Compiling Possibilistic Networks : Alternative Approaches to Possibilistic Inference

Raouia Ayachi, Nahla Ben Amor, Salem Benferhat, Rolf Haenni
Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, PMLR R8:40-47, 2010.

Abstract

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR8-ayachi10a, title = {Compiling Possibilistic Networks : Alternative Approaches to Possibilistic Inference}, author = {Ayachi, Raouia and Amor, Nahla Ben and Benferhat, Salem and Haenni, Rolf}, booktitle = {Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence}, pages = {40--47}, year = {2010}, editor = {Grünwald, Peter and Spirtes, Peter}, volume = {R8}, series = {Proceedings of Machine Learning Research}, month = {08--11 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r8/main/assets/ayachi10a/ayachi10a.pdf}, url = {https://proceedings.mlr.press/r8/ayachi10a.html}, abstract = {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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Compiling Possibilistic Networks : Alternative Approaches to Possibilistic Inference %A Raouia Ayachi %A Nahla Ben Amor %A Salem Benferhat %A Rolf Haenni %B Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2010 %E Peter Grünwald %E Peter Spirtes %F pmlr-vR8-ayachi10a %I PMLR %P 40--47 %U https://proceedings.mlr.press/r8/ayachi10a.html %V R8 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Ayachi, R., Amor, N.B., Benferhat, S. & Haenni, R.. (2010). Compiling Possibilistic Networks : Alternative Approaches to Possibilistic Inference. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R8:40-47 Available from https://proceedings.mlr.press/r8/ayachi10a.html. Reissued by PMLR on 04 October 2026.

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