Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A valuation-based language for expert systems
International Journal of Approximate Reasoning
The logical view of conditioning and its application to possibility and evidence theories
International Journal of Approximate Reasoning
Valuation-based systems for propositional logic
Methodologies for intelligent systems, 5
Handbook of logic in artificial intelligence and logic programming (vol. 3)
Independence concepts in possibility theory: part I
Fuzzy Sets and Systems
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Background and Perspectives of Possibilistic Graphical Models
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Propositional non-monotonic reasoning and inconsistency in symmetric neural networks
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Reasoning with conditional ceteris paribus preference statements
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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In the framework of quantitative possibility theory, two representation modes were developed: logical representation in term of quantitative possibilistic base and graphical representation in term of product-based possibilistic network. This article deals with logical and graphical representations of uncertain information around quantitative possibility theory. First, a deep analysis of relationships between these two forms of representational frameworks is provided. Then, in the logical setting, syntactical relations between penalty logic and quantitative possibilistic base are developed. Afterward, the relationship which exists between UCP networks and product-based possibilistic networks is pointed out in the graphical setting. These translations are useful for different applications and are interesting by taking advantage from each format at the inferential level. From these translations, we also exhibit the relation which is deduced, between UCP networks and penalty logic.