Uncertainty in Artificial Intelligence
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Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference
Raouia Ayachi, Nahla Ben Amor, Salem Benferhat, Rolf Haenni
Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory frame- work. Despite their importance, only the junction tree adaptation has been proposed for exact reasoning with such networks. This paper explores alternative algorithms using compilation techniques. We first propose possibilistic adaptations of standard compilation-based probabilistic methods. Then, we develop a new, purely possibilistic, method based on the transformation of the initial network into a possibilistic base. A comparative study shows that this latter performs better than the possibilistic adap- tations of probabilistic methods. This result is also confirmed by experimental results.
Pages: 40-47
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PDF Link: /papers/10/p40-ayachi.pdf
AUTHOR = "Raouia Ayachi and Nahla Ben Amor and Salem Benferhat and Rolf Haenni",
TITLE = "Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "40--47"

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