Minimization methods for non-differentiable functions
Minimization methods for non-differentiable functions
Convex Optimization
Artificial Intelligence - Special issue on nonmonotonic reasoning
Merging Qualitative Constraints Networks Using Propositional Logic
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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This paper is centered on the problem of merging (possibly conflicting) information coming from different sources. Though this problem has attracted much attention in propositional settings, propositional languages remain typically not expressive enough for a number of applications, especially when spatial information must be dealt with. In order to fill the gap, we consider a (limited) first-order logical setting, expressive enough for representing and reasoning about information modeled as half-spaces from metric affine spaces. In this setting, we define a family of distance-based majority merging operators which includes the propositional majority operator ΔdH,Σ. We identify a subclass of interpretations of our representation language for which the result of the merging process can be computed and expressed as a formula.