{"type":"ScholarlyArticle","title":"XOR-Sampling for Network Design with Correlated Stochastic Events","authors":[{"name":"Xiaojian Wu","slug":"xiaojian-wu","given":"Xiaojian","family":"Wu","affiliation":"Department of Computer Science , Cornell University , Ithaca , NY"},{"name":"Yexiang Xue","slug":"yexiang-xue","given":"Yexiang","family":"Xue","affiliation":"Department of Computer Science , Cornell University , Ithaca , NY"},{"name":"Bart Selman","slug":"bart-selman","given":"Bart","family":"Selman","affiliation":"Department of Computer Science , Cornell University , Ithaca , NY"},{"name":"Carla P. Gomes","slug":"carla-p-gomes","given":"Carla P.","family":"Gomes","affiliation":"Department of Computer Science , Cornell University , Ithaca , NY"}],"abstract":"Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can guarantee that edges will remain present.  We consider the problem of finding the optimal protection strategy under a budget limit in order to maximize some connectivity measurements of the network. Previous approaches rely on the assumption that edges are independent. \r\nIn this paper, we consider a more realistic setting where multiple edges are not independent due to natural disasters or regional events that make the states of multiple edges stochastically correlated. We use Markov Random Fields to model the correlation and define a new stochastic network design framework. We provide a novel algorithm based on Sample Average Approximation (SAA) coupled with a Gibbs or XOR sampler. The experimental results on real road network data show that the policies produced by SAA with the XOR sampler have higher quality and lower variance compared to SAA with Gibbs sampler.","doi":"10.24963/ijcai.2017/647","url":"https://ijcai.org/proceedings/2017/647","pdf":"https://ijcai.org/proceedings/2017/0647.pdf","markdown":"https://ijcai.org/proceedings/2017/647.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/647","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":"Uncertainty in AI","track":"Main track","number":647,"pages":{"first":4640,"last":4647},"keywords":["Uncertainty in AI: Graphical Models","Multidisciplinary Topics and Applications: Computational Sustainability","Combinatorial & Heuristic Search: Combinatorial search/optimisation","Constraints and Satisfiability: Constraint Optimisation"],"videos":[],"references":[{"position":1,"raw":"[Chakraborty et al., 2013] Supratik Chakraborty, Kuldeep S. Meel, and Moshe Y. Vardi. A scalable and nearly uni- form generator of sat witnesses. In Proceedings of the 25th International Conference on Computer Aided Verification, 2013.","title":"A scalable and nearly uniform generator of sat witnesses","authors":"Supratik Chakraborty, Kuldeep S Meel, Moshe Y Vardi","year":"2013","venue":"Proceedings of the 25th International Conference on Computer Aided Verification","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Dyer and Stougie, 2006] Martin Dyer and Leen Stougie. Computational complexity of stochastic programming problems. Mathematical Programming, 106(3):423-432, 2006.","title":"Computational complexity of stochastic programming problems","authors":"Stougie Dyer, Martin Dyer, Leen Stougie","year":"2006","venue":"Mathematical Programming","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ermon et al., 2013] Stefano Ermon, Carla P Gomes, Ashish Sabharwal, and Bart Selman. Embed and project: Discrete sampling with universal hashing. In Advances in Neural Information Processing Systems, pages 2085-2093, 2013.","title":"Embed and project: Discrete sampling with universal hashing","authors":"Stefano Ermon, Carla P Gomes, Ashish Sabharwal, Bart Selman","year":"2013","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gogate and Dechter, 2011] Vibhav Gogate and Rina Dechter. Samplesearch: Importance sampling in presence of determinism. Artificial Intelligence Journal, 2011.","title":"Samplesearch: Importance sampling in presence of determinism","authors":"Dechter ; ] Gogate, Gogate Vibhav, Rina Dechter","year":"2011","venue":"Artificial Intelligence Journal","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gomes et al., 2006] Carla P. Gomes, Ashish Sabharwal, and Bart Selman. Model counting: A new strategy for obtain- ing good bounds. In Proceedings of the 21st National Con- ference on Artificial Intelligence, 2006.","title":"Model counting: A new strategy for obtaining good bounds","authors":"Carla P Gomes, Ashish Sabharwal, Bart Selman","year":"2006","venue":"Proceedings of the 21st National Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Gomes et al., 2007] Carla P Gomes, Ashish Sabharwal, and Bart Selman. Near-uniform sampling of