Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Using possibility theory in expert systems
Fuzzy Sets and Systems
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Causes and Explanations: A Structural-Model Approach: Part 1: Causes
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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
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Causality notion in the possibilistic framework has not been widely studied, despite its importance in context of poor or incomplete information. In this paper, we first propose an approach for handling interventions in quantitative possibilistic networks. The main advantage of this approach is its ability to unify treatments of both observations and interventions through the propagation process. We then propose a model based on quantitative possibilistic networks for ascribing causal relations between elements of the system by presenting some of their properties. Using such graphical structures allows to provide a more parcimonious inference process (comparing to the possibilistic model based on System P) that both accepts interventions and observations.