Predicting causality ascriptions from background knowledge: model and experimental validation

Predicting causality ascriptions from background knowledge: model and experimental validation

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....

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Veröffentlicht in:International journal of approximate reasoning 2008-08, Vol.48 (3), p.752-765
Hauptverfasser: Bonnefon, Jean-François, Da Silva Neves, Rui, Dubois, Didier, Prade, Henri
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Computer Science
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container_title International journal of approximate reasoning
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creator Bonnefon, Jean-François
Da Silva Neves, Rui
Dubois, Didier
Prade, Henri
description 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.
doi_str_mv 10.1016/j.ijar.2007.07.003
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subjects Artificial Intelligence
Computer Science
title Predicting causality ascriptions from background knowledge: model and experimental validation
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