Artificial Intelligence
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
Probabilistic semantics for nonmonotonic reasoning: a survey
Proceedings of the first international conference on Principles of knowledge representation and reasoning
Constraint propagation with imprecise conditional probabilities
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
On the consistency of defeasible databases
Artificial Intelligence
Anytime deduction for probabilistic logic
Artificial Intelligence
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
Diverse confidence levels in a probabilistic semantics for conditional logics
Artificial Intelligence
Probabilistic logic programming with conditional constraints
ACM Transactions on Computational Logic (TOCL)
Default Reasoning: Causal and Conditional Theories
Default Reasoning: Causal and Conditional Theories
Probabilistic Consistency of Conditional Probability Bounds
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
An Investigation of the Laws of Thought
An Investigation of the Laws of Thought
Probabilistic deduction with conditional constraints over basic events
Journal of Artificial Intelligence Research
Possibilistic logic, preferential models, non-monotonicity and related issues
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Weak nonmonotonic probabilistic logics
Artificial Intelligence
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We study probabilistic logic under the viewpoint of the coherence principle of de Finetti. In detail, we explore the relationship between coherence-based and model-theoretic probabilistic logic. Interestingly, we show that the notions of g-coherence and of g-coherent entailment can be expressed by combining notions in model-theoretic probabilistic logic with concepts from default reasoning. Crucially, we even show that probabilistic reasoning under coherence is a probabilistic generalization of default reasoning in system P. That is, we provide a new probabilistic semantics for system P, which is neither based on infinitesimal probabilities nor on atomic-bound (or also big-stepped) probabilities. These results also give new insight into default reasoning with conditional objects.