Journal articles on the topic 'POMDPs'
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Zhang, N. L., and W. Liu. "A Model Approximation Scheme for Planning in Partially Observable Stochastic Domains." Journal of Artificial Intelligence Research 7 (November 1, 1997): 199–230. http://dx.doi.org/10.1613/jair.419.
Full textAras, R., and A. Dutech. "An Investigation into Mathematical Programming for Finite Horizon Decentralized POMDPs." Journal of Artificial Intelligence Research 37 (March 26, 2010): 329–96. http://dx.doi.org/10.1613/jair.2915.
Full textTennenholtz, Guy, Uri Shalit, and Shie Mannor. "Off-Policy Evaluation in Partially Observable Environments." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 06 (April 3, 2020): 10276–83. http://dx.doi.org/10.1609/aaai.v34i06.6590.
Full textWalraven, Erwin, and Matthijs T. J. Spaan. "Column Generation Algorithms for Constrained POMDPs." Journal of Artificial Intelligence Research 62 (July 17, 2018): 489–533. http://dx.doi.org/10.1613/jair.1.11216.
Full textDoshi, P., and P. J. Gmytrasiewicz. "Monte Carlo Sampling Methods for Approximating Interactive POMDPs." Journal of Artificial Intelligence Research 34 (March 24, 2009): 297–337. http://dx.doi.org/10.1613/jair.2630.
Full textWalraven, Erwin, and Matthijs T. J. Spaan. "Point-Based Value Iteration for Finite-Horizon POMDPs." Journal of Artificial Intelligence Research 65 (July 11, 2019): 307–41. http://dx.doi.org/10.1613/jair.1.11324.
Full textRoss, S., J. Pineau, S. Paquet, and B. Chaib-draa. "Online Planning Algorithms for POMDPs." Journal of Artificial Intelligence Research 32 (July 29, 2008): 663–704. http://dx.doi.org/10.1613/jair.2567.
Full textNI, YAODONG, and ZHI-QIANG LIU. "BOUNDED-PARAMETER PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES: FRAMEWORK AND ALGORITHM." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 21, no. 06 (December 2013): 821–63. http://dx.doi.org/10.1142/s0218488513500396.
Full textOliehoek, F. A., M. T. J. Spaan, and N. Vlassis. "Optimal and Approximate Q-value Functions for Decentralized POMDPs." Journal of Artificial Intelligence Research 32 (May 28, 2008): 289–353. http://dx.doi.org/10.1613/jair.2447.
Full textSpaan, M. T. J., and N. Vlassis. "Perseus: Randomized Point-based Value Iteration for POMDPs." Journal of Artificial Intelligence Research 24 (August 1, 2005): 195–220. http://dx.doi.org/10.1613/jair.1659.
Full textVictorio-Meza, Hermilo, Manuel Mejía-Lavalle, Alicia Martínez Rebollar, Andrés Blanco Ortega, Obdulia Pichardo Lagunas, and Grigori Sidorov. "Searching for Cerebrovascular Disease Optimal Treatment Recommendations Applying Partially Observable Markov Decision Processes." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 01 (October 9, 2017): 1860015. http://dx.doi.org/10.1142/s0218001418600157.
Full textOmidshafiei, Shayegan, Ali–Akbar Agha–Mohammadi, Christopher Amato, Shih–Yuan Liu, Jonathan P. How, and John Vian. "Decentralized control of multi-robot partially observable Markov decision processes using belief space macro-actions." International Journal of Robotics Research 36, no. 2 (February 2017): 231–58. http://dx.doi.org/10.1177/0278364917692864.
Full textDibangoye, Jilles Steeve, Christopher Amato, Olivier Buffet, and François Charpillet. "Optimally Solving Dec-POMDPs as Continuous-State MDPs." Journal of Artificial Intelligence Research 55 (February 24, 2016): 443–97. http://dx.doi.org/10.1613/jair.4623.
Full textZhang, W., and N. L. Zhang. "Restricted Value Iteration: Theory and Algorithms." Journal of Artificial Intelligence Research 23 (February 1, 2005): 123–65. http://dx.doi.org/10.1613/jair.1379.
Full textAmato, Christopher, Daniel S. Bernstein, and Shlomo Zilberstein. "Optimizing fixed-size stochastic controllers for POMDPs and decentralized POMDPs." Autonomous Agents and Multi-Agent Systems 21, no. 3 (August 25, 2009): 293–320. http://dx.doi.org/10.1007/s10458-009-9103-z.
Full textRoy, N., G. Gordon, and S. Thrun. "Finding Approximate POMDP solutions Through Belief Compression." Journal of Artificial Intelligence Research 23 (January 1, 2005): 1–40. http://dx.doi.org/10.1613/jair.1496.
