Abstract
When information comes from different sources inconsistent beliefs may appear. To handle inconsistency, several model-based belief merging operators have been proposed. Starting from different belief bases which might conflict, these operators return a unique consistent base which represents the beliefs of the group. The operators, parameterized by a distance between interpretations and aggregation function, usually only take into account consistent bases, consequently some information which is not responsible for conflicts may be ignored. An alternative way of merging uses the notion of Partial Satisfiability to define PS-Merge, a model-based merging operator that produces similar results to other merging approaches, but while other approaches require many merging operators in order to achieve satisfactory results for different scenarios PS-Merge obtains similar results for all these different scenarios with a unique operator. This paper analyzes some of the properties satisfied by PS-Merge.
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Pozos Parra, P., Borja Macías, V. (2007). Partial Satisfiability-Based Merging. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_22
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DOI: https://doi.org/10.1007/978-3-540-76631-5_22
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