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Communication Dans Un Congrès Année : 2006

Background default knowledge and causality ascriptions

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 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.
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hal-03361586 , version 1 (01-10-2021)

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  • HAL Id : hal-03361586 , version 1

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Jean-François Bonnefon, Rui da Silva Neves, Didier Dubois, Henri Prade. Background default knowledge and causality ascriptions. 17th European Conference on Artificial Intelligence (ECAI 2006), European Coordinating Committee for Artificial Intelligence (ECCAI); Italian Association of Artificial Intelligence, Aug 2006, Riva del Garda, Italy. pp.11-15. ⟨hal-03361586⟩
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