Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
What does a conditional knowledge base entail?
Artificial Intelligence
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
A logic of universal causation
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
An Empirical Test of Patterns for Nonmonotonic Inference
Annals of Mathematics and Artificial Intelligence
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
Determining explanations using transmutations
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A causal theory of ramifications and qualifications
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Predicting causality ascriptions from background knowledge: model and experimental validation
International Journal of Approximate Reasoning
A Comparative Study of Six Formal Models of Causal Ascription
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Transitive Observation-Based Causation, Saliency, and the Markov Condition
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Quantitative Possibilistic Networks: Handling Interventions and Ascribing Causality
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Interventions in Possibilistic Logic
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Inferring interventions in product-based possibilistic causal networks
Fuzzy Sets and Systems
Qualitative and quantitative conditions for the transitivity of perceived causation
Annals of Mathematics and Artificial Intelligence
Argument schemes for reasoning with legal cases using values
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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A model is defined that predicts an agent's ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on background knowledge about the normal course of the world. Background knowledge is represented by nonmonotonic consequence relations. This enables the model to handle situations of poor information, where background knowledge is not accurate enough to be represented in, e.g., structural equations. Tentative properties of causality ascriptions are explored, i.e., preference for abnormal factors, transitivity, coherence with logical entailment, and stability with respect to disjunction and conjunction. Empirical data are reported to support the psychological plausibility of our basic definitions.