Advertisement

The Establishment of Partnerships to Create Virtual Organizations: A Multiagent Approach

  • M. A. Hochuli Shmeil
  • E. Oliveira
Part of the IFIP — The International Federation for Information Processing book series (IFIPAICT)

Abstract

Virtual Organizations are aggregations of independent organizations or individuals aiming to contribute to a common goal. Two basic steps are needed to assemble a virtual organization: (i) a business process partitioning, and (ii) a partners’ selection process. Broadly speaking, in this paper we present a computational approach to model organizations based on Distributed Artificial Intelligence — Multiagent Systems (DAI-MAS) as well as Symbolic Learning (SL) paradigms. Each organization, which is seen as an agent, is provided with the needed observation, planning, coordination, execution, communication and learning capabilities to perform its social roles. In particular, we present a specific inter-organization relationship: the selection process that leads to the automatic establishment of contracts between organizations. This selection process is composed of a bid evaluation phase followed by a negotiation phase as a mean for agents conflicts resolution. Through negotiation interactions, a set of offer and counter-offer values which are seen as instances (positives and negatives) are supplied for further analysis in order to support the learning activities. The contribution of our work lies, not only, on the computational model proposed for the society of organizations, but also in some extent, on the learning methodologies applied to the established partnerships, in particular, and to the community, in general.

Keywords

Multiagent system Organization Modeling Application. 

References

  1. [1]
    Engelmore, R., Morgan, T. (1988) Blackboard Systems. Addison - Wesley Publishing Company.Google Scholar
  2. [2]
    Simon, H.A., Decision Making and Organizational Design. In: Pugh, D.S.ed. Organizational Theory. Penguin Books. p. 189–212.Google Scholar
  3. [3]
    Oliveira, et.al. (1993) Negotiation and Conflict Resolution within a Community of Cooperative Agents. In: Proceedings of The First International Symposium on Autonomous Decentralized Systems, Kawasaki, Japan.Google Scholar
  4. [4]
    Gasser, L. Huhns, M.N. (1989) Distributed Artificial Intelligence, vol.Il, Pitman Publishing, London.Google Scholar
  5. [5]
    Michalski, R.S. (1990) Learning Flexible Concepts: Fundamental Ideas and a Method Based on Two-Tired Representation. In: Machine Learning - An Artificial Intelligence Approach, vol. III, Edited by Yves Kodratoff and Ryszard Michalsky, Morgan Kaufmann Publishers, Inc.Google Scholar
  6. [6]
    Kodratoff, Y. (1990) Learning Expert Knowledge by Improving the Explanations Provided by the System. In: Machine Learning - An Artificial Intelligence Approach, vol. III, Edited by Yves Kodratoff and Ryszard Michalsky, Morgan Kaufmann Publishers, Inc.Google Scholar
  7. [7]
    Wellman, M.P. (1993) A Market-Oriented Programming Environment and its Application to Distributed Multicommodity Flow Problems. In: Journal of Artificial Intelligence Research, 1 (1993) 1–23, AI Access Foundation and Morgan Kaufmann Publishers.Google Scholar
  8. [8]
    Barbuceanu, M., Fox, M.S. (1994) The Information Agent: An Infrastructure for Collaboration in the Integrated Enterprise. In: Proceedings of the 2nd International Working Conference on Cooperating Knowledge Based Systems, Editor S.M.Deen, University of Keele.Google Scholar
  9. [9]
    Oliveira, E., Mouta, F. (1993) Distributed AI Architecture Enabling Multi-Agent Cooperation. In: Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Edited by Paul W.H. Chung, Gillian Lovegrove and Moonis Ali, Gordon and Breach Science Publishers.Google Scholar
  10. [10]
    Sycara, K.P. (1989) Multiagent Compromise via Negotiation. In: Distributed Artificial Intelligence, vol. 11, Edited by Les Gasser and Michael N. Huhns, Pitman Publishing, London.Google Scholar
  11. [11]
    Sian, S.S. (1991) Adaptation Based on Cooperative Learning in Multi-Agent Systems. In: Decentralize A.I. - 2, Edited by Yves Demazeau and Jean-Pierre Muller, Elsevier Science Publishers B.V.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • M. A. Hochuli Shmeil
    • 1
  • E. Oliveira
    • 1
  1. 1.Faculdade de Engenharia da Universidade do PortoPorto CodexPortugal

Personalised recommendations