{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T08:40:01Z","timestamp":1751100001935,"version":"3.41.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031959721","type":"print"},{"value":"9783031959738","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-95973-8_15","type":"book-chapter","created":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T08:01:01Z","timestamp":1751097661000},"page":"239-255","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bounded-Error Policy Optimization for\u00a0Mixed Discrete-Continuous MDPs via\u00a0Constraint Generation in\u00a0Nonlinear Programming"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4377-2142","authenticated-orcid":false,"given":"Michael","family":"Gimelfarb","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3919-6883","authenticated-orcid":false,"given":"Ayal","family":"Taitler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7984-8394","authenticated-orcid":false,"given":"Scott","family":"Sanner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,29]]},"reference":[{"issue":"1","key":"15_CR1","first-page":"229","volume":"59","author":"A Ahmed","year":"2017","unstructured":"Ahmed, A., Varakantham, P., Lowalekar, M., Adulyasak, Y., Jaillet, P.: Sampling based approaches for minimizing regret in uncertain markov decision processes (mdps). J. Artif. Int. Res. 59(1), 229\u2013264 (2017)","journal-title":"J. Artif. Int. Res."},{"key":"15_CR2","unstructured":"Albert, L.A.: A mixed-integer programming model for identifying intuitive ambulance dispatching policies. J. Oper. Res. Soc., 1\u201312 (2022)"},{"key":"15_CR3","unstructured":"Ariu, K., Fang, C., da\u00a0Silva\u00a0Arantes, M., Toledo, C., Williams, B.C.: Chance-constrained path planning with continuous time safety guarantees. In: AAAI Workshop (2017)"},{"key":"15_CR4","unstructured":"\u00c5str\u00f6m, K.J.: Introduction to Stochastic Control Theory. Courier Corporation (2012)"},{"key":"15_CR5","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/BF00934096","volume":"19","author":"JW Blankenship","year":"1976","unstructured":"Blankenship, J.W., Falk, J.E.: Infinitely constrained optimization problems. J. Optim. Theory Appl. 19, 261\u2013281 (1976)","journal-title":"J. Optim. Theory Appl."},{"key":"15_CR6","doi-asserted-by":"crossref","unstructured":"Bueno, T.P., de\u00a0Barros, L.N., Mau\u00e1, D.D., Sanner, S.: Deep reactive policies for planning in stochastic nonlinear domains. In: AAAI, vol.\u00a033, pp. 7530\u20137537 (2019)","DOI":"10.1609\/aaai.v33i01.33017530"},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1016\/j.compchemeng.2014.03.025","volume":"72","author":"PM Castro","year":"2015","unstructured":"Castro, P.M.: Tightening piecewise mccormick relaxations for bilinear problems. Comput. Chem. Eng. 72, 300\u2013311 (2015)","journal-title":"Comput. Chem. Eng."},{"key":"15_CR8","doi-asserted-by":"crossref","unstructured":"Chembu, A., Sanner, S., Khurram, H., Kumar, A.: Scalable and globally optimal generalized l1 k-center clustering via constraint generation in mixed integer linear programming. In: AAAI, vol.\u00a037, pp. 7015\u20137023 (2023)","DOI":"10.1609\/aaai.v37i6.25857"},{"key":"15_CR9","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1613\/jair.1.12716","volume":"72","author":"A Corso","year":"2021","unstructured":"Corso, A., Moss, R., Koren, M., Lee, R., Kochenderfer, M.: A survey of algorithms for black-box safety validation of cyber-physical systems. J. Artif. Intell. Res. 72, 377\u2013428 (2021)","journal-title":"J. Artif. Intell. Res."},{"key":"15_CR10","unstructured":"Dolgov, D., Durfee, E.: Stationary deterministic policies for constrained mdps with multiple rewards, costs, and discount factors. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence. IJCAI\u201905, pp. 1326\u20131331. Morgan Kaufmann Publishers Inc., San Francisco (2005)"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Farias, V.F., Van\u00a0Roy, B.: Tetris: a study of randomized constraint sampling, pp. 189\u2013201. Springer, London (2006). https:\/\/doi.org\/10.1007\/1-84628-095-8_6","DOI":"10.1007\/1-84628-095-8_6"},{"key":"15_CR12","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jprocont.2016.03.005","volume":"44","author":"M Farina","year":"2016","unstructured":"Farina, M., Giulioni, L., Scattolini, R.: Stochastic linear model predictive control with chance constraints-a review. J. Process Control 44, 53\u201367 (2016)","journal-title":"J. Process Control"},{"key":"15_CR13","unstructured":"Hauskrecht, M.: Approximate linear programming for solving hybrid factored mdps. In: International Symposium on Artificial Intelligence and Mathematics, AI &Math 2006, Fort Lauderdale, Florida, USA, 4\u20136 January 2006 (2006)"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Low, S.M., Kumar, A., Sanner, S.: Sample-efficient iterative lower bound optimization of deep reactive policies for planning in continuous mdps. In: AAAI, vol.