The job assignment problem: A study in parallel and distributed machine learning

  • Gerhard Weiß
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1337)


This article describes a parallel and distributed machine learning approach to a basic variant of the job assignment problem. The approach is in the line of the multiagent learning paradigm as investigated in distributed artificial intelligence. The job assignment problem requires to solve the task of assigning a given set of jobs to a given set of executing nodes in such a way that the overall execution time is reduced, where the individual jobs may depend on each other and the individual nodes may differ from each other in their execution abilities. Experimental results are presented that illustrate this approach.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chan, P.K., & Stolfo, S.J. (1995). A comparative evaluation of voting and meta-learning of partitioned data. In Proceedings of the Twelfth International Conference on Machine Learning (pp. 90–98).Google Scholar
  2. 2.
    Daley, R.P., Pitt, L., Velauthapillai, M., Will, T. (1991). Relations between probabilistic and team one-shot learners. In Proceedings of the Workshop on Computational Learning Theory (pp. 228–239).Google Scholar
  3. 3.
    Davies, W., & Edwards, P. (1997). The communication of inductive inferences. In [10].Google Scholar
  4. 4.
    Garey, M.JR., & Johnson, D. (1979). Computers and intractability. New York: Freeman.Google Scholar
  5. 5.
    Jain, S., & Sharma, A. (1995). On aggregating teams of learning machines. Theoretical Computer Science A, 137(1), 85–108.MathSciNetCrossRefGoogle Scholar
  6. 6.
    Pitt, L., & Smith, C. (1988). Probability and plurality for aggregations of learning machines. Information and Computation, 77, 77–92.MathSciNetCrossRefGoogle Scholar
  7. 7.
    Provost, F.J., & Hennessy, D.N. (1995). Distributed machine learning: Scaling up with coarse grained parallelism. In Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology (pp. 340–348).Google Scholar
  8. 8.
    Sen, S. (Ed.) (1996). Adaptation, coevolution and learning in multiagent systems. Papers from the 1996 AAAI Symposium. Technical Report SS-96-01. AAAI Press.Google Scholar
  9. 9.
    Sikora, R., & Shaw, M.J. (1991). A distributed problem-solving approach to inductive learning. Faculty Working Paper 91-0109. Department of Business Administration, University of Illinois at Urbana-Champaign.Google Scholar
  10. 10.
    Weiß, G. (Ed.) (1997). Distributed artificial intelligence meets machine learning. Lecture Notes in Artificial Intelligence, Vol. 1221. Springer-Verlag.Google Scholar
  11. 11.
    Weiß, G., & Sen, S. (Eds.) (1996). Adaption and learning in multi-agent systems. Lecture Notes in Artificial Intelligence, Vol. 1042. Springer-Verlag.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Gerhard Weiß
    • 1
  1. 1.Institut für InformatikTechnische Universität MünchenMünchenGermany

Personalised recommendations