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Simulating Cooperative Transportation Companies

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Simulation als betriebliche Entscheidungshilfe

Part of the book series: Fortschritte in der Simulationstechnik ((XFS,volume 8))

Abstract

In this paper we propose the use of methods from Distributed Artificial Intelligence for the simulation of complex economical and social Systems. Apart from [11], we stress the power that can be gained by applying mechanisms of coordination and cooperation among autonomous agents. As an example, the transportation domain is modeled as a multiagent scenario. Negotiation is used as a mechanism for achieving coordination among autonomous agents. We describe the different dimensions of cooperation and details of the simulation-system. Preliminary results are provided.

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References

  1. O. Boissier and Y. Demazeau. A distributed artificial intelligence view on general purpose vision systems. In Y. Demazeau and E. Werner, editors, Decentralized A.I.3. North-Holland, 1992.

    Google Scholar 

  2. A. Bond and L. Glaser. Readings in Distributed Artificial Intelligence. Morgan Kaufmann, Los Angeles, CA, 1988.

    Google Scholar 

  3. M. Buchheit, N. Kuhn, J.P. Müller, and M. Pischel. Mars: Modelling a multiagent scenario for shipping companies. In Proceedings of the European Simulation Symposium (ESS-92), Dresden, 1992. Society for Computer Simulation (SCS).

    Google Scholar 

  4. B. Chaib-Draa, B. Moulin, R. Mandiau, and P. Millot. Trends in distributed artificial intelligence. Artificial Intelligence Review, 1(6): 35–66, 1992.

    Article  Google Scholar 

  5. S. Cammarata, F. Hayes-Roth, R. Steeb, P. Thorndyke, and R. Wesson. Architectures for distributed air-traffic control. In A.H. Bond and L. Gasser, editors, Readings in Distributed Artificial Intelligence, pages 102–105. Morgan Kaufmann Publishers, Inc., San Mateo, CA, 1988.

    Google Scholar 

  6. Daniel D. Corkill and Victor R. Lesser. The distributed vehicle monitoring testbed: A tool for investigating distributed problem solving networks. In Robert Engelmore and Tony Morgan, editors, black-board Systems. Addison-Wesley Publihing Company, 1988.

    Google Scholar 

  7. R. Davis and R.G. Smith. Negotiation as a metaphor for distributed problem solving. In Artificial Intelligence, 20(1), pages 63–109, 1983.

    Article  Google Scholar 

  8. K. Fischer. Distributed Cooperative Planning in a Flexible Production Environment. PhD thesis, TU München, July 1992

    Google Scholar 

  9. K. Fischer and H.M. Windisch. Magsy-ein regelbasiertes Multiagen tensystem. In H.J. Müller, editor, KI1/92, Themenheft Verteilte KI. FBO-Verlag, 1992.

    Google Scholar 

  10. N.R. Jennings. Joint Intentions as a Model of Multi-Agent Cooperation. Ph.D. thesis, Queen Mary and Westfield College, London, August 1992.

    Google Scholar 

  11. N. Kuhn, H.J. Müller, and J.P. Müller. Task decomposition in dynamic agent societies. In Proceedings ofthe International Symposium on Autonomous Decentralized Systems (ISADS-93), Tokyo, Japan, 1993.

    Google Scholar 

  12. J.P. Latombe. How to move (physically speaking) in a multi-agent world. In Y. Demazeau and E. Werner, editors, Decentralized A.I. 3. North-Holland, 1992.

    Google Scholar 

  13. H. Müller-Merbach. Operations Research. Verlag Franz Vahlen, München, 3rd edition, 1973.

    Google Scholar 

  14. J.P. Müller. Towards a model for flexible agent interaction. Technical Report RR-93-01, German Research Center for Artificial Intelligence, Saarbrücken, 1993.

    Google Scholar 

  15. R. Rittmann. Die Macht der Trucks. Bild der Wissenschaft, 9:112–114, 1991.

    Google Scholar 

  16. K.P. Sycara. Multiagent compromise via negotiation. In L. Gasser and M.N. Huhns, editors, Distributed Artificial Intelligence, Volume II, pages 119–137. Morgan Kaufmann, San Mateo, California, 1989.

    Google Scholar 

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© 1994 Friedr. Vieweg & Sohn Verlagsgesellschaft mbH, Braunschweig/Wiesbaden

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Kuhn, N., Müller, J.P., Müller, J. (1994). Simulating Cooperative Transportation Companies. In: Biethahn, J., Hummeltenberg, W., Schmidt, B., Witte, T. (eds) Simulation als betriebliche Entscheidungshilfe. Fortschritte in der Simulationstechnik, vol 8. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-91115-5_18

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  • DOI: https://doi.org/10.1007/978-3-322-91115-5_18

  • Publisher Name: Vieweg+Teubner Verlag, Wiesbaden

  • Print ISBN: 978-3-528-06641-3

  • Online ISBN: 978-3-322-91115-5

  • eBook Packages: Springer Book Archive

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