Stochastic Constraint Programming
Abstract
Combinatorial optimisation problems often contain uncertainty that has to be taken into account to pro- duce realistic solutions. One way of describing the uncertainty is using scenarios, where each sce- nario describes different potential sets of problem parameters based on random distributions or his- torical data. While efficient algorithmic techniques exist for specific problem classes such as linear pro- grams, there are very few approaches that can han- dle general Constraint Programming formulations with uncertainty. The goal of my PhD is to develop generic methods for solving stochastic combina- torial optimisation problems formulated in a Con- straint Programming framework.
Cite
Text
Hemmi. "Stochastic Constraint Programming." International Joint Conference on Artificial Intelligence, 2017. doi:10.24963/IJCAI.2017/751Markdown
[Hemmi. "Stochastic Constraint Programming." International Joint Conference on Artificial Intelligence, 2017.](https://mlanthology.org/ijcai/2017/hemmi2017ijcai-stochastic/) doi:10.24963/IJCAI.2017/751BibTeX
@inproceedings{hemmi2017ijcai-stochastic,
title = {{Stochastic Constraint Programming}},
author = {Hemmi, David},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2017},
pages = {5183-5184},
doi = {10.24963/IJCAI.2017/751},
url = {https://mlanthology.org/ijcai/2017/hemmi2017ijcai-stochastic/}
}