{"type":"ScholarlyArticle","title":"Stochastic Constraint Programming","authors":[{"name":"David Hemmi","slug":"david-hemmi","given":"David","family":"Hemmi","affiliation":"Monash University Data61/CSIRO , Melbourne , Australia"}],"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.","doi":"10.24963/ijcai.2017/751","url":"https://ijcai.org/proceedings/2017/751","pdf":"https://ijcai.org/proceedings/2017/0751.pdf","markdown":"https://ijcai.org/proceedings/2017/751.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/751","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":751,"pages":{"first":5183,"last":5184},"keywords":["Artificial Intelligence: uncertainty in artificial intelligence","Artificial Intelligence: constraints","Artificial Intelligence: computer science","Artificial Intelligence: search and constraint satisfaction"],"videos":[],"references":[{"position":1,"raw":"[Ahmed, 2013] Shabbir Ahmed. A scenario decomposition algorithm for 0-1 stochastic programs. Operations Re- search Letters, 41(6):565-569, 2013.","title":"A scenario decomposition algorithm for 0-1 stochastic programs","authors":"Shabbir Ahmed","year":"2013","venue":"Operations Research Letters","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Albareda-Sambola et al., 2006] Maria Albareda-Sambola, Maarten H Van Der Vlerk, and Elena Fernández. Ex- act solutions to a class of stochastic generalized assign- ment problems. European journal of operational research, 173(2):465-487, 2006.","title":"Exact solutions to a class of stochastic generalized assignment problems","authors":"Maria Albareda-Sambola, H Maarten, Van Der, Elena Vlerk, Fernández","year":"2006","venue":"European journal of operational research","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Birge and Louveaux, 2011] John R Birge and Francois Lou- veaux. Introduction to stochastic programming. Springer Science & Business Media, 2011.","title":"Introduction to stochastic programming","authors":"R John, Francois Birge, Louveaux","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Hemmi et al., 2017] David Hemmi, Guido Tack, and Mark Wallace. Scenario-based learning for stochastic combina- torial optimisation. In Springer, editor, Integration of AI and OR Techniques in Constraint Programming for Com- binatorial Optimization Problems, 2017.","title":"Scenario-based learning for stochastic combinatorial optimisation","authors":"David Hemmi, Guido Tack, Mark Wallace","year":"2017","venue":"Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Nethercote et al., 2007] Nicholas Nethercote, Peter J Stuckey, Ralph Becket, Sebastian Brand, Gregory J Duck, and Guido Tack. Minizinc: Towards a standard cp modelling language. In Principles and Practice of Constraint Programming, pages 529-543. Springer, 2007.","title":"Minizinc: Towards a standard cp modelling language","authors":"Nicholas Nethercote, J Peter, Ralph Stuckey, Sebastian Becket, Gregory J Brand, Guido Duck, Tack","year":"2007","venue":"Principles and Practice of Constraint Programming","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Rendl et al., 2014] Andrea Rendl, Guido Tack, and Peter J Stuckey. Stochastic minizinc. In Principles and Prac- tice of Constraint Programming, pages 636-645. Springer, 2014.","title":"Stochastic minizinc","authors":"Andrea Rendl, Guido Tack, Peter J Stuckey","year":"2014","venue":"Principles and Practice of Constraint Programming","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}