Full textItoh, Hideaki, Hisao Fukumoto, Hiroshi Wakuya, and Tatsuya Furukawa. "Bottom-up learning of hierarchical models in a class of deterministic POMDP environments." International Journal of Applied Mathematics and Computer Science 25, no. 3 (September 1, 2015): 597–615. http://dx.doi.org/10.1515/amcs-2015-0044.
Full textAmato, Christopher, George Konidaris, Leslie P. Kaelbling, and Jonathan P. How. "Modeling and Planning with Macro-Actions in Decentralized POMDPs." Journal of Artificial Intelligence Research 64 (March 25, 2019): 817–59. http://dx.doi.org/10.1613/jair.1.11418.
Full textChatterjee, Krishnendu, and Martin Chmelík. "POMDPs under probabilistic semantics." Artificial Intelligence 221 (April 2015): 46–72. http://dx.doi.org/10.1016/j.artint.2014.12.009.
Full textOliehoek, F. A., M. T. J. Spaan, C. Amato, and S. Whiteson. "Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs." Journal of Artificial Intelligence Research 46 (March 29, 2013): 449–509. http://dx.doi.org/10.1613/jair.3804.
Full textWANG, YI, SHIQI ZHANG, and JOOHYUNG LEE. "Bridging Commonsense Reasoning and Probabilistic Planning via a Probabilistic Action Language." Theory and Practice of Logic Programming 19, no. 5-6 (September 2019): 1090–106. http://dx.doi.org/10.1017/s1471068419000371.
Full textZhang, N. L., and W. Zhang. "Speeding Up the Convergence of Value Iteration in Partially Observable Markov Decision Processes." Journal of Artificial Intelligence Research 14 (February 1, 2001): 29–51. http://dx.doi.org/10.1613/jair.761.
Full textPhan, Thomy, Lenz Belzner, Marie Kiermeier, Markus Friedrich, Kyrill Schmid, and Claudia Linnhoff-Popien. "Memory Bounded Open-Loop Planning in Large POMDPs Using Thompson Sampling." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7941–48. http://dx.doi.org/10.1609/aaai.v33i01.33017941.
Full textZHANG, Zong-Zhang, and Xiao-Ping CHEN. "Hybrid Heuristic Online Planning for POMDPs." Journal of Software 24, no. 7 (January 16, 2014): 1589–600. http://dx.doi.org/10.3724/sp.j.1001.2013.04318.
Full textShani, Guy. "Task-Based Decomposition of Factored POMDPs." IEEE Transactions on Cybernetics 44, no. 2 (February 2014): 208–16. http://dx.doi.org/10.1109/tcyb.2013.2252009.
Full textWilliams, Jason D., and Steve Young. "Scaling POMDPs for Spoken Dialog Management." IEEE Transactions on Audio, Speech and Language Processing 15, no. 7 (September 2007): 2116–29. http://dx.doi.org/10.1109/tasl.2007.902050.
Full textVargo, Erik P., and Randy Cogill. "Expectation-maximization for Bayes-adaptive POMDPs." Journal of the Operational Research Society 66, no. 10 (October 2015): 1605–23. http://dx.doi.org/10.1057/jors.2015.49.
Full textNi, Yaodong, and Zhi-Qiang Liu. "Policy iteration for bounded-parameter POMDPs." Soft Computing 17, no. 4 (September 27, 2012): 537–48. http://dx.doi.org/10.1007/s00500-012-0932-3.
Full textPetric, Frano, Damjan Miklić, and Zdenko Kovačić. "POMDP-Based Coding of Child–Robot Interaction within a Robot-Assisted ASD Diagnostic Protocol." International Journal of Humanoid Robotics 15, no. 02 (April 2018): 1850011. http://dx.doi.org/10.1142/s0219843618500111.
Full textLin, Yong, Xingjia Lu, and Fillia Makedon. "Approximate Planning in POMDPs with Weighted Graph Models." International Journal on Artificial Intelligence Tools 24, no. 04 (August 2015): 1550014. http://dx.doi.org/10.1142/s0218213015500141.
Full textShatkay, H., and L. P. Kaelbling. "Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap." Journal of Artificial Intelligence Research 16 (March 1, 2002): 167–207. http://dx.doi.org/10.1613/jair.874.
Full textYe, Nan, Adhiraj Somani, David Hsu, and Wee Sun Lee. "DESPOT: Online POMDP Planning with Regularization." Journal of Artificial Intelligence Research 58 (January 26, 2017): 231–66. http://dx.doi.org/10.1613/jair.5328.
Full textFrancois-Lavet, Vincent, Guillaume Rabusseau, Joelle Pineau, Damien Ernst, and Raphael Fonteneau. "On Overfitting and Asymptotic Bias in Batch Reinforcement Learning with Partial Observability." Journal of Artificial Intelligence Research 65 (May 5, 2019): 1–30. http://dx.doi.org/10.1613/jair.1.11478.
