Heuristic reasoning about uncertainty: an artificial intelligence approach
Heuristic reasoning about uncertainty: an artificial intelligence approach
Artificial Intelligence
Bayesian and non-Bayesian evidential updating
Artificial Intelligence
A logic to reason about likelihood
Artificial Intelligence
Higher order probability and intervals
International Journal of Approximate Reasoning
Journal of Complexity
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On the Conceptual Status of Belief Functions with Respect to Coherent Lower Probabilities
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Pr$\mathcal{SH}$: A Belief Description Logic
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
PrDLs: a new kind of probabilistic description logics about belief
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Fault tree analysis of software-controlled component systems based on second-order probabilities
ISSRE'09 Proceedings of the 20th IEEE international conference on software reliability engineering
Editorial: Acquiring knowledge from inconsistent data sources through weighting
Data & Knowledge Engineering
Secure transaction protocol analysis: models and applications
Secure transaction protocol analysis: models and applications
Three alternative combinatorial formulations of the theory of evidence
Intelligent Data Analysis - Artificial Intelligence
Two views of belief: belief as generalized probability and belief as evidence
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
The belief calculus and uncertain reasoning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A hybrid framework for representing uncertain knowledge
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Approximate reasoning systems: a personal perspective
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Dealing with inconsistent secure messages by weighting majority
Knowledge-Based Systems
Hybrid probabilistic programs: algorithms and complexity
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Belief and surprise: a belief-function formulation
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Evidential reasoning in a categorial perspective: conjunction and disjunction of belief functions
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Non-monotonic negation in probabilistic deductive databases
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
On the relative belief transform
International Journal of Approximate Reasoning
Extreme points of the credal sets generated by elementary comparative probabilities
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. Thus, the measure of belief in an event can be represented by an interval (defined by the inner and outer measure), rather than by a single number. Further, this approach allows us to assign a belief (inner measure) to an event E without committing to a belief about its negation E (since the inner measure of an event plus the inner measure of its negation is not necessarily one). Interestingly enough, inner measures induced by probability measures turn out to correspond in a precise sense to Dempster-Shafer belief functions. Hence, in addition to providing promising new conceptual tools for dealing with uncertainty, our approach shows that a key part of the important Dempster-Shafer theory of evidence is firmly rooted in classical probability theory.