Title :
An Augmented-Based Approach for Compiling Min-based Possibilistic Causal Networks
Author :
Ayachi, Raouia ; Amor, N.B. ; Benferhat, Salem
Author_Institution :
LARODEC, ISG Tunis, Le Bardo, Tunisia
Abstract :
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.
Keywords :
inference mechanisms; possibility theory; augmented network; augmented-based approach; compilation-based inference algorithms; knowledge compilation; min-based possibilistic causal network compilation; representational point; Boolean functions; Cognition; Data structures; Electronic mail; Encoding; Knowledge based systems; Possibility theory; augmentation; compilation; inferring causal possibilistic networks; interventions;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on
Conference_Location :
Boca Raton, FL
Print_ISBN :
978-1-4577-2068-0
Electronic_ISBN :
1082-3409
DOI :
10.1109/ICTAI.2011.107