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Integrating uncertainty handling formalisms in distributed artificial intelligence

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Book cover Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU 1993)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 747))

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Abstract

In distributed artificial intelligence systems it is important that the constituent intelligent systems communicate. This may be a problem if the systems use different methods to represent uncertain information. This paper presents a method that enables systems that use different uncertainty handling formalisms to qualitatively integrate their uncertain information, and argues that this makes it possible for distributed intelligent systems to achieve tasks that would otherwise be beyond them.

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References

  1. Agustí-Cullell, J., Esteva, F., García, P., Godó, L., Sierra, C. Combining Multiple-valued logics in modular expert systems, Proceedings of the 7th Conference on Uncertainty in Artificial Intelligence, (1991).

    Google Scholar 

  2. Avouris, N. M. and Gasser, L. (eds.) Distributed Artificial Intelligence; Theory and Praxis, Kluwer Academic Press, (1992).

    Google Scholar 

  3. De Finetti, B. Sul significato soggettivo della probabilità, Fundamenta Mathematica 17 (1931) 298–329.

    Google Scholar 

  4. Dubois, D. and Prade, H. Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press, New York, (1988).

    Google Scholar 

  5. Glowinski, A. O'Neil, M., Fox, J. Design of a generic information system and its application to Primary Care, Proceedings of the European Conference on Artificial Intelligence in Medicine, (1989).

    Google Scholar 

  6. Parsons, S. Qualitative methods for reasoning under uncertainty, Ph D. Thesis, Department of Electronic Engineering, Queen Mary and Westfield College, (1993)

    Google Scholar 

  7. Parsons, S. and Mamdani, E. H. On reasoning in networks with qualitative uncertainty, Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, (1993).

    Google Scholar 

  8. Saffiotti, A. An AI view of the treatment of uncertainty, The Knowledge Engineering Review, 2(2) (1987) 75–97.

    Google Scholar 

  9. Shafer, G. A mathematical theory of evidence, Princeton University Press, (1976).

    Google Scholar 

  10. Zadeh, L. A. Fuzzy sets as the basis for a theory of possibility, Fuzzy sets and systems, 1 (1978) 1–28.

    Google Scholar 

  11. Zhang, C. Cooperation under uncertainty in distributed expert systems, Artificial Intelligence, 56 (1992) 21–69.

    Google Scholar 

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Michael Clarke Rudolf Kruse Serafín Moral

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© 1993 Springer-Verlag Berlin Heidelberg

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Parsons, S., Saffiotti, A. (1993). Integrating uncertainty handling formalisms in distributed artificial intelligence. In: Clarke, M., Kruse, R., Moral, S. (eds) Symbolic and Quantitative Approaches to Reasoning and Uncertainty. ECSQARU 1993. Lecture Notes in Computer Science, vol 747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0028214

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  • DOI: https://doi.org/10.1007/BFb0028214

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57395-1

  • Online ISBN: 978-3-540-48130-0

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