Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Explorations in parallel distributed processing: a handbook of models, programs, and exercises
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Artificial Intelligence - Special issue on logical formalizations and commonsense reasoning
Predicting causality ascriptions from background knowledge: model and experimental validation
International Journal of Approximate 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
Possibilistic causal networks for handling interventions: a new propagation algorithm
AAAI'07 Proceedings of the 22nd national 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
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
Inferring interventions in product-based possibilistic causal networks
Fuzzy Sets and Systems
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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Ascribing causality amounts to determining what elements in a sequence of reported facts can be related in a causal way, on the basis of some knowledge about the course of the world. The paper offers a comparison of a large span of formal models (based on structural equations, non-monotonic consequence relations, trajectory preference relations, identification of violated norms, graphical representations, or connectionism), using a running example taken from a corpus of car accident reports. Interestingly enough, the compared approaches focus on different aspects of the problem by either identifying all the potential causes, or selecting a smaller subset by taking advantages of contextually abnormal facts, or by modeling interventions to get rid of simple correlations. The paper concludes by a general discussion based on a battery of criteria (several of them being proper to AI approaches to causality).