Abstract
The DRUMS project is addressing a variety of symbolic and numerical techniques for reasoning under uncertainty and with incomplete information. This paper discusses work which is directed towards identifying a unifying framework which will enable a variety of uncertainty handling techniques to be integrated in a single programming environment.
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References
Dawes R M, 1979, “The robust beauty of improper linear models in decision making”, American Psychologist 34 pp 571–582.
Donnai M, Krause P and Parsons S, 1991, Using symbolic distances among heterogeneous hypotheses as a relative measure of their uncertainty Technical Report, Advanced Computation Laboratory, Imperial Cancer Research Fund.
Fox J, 1986, “Three Arguments for Extending the Framework of Probability” in Kanal L N and Lenner J F (eds), Uncertainty and Artificial Intelligence, Elsevier Science Publishers.
Fox J and Krause P J, 1990, “Combining symbolic and numerical methods for defeasible reasoning” Proceedings of Colloquium on Uncertainty Management, Institute of Electrical Engineers, London.
Fox J and Krause P J, 1991, “Symbolic Decision Theory and Autonomous Systems” in D'Ambrosio B D, Smets P and Bonissone P P (eds), Uncertainty in Artificial Intelligence: Proceedings of the Seventh Conference, San Mateo, Calif.: Morgan Kaufmann.
Hajnal S, Fox J and Krause P, 1990, Sceptic User Manual Technical Report, Advanced Computation Laboratory, Imperial Cancer Research Fund.
Krause P J and Fox J, 1991 “Combining symbolic and numerical methods for reasoning under uncertainty” in AI and Computer Power: The Impact on Statistics, Unicom Seminars Ltd.
Lauritzen S L and Spiegelhalter D, 1988, “Local computations with probabilities on graphical structures and their application to expert systems” J Roy. Statist. Soc. B 50 (2) pp 157–224.
Parsons S and Fox J, 1991, “Qualitative and interval algebras for robust decision making under uncertainty” in Proceedings of IMACS Workshop on Qualitative Reasoning and Decision Support Systems, Toulouse, France.
Pearl J, 1988, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, San Mateo, Calif.: Morgan Kaufmann.
Shafer G, Shenoy P and Mellouli, 1987, “Propagating Belief Functions in Qualitative Markov Trees”, International Journal of Approximate Reasoning 1 pp 349–400.
Wellman M P, 1990, “Fundamental Concepts of Qualitative Probabilistic Networks”, Artificial Intelligence 44 pp 257–303.
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© 1991 Springer-Verlag Berlin Heidelberg
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Fox, J., Krause, P., Dohnal, M. (1991). An extended logic language for representing belief. In: Kruse, R., Siegel, P. (eds) Symbolic and Quantitative Approaches to Uncertainty. ECSQARU 1991. Lecture Notes in Computer Science, vol 548. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-54659-6_67
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DOI: https://doi.org/10.1007/3-540-54659-6_67
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