A logic for reasoning about probabilities
Information and Computation - Selections from 1988 IEEE symposium on logic in computer science
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Conditional entailment: bridging two approaches to default reasoning
Artificial Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Belief functions and default reasoning
Artificial Intelligence
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
Probabilistic Default Reasoning with Conditional Constraints
Annals of Mathematics and Artificial Intelligence
System Z: a natural ordering of defaults with tractable applications to nonmonotonic reasoning
TARK '90 Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Most techniques for probabilistic reasoning focus on reasoning about conditional probability constraints. However, human experts are accustomed to representing uncertain knowledge in the form of expectation rather than probability distribution directly in many cases. It is necessary to provide a logic for encoding hybrid probabilistic knowledge bases that contain expectation knowledge as well as the purely probabilistic knowledge in the form of conditional probability. This paper constructs a nonmonotonic logic for reasoning about hybrid probabilistic knowledge bases. We extend the propositional logic for reasoning about expectation to encoding hybrid probabilistic knowledge by introducing the conditional expectation constraint formula. Then we provide an approach to nonmonotonic reasoning about hybrid probabilistic knowledge bases. Finally, we compare this logic with related works.