Optimal expert elicitation to reduce interval uncertainty

Nadia Ben Abdallah, Sébastien Destercke
Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, PMLR R13:487-496, 2015.

Abstract

Reducing uncertainty is an important problem in many applications such as risk and reliability analysis, system design, etc. In this paper, we study the problem of optimally querying experts to reduce interval uncertainty. Surprisingly, this problem has received little attention in the past, while similar issues in preference elicitation or social choice theory have witnessed a rising interest. We propose and discuss some solutions to determine optimal questions in a myopic way (one-at-a-time), and study the computational aspects of these solutions both in general and for some specific functions of practical interest. Finally, we illustrate the application of the approach in reliability analysis problems.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR13-abdallah15a, title = {Optimal expert elicitation to reduce interval uncertainty}, author = {Abdallah, Nadia Ben and Destercke, S{\'e}bastien}, booktitle = {Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence}, pages = {487--496}, year = {2015}, editor = {Meila, Marina and Heskes, Tom}, volume = {R13}, series = {Proceedings of Machine Learning Research}, month = {12--16 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r13/main/assets/abdallah15a/abdallah15a.pdf}, url = {https://proceedings.mlr.press/r13/abdallah15a.html}, abstract = {Reducing uncertainty is an important problem in many applications such as risk and reliability analysis, system design, etc. In this paper, we study the problem of optimally querying experts to reduce interval uncertainty. Surprisingly, this problem has received little attention in the past, while similar issues in preference elicitation or social choice theory have witnessed a rising interest. We propose and discuss some solutions to determine optimal questions in a myopic way (one-at-a-time), and study the computational aspects of these solutions both in general and for some specific functions of practical interest. Finally, we illustrate the application of the approach in reliability analysis problems.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Optimal expert elicitation to reduce interval uncertainty %A Nadia Ben Abdallah %A Sébastien Destercke %B Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2015 %E Marina Meila %E Tom Heskes %F pmlr-vR13-abdallah15a %I PMLR %P 487--496 %U https://proceedings.mlr.press/r13/abdallah15a.html %V R13 %X Reducing uncertainty is an important problem in many applications such as risk and reliability analysis, system design, etc. In this paper, we study the problem of optimally querying experts to reduce interval uncertainty. Surprisingly, this problem has received little attention in the past, while similar issues in preference elicitation or social choice theory have witnessed a rising interest. We propose and discuss some solutions to determine optimal questions in a myopic way (one-at-a-time), and study the computational aspects of these solutions both in general and for some specific functions of practical interest. Finally, we illustrate the application of the approach in reliability analysis problems. %Z Reissued by PMLR on 04 October 2026.
APA
Abdallah, N.B. & Destercke, S.. (2015). Optimal expert elicitation to reduce interval uncertainty. Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R13:487-496 Available from https://proceedings.mlr.press/r13/abdallah15a.html. Reissued by PMLR on 04 October 2026.

Related Material