. (ed.) Proceedings IBERAMIA 1994/CNAISE 1994. McGraw-Hill, New York (1994)Google Scholar
  7. 7.
    Mathieu, P., Verrons, M.-H.: A General Negotiation Model using XML. Artificial Intelligence and Simulation of Behaviour Journal (AISBJ) 1(6), 523–542 (2005)Google Scholar
  8. 8.
    Melaye, D., Demazeau, Y.: Bayesian dynamic trust model. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds.) CEEMAS 2005. LNCS (LNAI), vol. 3690, pp. 480–489. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Ossowski, S.: Co-ordination in Artificial Agent Societies. LNCS (LNAI), vol. 1535. Springer, Heidelberg (1998)Google Scholar
  10. 10.
    Radcliffe-Brown, A.: Structure and function in primitive society. Cohen & West (1952)Google Scholar
  11. 11.
    Ramchurn, S.D., Huynh, D., Jennings, N.R.: Trust in multi-agent systems. The Knowledge Engineering Review 19(1) (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yves Demazeau
    • 1
  • Dimitri Melaye
    • 1
  • Marie-Hélène Verrons
    • 1
  1. 1.Équipe MAGMA, Laboratoire Leibniz-Imag – CNRS UMR 5522, INPGGRENOBLEFrance

Personalised recommendations