Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference
Abstract
Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory framework. 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 adaptations of probabilistic methods. This result is also confirmed by experimental results.
Cite
Text
Ayachi et al. "Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference." Conference on Uncertainty in Artificial Intelligence, 2010.Markdown
[Ayachi et al. "Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference." Conference on Uncertainty in Artificial Intelligence, 2010.](https://mlanthology.org/uai/2010/ayachi2010uai-compiling/)BibTeX
@inproceedings{ayachi2010uai-compiling,
title = {{Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference}},
author = {Ayachi, Raouia and Amor, Nahla Ben and Benferhat, Salem and Haenni, Rolf},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2010},
pages = {40-47},
url = {https://mlanthology.org/uai/2010/ayachi2010uai-compiling/}
}