Finding (α, Θ)-Solutions via Sampled SCSPs
Abstract
We discuss a novel approach for dealing with single-stage stochastic constraint satisfaction problems (SCSPs) that include random variables over a continuous or large discrete support. Our approach is based on two novel tools: sampled SCSPs and (α,ϑ)-solutions. Instead of explicitly enumerating a very large or infinite set of future scenarios, we employ statistical estimation to determine if a given assignment is consistent for a SCSP. As in statistical estimation, the quality of our estimate is determined via confidence interval analysis. In contrast to existing approaches based on sampling, we provide likelihood guarantees for the quality of the solutions found. Our approach can be used in concert with existing strategies for solving SCSPs.
Cite
Text
Rossi et al. "Finding (α, Θ)-Solutions via Sampled SCSPs." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-362Markdown
[Rossi et al. "Finding (α, Θ)-Solutions via Sampled SCSPs." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/rossi2011ijcai-finding/) doi:10.5591/978-1-57735-516-8/IJCAI11-362BibTeX
@inproceedings{rossi2011ijcai-finding,
title = {{Finding (α, Θ)-Solutions via Sampled SCSPs}},
author = {Rossi, Roberto and Hnich, Brahim and Tarim, S. Armagan and Prestwich, Steven D.},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2011},
pages = {2172-2177},
doi = {10.5591/978-1-57735-516-8/IJCAI11-362},
url = {https://mlanthology.org/ijcai/2011/rossi2011ijcai-finding/}
}