Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model theoretic approach to propositional fuzzy logic using Beth Tableaux
Fuzzy logic for the management of uncertainty
A first course in fuzzy logic
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Machine Learning
Rigid Flexibility: The Logic of Intelligence (Applied Logic Series)
Rigid Flexibility: The Logic of Intelligence (Applied Logic Series)
Artificial General Intelligence (Cognitive Technologies)
Artificial General Intelligence (Cognitive Technologies)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Artificial Intelligence: Structures and Strategies for Complex Problem Solving
Toward general analysis of recursive probability models
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
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P(Z) logic offers a new way to reason about vagueness (ie fuzziness), that treats fuzziness as degrees, distinct from probabilities. One then applies probability distributions over fuzziness. This approach is different from both classical fuzzy logic [26] and possibility theory [1]. P(Z) logic is specially designed for common-sense reasoning.