combinatorial spaces using xor constraints. In Advances in Neural In- formation Processing Systems, 2007.","title":"Near-uniform sampling of combinatorial spaces using xor constraints","authors":"Carla P Gomes, Ashish Sabharwal, Bart Selman","year":"2007","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kempe et al., 2003] David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a so- cial network. In Proceedings of the 9th ACM SIGKDD In- ternational Conference on Knowledge Discovery and Data Mining, pages 137-146, 2003.","title":"Maximizing the spread of influence through a social network","authors":"David Kempe, Jon Kleinberg, Éva Tardos","year":"2003","venue":"Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kleywegt et al., 2002] Anton J. Kleywegt, Alexander Shapiro, and Tito Homem-de Mello. The sample average approximation method for stochastic discrete optimiza- tion. SIAM Journal on Optimization, 12:479-502, February 2002.","title":"The sample average approximation method for stochastic discrete optimization","authors":"Anton J Kleywegt, Alexander Shapiro, Tito Homem, -De Mello","year":"2002","venue":"SIAM Journal on Optimization","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kumar et al., 2012] Akshat Kumar, Xiaojian Wu, and Shlomo Zilberstein. Lagrangian relaxation techniques for scalable spatial conservation planning. In Proceedings of the 26th Conference on Artificial Intelligence, pages 309- 315\", 2012.","title":"Lagrangian relaxation techniques for scalable spatial conservation planning","authors":"Akshat Kumar, Xiaojian Wu, Shlomo Zilberstein","year":"2012","venue":"Proceedings of the 26th Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Neeson et al., 2015] Thomas M Neeson, Michael C Ferris, Matthew W Diebel, Patrick J Doran, Jesse R OHanley, and Peter B McIntyre. Enhancing ecosystem restora- tion efficiency through spatial and temporal coordina- tion. Proceedings of the National Academy of Sciences, 112(19):6236-6241, 2015.","title":"Enhancing ecosystem restoration efficiency through spatial and temporal coordination","authors":"Michael C Thomas M Neeson, Matthew W Ferris, Patrick J Diebel, Jesse R Doran, Peter B Ohanley, Mcintyre","year":"2015","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Neumayer and Modiano, 2010] Sebastian Neumayer and Eytan Modiano. Network reliability with geographically correlated failures. In INFOCOM, 2010 Proceedings IEEE, pages 1-9. IEEE, 2010.","title":"Network reliability with geographically correlated failures","authors":"Modiano Neumayer, Sebastian Neumayer, Eytan Modiano","year":"2010","venue":"Proceedings IEEE","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Peeta et al., 2010] Srinivas Peeta, F. Sibel Salman, Dilek Gunnec, and Kannan Viswanath. Pre-disaster investment decisions for strengthening a highway network. Comput- ers and Operations Research, 37(10):1708-1719, 2010.","title":"Pre-disaster investment decisions for strengthening a highway network","authors":"Srinivas Peeta, F Sibel Salman, Dilek Gunnec, Kannan Viswanath","year":"2010","venue":"Computers and Operations Research","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Pipatsrisawat and Darwiche, 2010] Knot Pipatsrisawat and Adnan Darwiche. On modern clause-learning satisfiability solvers. Journal of Automated Reasoning, 2010.","title":"Knot Pipatsrisawat and Adnan Darwiche. On modern clause-learning satisfiability solvers","authors":"Darwiche Pipatsrisawat","year":"2010","venue":"Journal of Automated Reasoning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Poon and Domingos, 2006] Hoifung Poon and Pedro Domingos. Sound and efficient inference with probabilis- tic and deterministic dependencies. In Proceedings of the 21st Conference on Artificial Intelligence, 2006.","title":"Hoifung Poon and Pedro Domingos. Sound and efficient inference with probabilistic and deterministic dependencies","authors":"Domingos Poon","year":"2006","venue":"Proceedings of the 21st Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Rahnamay-Naeini et al., 2011] Mahshid Rahnamay- Naeini, Jorge E Pezoa, Ghady Azar, Nasir Ghani, and Majeed M Hayat. Modeling stochastic correlated failures and their effects on network reliability. In Computer Com- munications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on, pages 1-6. IEEE, 2011.","title":"Modeling stochastic correlated failures and their effects on network reliability","authors":"Mahshid Rahnamay-Naeini, Jorge E Pezoa, Ghady Azar, Nasir Ghani, M Majeed, Hayat","year":"2011","venue":"Proceedings of 20th International Conference on","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Riedel, 2008] Sebastian Riedel. Improving