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Article Dans Une Revue International Journal of Approximate Reasoning Année : 2008

Predicting causality ascriptions from background knowledge: model and experimental validation

Résumé

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.

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Dates et versions

hal-03358847 , version 1 (29-09-2021)

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Jean-François Bonnefon, Rui da Silva Neves, Didier Dubois, Henri Prade. Predicting causality ascriptions from background knowledge: model and experimental validation. International Journal of Approximate Reasoning, 2008, 48 (3), pp.752-765. ⟨10.1016/j.ijar.2007.07.003⟩. ⟨hal-03358847⟩
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