Abstract
The problem of merging multiple sources information is central in several domains of computer science. In knowledge representation for artificial intelligence, several approaches have been proposed for propositional bases fusion, however, most of them are defined at a semantic level and are untractable. This paper proposes a new syntactic approach of belief bases fusion, called Removed Sets Fusion (RSF). The notion of removed-set, initially defined in the context of belief revision is extended to fusion and most of the classical fusion operations are syntactically captured by RSF. In order to efficiently implement RSF, the paper shows how RSF can be encoded into a logic program with answer set semantics, then presents an adaptation of the smodels system devoted to efficiently compute the removed sets in order to perform RSF. Finally a preliminary experimental study shows that the answer set programming approach seems promising for performing belief bases fusion on real scale applications.
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Hue, J., Papini, O., Wurbel, E. (2007). Syntactic Propositional Belief Bases Fusion with Removed Sets. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_9
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DOI: https://doi.org/10.1007/978-3-540-75256-1_9
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