{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T15:43:44Z","timestamp":1784303024718,"version":"3.55.0"},"reference-count":29,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2010,5,4]],"date-time":"2010-05-04T00:00:00Z","timestamp":1272931200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2010,7]]},"abstract":"<jats:p>Partially observable Markov decision processes (POMDPs) provide a principled, general framework for robot motion planning in uncertain and dynamic environments. They have been applied to various robotic tasks. However, solving POMDPs exactly is computationally intractable. A major challenge is to scale up POMDP algorithms for complex robotic tasks. Robotic systems often have mixed observability : even when a robot\u2019s state is not fully observable, some components of the state may still be so. We use a factored model to represent separately the fully and partially observable components of a robot\u2019s state and derive a compact lower-dimensional representation of its belief space. This factored representation can be combined with any point-based algorithm to compute approximate POMDP solutions. Experimental results show that on standard test problems, our approach improves the performance of a leading point-based POMDP algorithm by many times.<\/jats:p>","DOI":"10.1177\/0278364910369861","type":"journal-article","created":{"date-parts":[[2010,5,4]],"date-time":"2010-05-04T21:50:51Z","timestamp":1273009851000},"page":"1053-1068","source":"Crossref","is-referenced-by-count":140,"title":["Planning under Uncertainty for Robotic Tasks with Mixed Observability"],"prefix":"10.1177","volume":"29","author":[{"given":"Sylvie C. W.","family":"Ong","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore 117417, Singapore,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Shao Wei Png","sequence":"additional","affiliation":[{"name":"School of Computer Science, McGill University, Montreal, Quebec H3A 2A7, Canada,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore 117417, Singapore,"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Wee Sun Lee","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Singapore, Singapore 117417, Singapore,"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2010,5,4]]},"reference":[{"key":"atypb1","volume-title":"Exact Solutions to Time-dependent MDPs (Advances in Neural Information Processing Systems (NIPS), Vol. 13)","author":"Boyan, J.A.","year":"2001"},{"key":"atypb2","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2006.05.007"},{"key":"atypb3","volume-title":"Proceedings of Uncertainty in Artificial Intelligence","author":"Guestrin, C."},{"key":"atypb4","volume-title":"International Joint Conference on Artificial Intelligence","author":"Guestrin, C."},{"key":"atypb5","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1000"},{"key":"atypb6","volume-title":"Proceedings of the International Conference on AI Planning Systems","author":"Hansen, E.A."},{"key":"atypb7","doi-asserted-by":"publisher","DOI":"10.1613\/jair.678"},{"key":"atypb8","volume-title":"Proceedings of the International Workshop on Principles of Diagnosis","author":"Hauskrecht, M."},{"key":"atypb9","volume-title":"Proceedings of the International Conference on Vision Systems","author":"Hoey, J."},{"key":"atypb10","volume-title":"Proceedings IEEE International Conference on Robotics and Automation","author":"Hsiao, K."},{"key":"atypb11","volume-title":"What Makes Some POMDP Problems Easy to Approximate? (Advances in Neural Information Processing Systems (NIPS), Vol. 20)","author":"Hsu, D.","year":"2008"},{"key":"atypb12","volume-title":"Proceedings IEEE International Conference on Robotics and Automation","author":"Hsu, D."},{"key":"atypb13","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(98)00023-X"},{"key":"atypb14","volume-title":"Proceedings of Robotics: Science and Systems","author":"Kurniawati, H."},{"key":"atypb15","doi-asserted-by":"publisher","DOI":"10.1287\/moor.12.3.441"},{"key":"atypb16","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Pineau, J."},{"key":"atypb17","doi-asserted-by":"publisher","DOI":"10.1016\/S0921-8890(02)00381-0"},{"key":"atypb18","unstructured":"Poupart, P. and Boutilier, C. ( 2003) Value-directed Compression of POMDPs (Advances in Neural Information Processing Systems (NIPS), Vol. 15). Cambridge, MA, The MIT Press, pp. 1547-1554."},{"key":"atypb19","doi-asserted-by":"publisher","DOI":"10.1613\/jair.1496"},{"key":"atypb20","unstructured":"Roy, N. and Thrun, S. ( 2000) Coastal Navigation with Mobile Robots. 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