\u00a036, pp. 9840\u20139848 (2022)","DOI":"10.1609\/aaai.v36i9.21220"},{"key":"15_CR15","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.disopt.2016.01.005","volume":"19","author":"DR Morrison","year":"2016","unstructured":"Morrison, D.R., Jacobson, S.H., Sauppe, J.J., Sewell, E.C.: Branch-and-bound algorithms: a survey of recent advances in searching, branching, and pruning. Disc. Optim. 19, 79\u2013102 (2016)","journal-title":"Disc. Optim."},{"key":"15_CR16","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/s10514-015-9467-7","volume":"39","author":"M Ono","year":"2015","unstructured":"Ono, M., Pavone, M., Kuwata, Y., Balaram, J.: Chance-constrained dynamic programming with application to risk-aware robotic space exploration. Auton. Robot. 39, 555\u2013571 (2015)","journal-title":"Auton. Robot."},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Patton, N., Jeong, J., Gimelfarb, M., Sanner, S.: A distributional framework for risk-sensitive end-to-end planning in continuous mdps. In: AAAI, vol.\u00a036, pp. 9894\u20139901 (2022)","DOI":"10.1609\/aaai.v36i9.21226"},{"key":"15_CR18","volume-title":"Markov Decision Processes: Discrete Stochastic Dynamic Programming","author":"ML Puterman","year":"2014","unstructured":"Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Hoboken (2014)"},{"key":"15_CR19","doi-asserted-by":"crossref","unstructured":"Raghavan, A., Sanner, S., Khardon, R., Tadepalli, P., Fern, A.: Hindsight optimization for hybrid state and action mdps. In: AAAI, vol.\u00a031 (2017)","DOI":"10.1609\/aaai.v31i1.11056"},{"key":"15_CR20","unstructured":"Scala, E., Haslum, P., Thi\u00e9baux, S., Ramirez, M.: Interval-based relaxation for general numeric planning. In: ECAI, pp. 655\u2013663. IOS Press (2016)"},{"key":"15_CR21","unstructured":"Scarf, H., Arrow, K., Karlin, S., Suppes, P.: The optimality of (s, s) policies in the dynamic inventory problem. In: Optimal Pricing, Inflation, and the Cost of Price Adjustment, pp. 49\u201356 (1960)"},{"key":"15_CR22","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. ArXiv arxiv:1707.06347 (2017)"},{"key":"15_CR23","unstructured":"Schuurmans, D., Patrascu, R.: Direct value-approximation for factored MDPs. In: Dietterich, T., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol.\u00a014. MIT Press (2001)"},{"issue":"3","key":"15_CR24","doi-asserted-by":"publisher","first-page":"2497","DOI":"10.1007\/s10462-022-10228-y","volume":"56","author":"M \u015awiechowski","year":"2023","unstructured":"\u015awiechowski, M., Godlewski, K., Sawicki, B., Ma\u0144dziuk, J.: Monte carlo tree search: a review of recent modifications and applications. Artif. Intell. Rev. 56(3), 2497\u20132562 (2023)","journal-title":"Artif. Intell. Rev."},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Topin, N., Milani, S., Fang, F., Veloso, M.: Iterative bounding mdps: learning interpretable policies via non-interpretable methods. In: AAAI, vol.\u00a035, pp. 9923\u20139931 (2021)","DOI":"10.1609\/aaai.v35i11.17192"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Vos, D., Verwer, S.: Optimal decision tree policies for markov decision processes. In: IJCAI, pp. 5457\u20135465 (2023)","DOI":"10.24963\/ijcai.2023\/606"},{"issue":"1","key":"15_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dcan.2021.06.001","volume":"8","author":"Y Wang","year":"2022","unstructured":"Wang, Y., et al.: A survey on deploying mobile deep learning applications: a systemic and technical perspective. Digital Commun. Netw. 8(1), 1\u201317 (2022)","journal-title":"Digital Commun. Netw."},{"key":"15_CR28","unstructured":"Wu, G., Say, B., Sanner, S.: Scalable planning with tensorflow for hybrid nonlinear domains. In: NeurIPS, vol. 30 (2017)"}],"container-title":["Lecture Notes in Computer Science","Integration of Constraint Programming, Artificial Intelligence, and Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-95973-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,28]],"date-time":"2025-06-28T08:01:07Z","timestamp":1751097667000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-95973-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031959721","9783031959738"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-95973-8_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"29 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CPAIOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cpaior2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/cpaior2025","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}