An Augmented-Based Approach for Compiling Min-based Possibilistic Causal Networks

This paper emphasizes on handling uncertain and causal information in a min-based possibility theory framework. More precisely, we focus on studying the representational point of view of interventions under a compilation framework. We propose two compilation-based inference algorithms for min-based possibilistic causal networks based on encoding the augmented network into a propositional theory and compiling this output in order to efficiently compute the effect of both observations and interventions.