A complete revision function in propositional calculus
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
On the semantics of theory change: arbitration between old and new information
PODS '93 Proceedings of the twelfth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Integration of weighted knowledge bases
Artificial Intelligence
Artificial Intelligence
Possibilistic Merging and Distance-Based Fusion of Propositional Information
Annals of Mathematics and Artificial Intelligence
Smodels - An Implementation of the Stable Model and Well-Founded Semantics for Normal LP
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
XSB: A System for Effciently Computing WFS
LPNMR '97 Proceedings of the 4th International Conference on Logic Programming and Nonmonotonic Reasoning
Combining Knowledge Bases Consisting of First Order Theories
ISMIS '91 Proceedings of the 6th International Symposium on Methodologies for Intelligent Systems
Syntactic Representations of Semantic Merging Operations
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Merging Belief Bases Represented by Logic Programs
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Belief merging using normal forms
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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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.