Full textPineau, J., G. Gordon, and S. Thrun. "Anytime Point-Based Approximations for Large POMDPs." Journal of Artificial Intelligence Research 27 (November 26, 2006): 335–80. http://dx.doi.org/10.1613/jair.2078.
Full textEnlu Zhou, Michael C. Fu, and Steven I. Marcus. "Solving Continuous-State POMDPs via Density Projection." IEEE Transactions on Automatic Control 55, no. 5 (May 2010): 1101–16. http://dx.doi.org/10.1109/tac.2010.2042005.
Full textCapitan, Jesus, Matthijs T. J. Spaan, Luis Merino, and Anibal Ollero. "Decentralized multi-robot cooperation with auctioned POMDPs." International Journal of Robotics Research 32, no. 6 (May 2013): 650–71. http://dx.doi.org/10.1177/0278364913483345.
Full textChatterjee, Krishnendu, Martin Chmelík, Raghav Gupta, and Ayush Kanodia. "Optimal cost almost-sure reachability in POMDPs." Artificial Intelligence 234 (May 2016): 26–48. http://dx.doi.org/10.1016/j.artint.2016.01.007.
Full textXIANG, YANG, and FRANK HANSHAR. "MULTIAGENT EXPEDITION WITH GRAPHICAL MODELS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 19, no. 06 (December 2011): 939–76. http://dx.doi.org/10.1142/s0218488511007416.
Full textHan, Yanlin, and Piotr Gmytrasiewicz. "IPOMDP-Net: A Deep Neural Network for Partially Observable Multi-Agent Planning Using Interactive POMDPs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6062–69. http://dx.doi.org/10.1609/aaai.v33i01.33016062.
Full textHauskrecht, M. "Value-Function Approximations for Partially Observable Markov Decision Processes." Journal of Artificial Intelligence Research 13 (August 1, 2000): 33–94. http://dx.doi.org/10.1613/jair.678.
Full textNair, R., and M. Tambe. "Hybrid BDI-POMDP Framework for Multiagent Teaming." Journal of Artificial Intelligence Research 23 (April 1, 2005): 367–420. http://dx.doi.org/10.1613/jair.1549.
Full textWU, Bo, Min WU, and Jin-Hua SHE. "Point-Based Online Value Iteration Algorithm for POMDPs." Journal of Software 24, no. 1 (January 14, 2014): 25–36. http://dx.doi.org/10.3724/sp.j.1001.2013.04258.
Full textWu, Bo, Yan Peng Feng, and Hong Yan Zheng. "Point-Based Monte Carto Online Planning in POMDPs." Advanced Materials Research 846-847 (November 2013): 1388–91. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1388.
Full textPng, Shaowei, Joelle Pineau, and Brahim Chaib-Draa. "Building Adaptive Dialogue Systems Via Bayes-Adaptive POMDPs." IEEE Journal of Selected Topics in Signal Processing 6, no. 8 (December 2012): 917–27. http://dx.doi.org/10.1109/jstsp.2012.2229962.
Full textBrooks, Alex, Alexei Makarenko, Stefan Williams, and Hugh Durrant-Whyte. "Parametric POMDPs for planning in continuous state spaces." Robotics and Autonomous Systems 54, no. 11 (November 2006): 887–97. http://dx.doi.org/10.1016/j.robot.2006.05.007.
Full textFoka, Amalia, and Panos Trahanias. "Real-time hierarchical POMDPs for autonomous robot navigation." Robotics and Autonomous Systems 55, no. 7 (July 2007): 561–71. http://dx.doi.org/10.1016/j.robot.2007.01.004.
Full textSzer, Daniel, François Charpillet, and Shlomo Zilberstein. "Résolution optimale de DEC-POMDPs par recherche heuristique." Revue d'intelligence artificielle 21, no. 1 (February 15, 2007): 107–28. http://dx.doi.org/10.3166/ria.21.107-128.
Full textDoshi, Prashant, Yifeng Zeng, and Qiongyu Chen. "Graphical models for interactive POMDPs: representations and solutions." Autonomous Agents and Multi-Agent Systems 18, no. 3 (September 25, 2008): 376–416. http://dx.doi.org/10.1007/s10458-008-9064-7.
Full textMaliah, Shlomi, and Guy Shani. "Using POMDPs for learning cost sensitive decision trees." Artificial Intelligence 292 (March 2021): 103400. http://dx.doi.org/10.1016/j.artint.2020.103400.
Full textBaxter, J., P. L. Bartlett, and L. Weaver. "Experiments with Infinite-Horizon, Policy-Gradient Estimation." Journal of Artificial Intelligence Research 15 (November 1, 2001): 351–81. http://dx.doi.org/10.1613/jair.807.
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