{"type":"ScholarlyArticle","title":"Efficient Algorithms And Representations For Chance-constrained Mixed Constraint Programming","authors":[{"name":"Cheng Fang","slug":"cheng-fang","given":"Cheng","family":"Fang","affiliation":"Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory 32 Vassar Street , Cambridge MA , 02139"}],"abstract":"Resistance to adoption of autonomous systems comes in part from the perceived unreliability of the systems. Concerns can be addressed by approaches that guarantee the probability of success. This is achieved in chance-constrained constraint programming (CC-CP) by  imposing constraints required for success, and providing upper-bounds on the probability of violating constraints. This extended abstract reports on novel uncertainty representations to address problems prevalent in current methods.","doi":"10.24963/ijcai.2017/749","url":"https://ijcai.org/proceedings/2017/749","pdf":"https://ijcai.org/proceedings/2017/0749.pdf","markdown":"https://ijcai.org/proceedings/2017/749.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/749","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":749,"pages":{"first":5179,"last":5180},"keywords":["Artificial Intelligence: constraints","Artificial Intelligence: artificial intelligence"],"videos":[],"references":[{"position":1,"raw":"[Bertsimas et al., 2017] Dimitris Bertsimas, Vishal Gupta, and Nathan Kallus. Data-driven robust optimization. Mathematical Programming, pages 1-58, 2017.","title":"Data-driven robust optimization","authors":"Dimitris Bertsimas, Vishal Gupta, Nathan Kallus","year":"2017","venue":"Mathematical Programming","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Fang et al., 2014] Cheng Fang, Peng Yu, and Brian C Williams. Chance-constrained probabilistic simple temporal problems. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, pages 2264-2270. AAAI Press, 2014.","title":"Chance-constrained probabilistic simple temporal problems","authors":"Cheng Fang, Peng Yu, Brian C Williams","year":"2014","venue":"Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Gibbons and Chakraborti, 2011] Jean Dickinson Gibbons and Subhabrata Chakraborti. Nonparametric statistical inference. Springer, 2011.","title":"Nonparametric statistical inference","authors":"Chakraborti Gibbons","year":"2011","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gill et al., 2005] Philip E Gill, Walter Murray, and Michael A Saunders. Snopt: An sqp algorithm for large-scale constrained optimization. SIAM review, 47(1):99-131, 2005.","title":"Snopt: An sqp algorithm for large-scale constrained optimization","authors":"Walter Philip E Gill, Michael A Murray, Saunders","year":"2005","venue":"SIAM review","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Jones et al., 2001] Eric Jones, Travis Oliphant, Pearu Peterson, et al. SciPy: Open source scientific tools for Python, 2001. [On- line; accessed 2017-03-06].","title":"SciPy: Open source scientific tools for Python","authors":"Eric Jones, Travis Oliphant, Pearu Peterson","year":"2001","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Wächter and Biegler, 2006] Andreas Wächter and Lorenz T Biegler. On the implementation of an interior-point filter line- search algorithm for large-scale nonlinear programming. Mathe- matical programming, 106(1):25-57, 2006.","title":"On the implementation of an interior-point filter linesearch algorithm for large-scale nonlinear programming","authors":"Biegler Wächter, Andreas Wächter, T Lorenz, Biegler","year":"2006","venue":"Mathematical programming","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}