Multiple task selection protocol in a distributed problem solving network

  • Tibor Gyires
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 364)


In a Distributed Solving Network each node stores only a limited extent of knowledge in its knowledge base. Therefore complex problems are decomposed into subproblems which can already be solved by the nodes. The aim of a problem solving network is to assign the subproblems to nodes which attempt to solve those cooperatively. The notion of Communication Functions is introduced to specify the behavior of cooperative nodes in distributed problem-solving network. These functions are used to specify the Multiple Task Selection Protocol which dynamically selects the most competent group of nodes for solving a problem and improves the problem solving capability of the network by making use of previous experience.


intelligent data bases distributed protocols distributed artificial intelligence distributed expert systems negotiation 


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  1. [1]
    Distributed Systems: Methods and Tools for Specification. An Advanced Course. Ed. M. Paul-H.J. Siegert. Berlin-New York, Springer, 1985. 573 p.Google Scholar
  2. [2]
    Brett D. Fleisch: Meta-Activities: Towards Coherent Distributed Jobs, in: Proceedings of the 4th International Conference on Distributed Computing Systems. IEEE. San Francisco, Calif., 1984. 566–577 p.Google Scholar
  3. [3]
    Randall Davis, Reid G. Smith: Negotiation as a Metaphor for Distributed Problem Solving, Artificial Intelligence 20, 1983. 63–109 p.Google Scholar
  4. [4]
    Leslie Lamport, Robert Shostak, and Marshall Pease: The Byzantine Generals Problem, ACM Transactions on Programming Languages and Systems, Vol. 4, No. 3, 1982. 382–401 p.CrossRefGoogle Scholar
  5. [5]
    Tibor Gyires: Architecture of Distributed Operating System, in: Proceedings of the 24th Southeast Conference of the ACM, Tampa, Florida, 1986. 129–133 p.Google Scholar
  6. [6]
    Tibor Gyires: Distributed Problem solving Network with uncertainty in: Proceedings of the IFIP WG6.5 International Working Conference on Message Handling Systems and Distributed Applications, Costa Mesa, California, Oct. 10–12. 1988. Amsterdam, North-Holland, Elsevier, in print.Google Scholar
  7. [7]
    Victor R. Lesser, Daniel D. Corkill: Functionally Accurate, Cooperative Distributed Systems, IEEE Transactions on Systems, Man, and Cybernetics, Vol.SMC-11,No. 1, 1981. 81–95 p.Google Scholar
  8. [8]
    Joshep Y. Halpern: Knowledge and Common Knowledge in a Distributed Environment, in: Proceedings of the Third Annual ACM Symposium on Principles of Distributed Computing, Vancouver, B.C., Canada, August 27–29, 1984. 50–61 p.Google Scholar
  9. [9]
    J.S. Rosenchein, M.R. Genesereth, Deals among rational agents, in: Proceedings of 9th Int. Joint Conf. Artificial Intelligence, August, 1985. 91–99 p.Google Scholar
  10. [10]
    Keith S. Decker, Distributed Problem-Solving Techniques: A survey, IEEE Transactions On Systems, Man, and Cybernetics, Vol.SMC-17, No.5, 1987. 729–740 p.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1989

Authors and Affiliations

  • Tibor Gyires
    • 1
  1. 1.Department of Computer ScienceUniversity of North Carolina at CharlotteCharlotte

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