{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T00:09:27Z","timestamp":1783814967177,"version":"3.55.0"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031210891","type":"print"},{"value":"9783031210907","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T00:00:00Z","timestamp":1671062400000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-21090-7_11","type":"book-chapter","created":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T18:11:35Z","timestamp":1671041495000},"page":"170-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive Discretization Using Voronoi Trees for Continuous-Action POMDPs"],"prefix":"10.1007","author":[{"given":"Marcus","family":"Hoerger","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hanna","family":"Kurniawati","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk","family":"Kroese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,15]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Agha-Mohammadi, A.A., Chakravorty, S., Amato, N.M.: Firm: Feedback controller-based information-state roadmap. A framework for motion planning under uncertainty. In: IROS, pp. 4284\u20134291. IEEE (2011)","DOI":"10.1109\/IROS.2011.6095010"},{"issue":"2\u20133","key":"11_CR2","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1023\/A:1013689704352","volume":"47","author":"P Auer","year":"2002","unstructured":"Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2\u20133), 235\u2013256 (2002)","journal-title":"Mach. Learn."},{"issue":"9","key":"11_CR3","first-page":"1288","volume":"33","author":"H Bai","year":"2014","unstructured":"Bai, H., Hsu, D., Lee, W.S.: Integrated perception and planning in the continuous space: a POMDP approach. IJRR 33(9), 1288\u20131302 (2014)","journal-title":"IJRR"},{"key":"11_CR4","unstructured":"Bubeck, S., Munos, R., Stoltz, G., Szepesv\u00e1ri, C.: X-armed bandits. J. Mach. Learn. Res. 12(5) (2011)"},{"key":"11_CR5","volume-title":"Numerical Analysis","author":"RL Burden","year":"2016","unstructured":"Burden, R.L., Faires, J.D., Burden, A.M.: Numerical Analysis, 10th edn. Cengage Learning, Boston (2016)","edition":"10"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Cou\u00ebtoux, A., Hoock, J.B., Sokolovska, N., Teytaud, O., Bonnard, N.: Continuous upper confidence trees. In: LION, pp. 433\u2013445. Springer (2011)","DOI":"10.1007\/978-3-642-25566-3_32"},{"key":"11_CR7","unstructured":"Fischer, J., Tas, \u00d6.S.: Information particle filter tree: an online algorithm for POMDPs with belief-based rewards on continuous domains. In: ICML, pp. 3177\u20133187. PMLR (2020)"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Hoerger, M., Kurniawati, H., Bandyopadhyay, T., Elfes, A.: Linearization in motion planning under uncertainty. In: Algorithmic Foundations of Robotics XII, pp. 272\u2013287. Springer, Cham (2020)","DOI":"10.1007\/978-3-030-43089-4_18"},{"key":"11_CR9","unstructured":"Hoerger, M., Kurniawati, H., Elfes, A.: OPPT. https:\/\/github.com\/RDLLab\/oppt"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Hoerger, M., Kurniawati, H., Elfes, A.: A software framework for planning under partial observability. In: IROS, pp. 1\u20139. IEEE (2018)","DOI":"10.1109\/IROS.2018.8593714"},{"issue":"1\u20132","key":"11_CR11","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/S0004-3702(98)00023-X","volume":"101","author":"LP Kaelbling","year":"1998","unstructured":"Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1\u20132), 99\u2013134 (1998)","journal-title":"Artif. Intell."},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"Kim, B., Lee, K., Lim, S., Kaelbling, L., Lozano-P\u00e9rez, T.: Monte carlo tree search in continuous spaces using Voronoi optimistic optimization with regret bounds. In: AAAI, vol. 34, pp. 9916\u20139924 (2020)","DOI":"10.1609\/aaai.v34i06.6546"},{"key":"11_CR13","unstructured":"Klimenko, D., Song, J., Kurniawati, H.: TAPIR. https:\/\/github.com\/RDLLab\/tapir"},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Kurniawati, H.: Partially observable Markov decision processes and robotics. Ann. Rev. Control Robot. Auton. Syst. 5(1) (2022). To appear","DOI":"10.1146\/annurev-control-042920-092451"},{"issue":"3","key":"11_CR15","first-page":"308","volume":"30","author":"H Kurniawati","year":"2011","unstructured":"Kurniawati, H., Du, Y., Hsu, D., Lee, W.S.: Motion planning under uncertainty for robotic tasks with long time horizons. IJRR 30(3), 308\u2013323 (2011)","journal-title":"IJRR"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Kurniawati, H., Hsu, D., Lee, W.S.: SARSOP: Efficient point-based POMDP planning by approximating optimally reachable belief spaces. In: RSS (2008)","DOI":"10.15607\/RSS.2008.IV.009"},{"key":"11_CR17","unstructured":"Kurniawati, H., Yadav, V.: An online POMDP solver for uncertainty planning in dynamic environment. In: Proceedings of the International Symposium on Robotics Research (2013)"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Lim, M.H., Tomlin, C.J., Sunberg, Z.N.: Voronoi progressive widening: efficient online solvers for continuous state, action, and observation POMDPs. In: 60th IEEE Conference on Decision and Control (CDC), pp. 4493\u20134500 (2021)","DOI":"10.1109\/CDC45484.2021.9683490"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Mansley, C., Weinstein, A., Littman, M.: Sample-based planning for continuous action Markov decision processes. In: ICAPS (2011)","DOI":"10.1609\/icaps.v21i1.13484"},{"key":"11_CR20","doi-asserted-by":"crossref","unstructured":"Mern, J., Yildiz, A., Sunberg, Z., Mukerji, T., Kochenderfer, M.J.: Bayesian optimized monte carlo planning. In: AAAI, vol. 35, pp. 11880\u201311887 (2021)","DOI":"10.1609\/aaai.v35i13.17411"},{"issue":"3","key":"11_CR21","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1287\/moor.12.3.441","volume":"12","author":"CH Papadimitriou","year":"1987","unstructured":"Papadimitriou, C.H., Tsitsiklis, J.N.: The complexity of Markov decision processes. Math. Oper. Res. 12(3), 441\u2013450 (1987)","journal-title":"Math. Oper. Res."