Artificial Intelligence
A Monte-Carlo algorithm for Dempster-Shafer belief
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Artificial Intelligence - Special issue on knowledge representation
Fundamenta Informaticae - Special issue: logics for artificial intelligence
Markov Chain Monte-Carlo algorithms for the calculation of Dempster-Shafer belief
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Tractable disjunctions of linear constraints: basic results and applications to temporal reasoning
Theoretical Computer Science
Assumption-Based Modeling Using ABEL
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Probabilistic argumentation systems a new way to combine logic with probability
Journal of Applied Logic - Special issue on combining probability and logic
A logic of soft constraints based on partially ordered preferences
Journal of Heuristics
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Belief revision of GIS systems: the results of REV!GIS
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at least unhelpful). To overcome this difficulty, we express reasoning with linear constraints as a logic, and develop the formalisms based on this different underlying logic. We focus in particular on a possibilistic logic representation of uncertain linear constraints, a lattice-valued possibilistic logic, an assumption-based reasoning formalism and a Dempster-Shafer representation, proving some fundamental results for these extended systems. Our results on extending uncertainty formalisms also apply to a very general class of underlying monotonic logics.