Integration and Conditioning in Numerical Possibility Theory
Annals of Mathematics and Artificial Intelligence
Possibility Theory, Probability Theory and Multiple-Valued Logics: A Clarification
Annals of Mathematics and Artificial Intelligence
Artificial Intelligence
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Fuzzy Sets and Systems
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Possibility theory and statistical reasoning
Computational Statistics & Data Analysis
Practical representations of incomplete probabilistic knowledge
Computational Statistics & Data Analysis
In Memoriam: Philippe Smets (1938--2005)
Fuzzy Sets and Systems
Fuzzy methods in machine learning and data mining: Status and prospects
Fuzzy Sets and Systems
In memoriam: Philippe Smets (1938-2005)
International Journal of Approximate Reasoning
Criticality analysis of activity networks under interval uncertainty
Journal of Scheduling
Object association with belief functions, an application with vehicles
Information Sciences: an International Journal
Possibilistic bottleneck combinatorial optimization problems with ill-known weights
International Journal of Approximate Reasoning
Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables
International Journal of Approximate Reasoning
Numerical representations of acceptance
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A new approach on ρ to decision making using belief functions under incomplete information
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Information Sciences: an International Journal
A generic framework for a compilation-based inference in probabilistic and possibilistic networks
Information Sciences: an International Journal
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This paper advocates the use of nonpurely probabilistic approaches to higher-order uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decision-driven and as a consequence, uncertainty should be represented by probability. Here we argue that representing partial ignorance is not always decision-driven. Other reasoning tasks such as belief revision for instance are more naturally carried out at the purely cognitive level. Conceiving knowledge representation and decision-making as separate concerns opens the way to nonpurely probabilistic representations of incomplete knowledge. It is pointed out that within a numerical framework, two numbers are needed to account for partial ignorance about events, because on top of truth and falsity, the state of total ignorance must be encoded independently of the number of underlying alternatives. The paper also points out that it is consistent to accept a Bayesian view of decision-making and a non-Bayesian view of knowledge representation because it is possible to map nonprobabilistic degrees of belief to betting probabilities when needed. Conditioning rules in non-Bayesian settings are reviewed, and the difference between focusing on a reference class and revising due to the arrival of new information is pointed out. A comparison of Bayesian and non-Bayesian revision modes is discussed on a classical example