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Qualitative combination of Bayesian networks. (English) Zbl 1028.68165
Summary: Directed graphic models based on conditional independence provide a compact and concise representation of an expert’s subjective belief about existing relationships between variables. Faced with the task of building a greater model, each expert must be a specialist in some subset of the whole knowledge domain. It would be desirable to aggregate the knowledge provided by those specialists under the form of graphical models into a single and more general representation. This article studies the consensus model that would be obtained by combining two graphs associated with Bayesian networks and applying the union and intersection of their independencies.

MSC:
68T35 Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence
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[1] Expert systems and probabilistic network models. New York, NY: Springer-Verlag; 1997.
[2] An introduction to Bayesian networks. London: UCL Press; 1996.
[3] Probabilistic reasoning in intelligent systems. San Francisco, CA: Morgan Kaufman; 1988.
[4] Experts systems in uncertainty: Opinion and subjective probability in science. New York: Oxford University Press; 1991.
[5] Genest, Stat Sci 1 pp 114– (1986)
[6] The topological fusion of Bayes nets. In: Proc 8th Annual Conf on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann; 1992. pp 191-198.
[7] Graphical representations of consensus belief. In: Proc 15th Conf on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann; 1999. pp 531-540.
[8] Aggregating probabilistic beliefs: Market mechanisms and graphical representations. PhD thesis, University of Michigan, 1999.
[9] Some complexity considerations in the combination of belief networks. In: Proc 9th Conf on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann; 1993. pp 159-165.
[10] GRAPHOIDS: A graph-based logic for reasoning about relevance relations; Technical Report 850038 (R-53-L), Cognitive Systems Laboratory, University of California, Los Angeles, 1985.
[11] Causal networks: Semantics and expressiveness. In: editors. Uncertainty in Artificial Intelligence, 4. Amsterdam: Elsevier Science Publishers B.V. (North-Holland); 1990. pp 69-76.
[12] Fusión topológica y cuantitativa de redes causales. PhD thesis, University of Granada, 2000. (In Spanish)
[13] Combining multiple directed graphical representations into a single probabilistic model. In: editor. Actas de la Séptima Conferencia Española para la Inteligencia Artificial, CAEPIA 97. Málaga: Imagraf; 1997. pp 645-652.
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