Belief structures, possibility theory and decomposable confidence measures on finite sets
Computers and Artificial Intelligence
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Coherent qualitative probability
Journal of Mathematical Psychology
Management Science
A first course in fuzzy logic
Measures of uncertainty in expert systems
Artificial Intelligence
The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
Modeling agents as qualitative decision makers
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Confidence relations and ordinal information
Information Sciences: an International Journal
Proceedings of the 1999 international conference on Logic programming
Complexity and expressive power of logic programming
ACM Computing Surveys (CSUR)
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Qualitative decision theory: from savage's axioms to nonmonotonic reasoning
Journal of the ACM (JACM)
Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Defining relative likelihood in partially-ordered preferential structures
Journal of Artificial Intelligence Research
Generalized qualitative probability: savage revisited
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A 25-year perspective on logic programming
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In recent decades, qualitative approaches to probabilistic uncertainty have been receiving wider and wider attention. We propose a new characterization of some of the most adopted partial preference orders by providing an uniform axiomatic treatment of a variety of qualitative uncertainty notions. We prove a representation result that connects qualitative notions of partial uncertainty to their numerical counterparts. We also describe an executable specification, in the declarative framework of Answer Set Programming, that constitutes the core engine for the qualitative management of uncertainty. Some basic reasoning tasks are also identified.