},{"key":"11_CR22","unstructured":"Pineau, J., Gordon, G., Thrun, S.: Point-based value iteration: an anytime algorithm for POMDPs. In: IJCAI (2003)"},{"key":"11_CR23","doi-asserted-by":"crossref","unstructured":"Seiler, K.M., Kurniawati, H., Singh, S.P.: An online and approximate solver for POMDPs with continuous action space. In: ICRA, pp. 2290\u20132297. IEEE (2015)","DOI":"10.1109\/ICRA.2015.7139503"},{"key":"11_CR24","unstructured":"Silver, D., Veness, J.: Monte Carlo planning in large POMDPs. In: Advances in Neural Information Processing Systems, pp. 2164\u20132172 (2010)"},{"issue":"6","key":"11_CR25","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1287\/opre.32.6.1296","volume":"32","author":"RL Smith","year":"1984","unstructured":"Smith, R.L.: Efficient monte carlo procedures for generating points uniformly distributed over bounded regions. Oper. Res. 32(6), 1296\u20131308 (1984)","journal-title":"Oper. Res."},{"key":"11_CR26","unstructured":"Smith, T., Simmons, R.: Point-based POMDP algorithms: improved analysis and implementation. In: UAI (2005)"},{"key":"11_CR27","unstructured":"Sondik, E.J.: The optimal control of partially observable Markov decision processes. Ph.D. Thesis, Stanford, California (1971)"},{"issue":"1","key":"11_CR28","first-page":"104","volume":"31","author":"W Sun","year":"2015","unstructured":"Sun, W., Patil, S., Alterovitz, R.: High-frequency replanning under uncertainty using parallel sampling-based motion planning. IEEE TRO 31(1), 104\u2013116 (2015)","journal-title":"IEEE TRO"},{"key":"11_CR29","doi-asserted-by":"crossref","unstructured":"Sunberg, Z.N., Kochenderfer, M.J.: Online algorithms for POMDPs with continuous state, action, and observation spaces. In: ICAPS (2018)","DOI":"10.1609\/icaps.v28i1.13882"},{"key":"11_CR30","unstructured":"Touati, A., Taiga, A.A., Bellemare, M.G.: Zooming for efficient model-free reinforcement learning in metric spaces (2020). arXiv:2003.04069"},{"key":"11_CR31","unstructured":"Valko, M., Carpentier, A., Munos, R.: Stochastic simultaneous optimistic optimization. In: ICML, pp. 19\u201327. PMLR (2013)"},{"issue":"7","key":"11_CR32","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1177\/0278364911406562","volume":"30","author":"J Van Den Berg","year":"2011","unstructured":"Van Den Berg, J., Abbeel, P., Goldberg, K.: LQG-MP: optimized path planning for robots with motion uncertainty and imperfect state information. Int. J. Robot. Res. 30(7), 895\u2013913 (2011)","journal-title":"Int. J. Robot. Res."},{"issue":"11","key":"11_CR33","first-page":"1263","volume":"31","author":"J Van Den Berg","year":"2012","unstructured":"Van Den Berg, J., Patil, S., Alterovitz, R.: Motion planning under uncertainty using iterative local optimization in belief space. IJRR 31(11), 1263\u20131278 (2012)","journal-title":"IJRR"},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Wang, T., Ye, W., Geng, D., Rudin, C.: Towards practical Lipschitz bandits. In: FODS, pp. 129\u2013138 (2020)","DOI":"10.1145\/3412815.3416885"},{"issue":"3","key":"11_CR35","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF00992698","volume":"8","author":"CJ Watkins","year":"1992","unstructured":"Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279\u2013292 (1992)","journal-title":"Mach. Learn."},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Welzl, E.: Smallest enclosing disks (balls and ellipsoids). In: Maurer, H. (ed.) New Results and New Trends in Computer Science, pp. 359\u2013370. Springer, Berlin (1991)","DOI":"10.1007\/BFb0038202"},{"key":"11_CR37","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1613\/jair.5328","volume":"58","author":"N Ye","year":"2017","unstructured":"Ye, N., Somani, A., Hsu, D., Lee, W.S.: DESPOT: online POMDP planning with regularization. J. Artif. Intell. Res. 58, 231\u2013266 (2017)","journal-title":"J. Artif. Intell. Res."}],"container-title":["Springer Proceedings in Advanced Robotics","Algorithmic Foundations of Robotics XV"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21090-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T18:17:43Z","timestamp":1671041863000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21090-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,15]]},"ISBN":["9783031210891","9783031210907"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21090-7_11","relation":{},"ISSN":["2511-1256","2511-1264"],"issn-type":[{"value":"2511-1256","type":"print"},{"value":"2511-1264","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,15]]},"assertion":[{"value":"15 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WAFR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on the Algorithmic Foundations of Robotics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":", MD","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wafr2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/wafr2022.github.io","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}