Propositional knowledge base revision and minimal change
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
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 and belief changes play an important role in many applications. Recently, Pearl [6] has proposed approaches based on probability theory using causal graphs to give formal semantics to the notion of interventions. From representational point of view, interventions are distinguished from observations using the concept of the "do" operator [4]. From reasoning point of view, handling interventions consists in "ignoring" the effects of all direct (and undirected) causes related to the variable of interest.