the accuracy and efficiency of map inference for markov logic. In Proceed- ings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence, 2008.","title":"Improving the accuracy and efficiency of map inference for markov logic","authors":"Sebastian Riedel","year":"2008","venue":"Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Sheldon et al., 2010] Daniel Sheldon, Bistra Dilkina, Adam Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Con- rad, Carla Gomes, David Shmoys, William Allen, Ole Amundsen, and William Vaughan. Maximizing the spread of cascades using network design. In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, pages 517-526, 2010.","title":"Maximizing the spread of cascades using network design","authors":"Daniel Sheldon, Bistra Dilkina, Adam Elmachtoub, Ryan Finseth, Ashish Sabharwal, Jon Conrad, Carla Gomes, David Shmoys, William Allen, Ole Amundsen, William Vaughan","year":"2010","venue":"Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Sontag et al., 2008] David Sontag, Talya Meltzer, Amir Globerson, Yair Weiss, and Tommi Jaakkola. Tightening LP relaxations for MAP using message-passing. In 24th Conference in Uncertainty in Artificial Intelligence, 2008.","title":"Tightening LP relaxations for MAP using message-passing","authors":"David Sontag, Talya Meltzer, Amir Globerson, Yair Weiss, Tommi Jaakkola","year":"2008","venue":"24th Conference in Uncertainty in Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Wu et al., 2014a] Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. Rounded dynamic programming for tree-structured stochastic network design. In Proceedings of the 28th Conference on Artificial Intelligence, 2014.","title":"Rounded dynamic programming for tree-structured stochastic network design","authors":"Wu","year":"2014","venue":"Proceedings of the 28th Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wu et al., 2014b] Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. Stochastic network design in bidi- rected trees. In Advances in Neural Information Process- ing Systems, pages 882-890, 2014.","title":"Stochastic network design in bidirected trees","authors":"Wu","year":"2014","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wu et al., 2015] Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein. Fast combinatorial algorithm for optimizing the spread of cascades. In Proceedings of the 24th Interna- tional Joint Conference on Artificial Intelligence (IJCAI), pages 2655-2661, 2015.","title":"Fast combinatorial algorithm for optimizing the spread of cascades","authors":"Xiaojian Wu, Daniel Sheldon, Shlomo Zilberstein","year":"2015","venue":"Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":"/proceedings/2015/376"},{"position":22,"raw":"[Wu et al., 2016] Xiaojian Wu, Daniel R Sheldon, and Shlomo Zilberstein. Optimizing resilience in large scale networks. In Proceedings of the 30th Conference on Arti- ficial Intelligence, 2016.","title":"Optimizing resilience in large scale networks","authors":"Xiaojian Wu, Shlomo Daniel R Sheldon, Zilberstein","year":"2016","venue":"Proceedings of the 30th Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wu et al., 2017] Xiaojian Wu, Akshat Kumar, Daniel Shel- don, and Shlomo Zilberstein. Robust optimization for tree- structured stochastic network design. In Proceedings of the 31st Conference on Artificial Intelligence, 2017.","title":"Robust optimization for treestructured stochastic network design","authors":"Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein","year":"2017","venue":"Proceedings of the 31st Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xue et al., 2016] Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P. Gomes, and Bart Selman. Solving marginal map problems with np oracles and parity constraints. In Ad- vances in Neural Information Processing Systems, 2016.","title":"Solving marginal map problems with np oracles and parity constraints","authors":"Yexiang Xue, Zhiyuan Li, Stefano Ermon, Carla P Gomes, Bart Selman","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Xue et al., 2017] Yexiang Xue, Xiaojian Wu, Dana Morin, Bistra Dilkina, Angela Fuller, J. Andrew Royle, and Carla P. Gomes. Dynamic optimization of landscape connectivity embedding spatial-capture-recapture infor- mation. In Proceedings of the 31th Conference on Arti- ficial Intelligence, 2017.","title":"Dynamic optimization of landscape connectivity embedding spatial-capture-recapture information","authors":"Yexiang Xue, Xiaojian Wu, Dana Morin, Bistra Dilkina, Angela Fuller, J Andrew Royle, Carla P Gomes","year":"2017","venue":"Proceedings of the 31th Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}