A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
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
Diverse confidence levels in a probabilistic semantics for conditional logics
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible 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
Background default knowledge and causality ascriptions
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Determining explanations using transmutations
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Responsibility and blame: a structural-model approach
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Causal theories for nonmonotonic reasoning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
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
Making Sense of a Sequence of Events: A Psychologically Supported AI Implementation
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
Making sense as a process emerging from perception–memory interaction: A model
International Journal of Intelligent Systems
Qualitative and quantitative conditions for the transitivity of perceived causation
Annals of Mathematics and Artificial Intelligence
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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 non-monotonic 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 discussed, and the conditions under which they hold are identified (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.