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Partially observable Markov decision process
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111	Journal of Machine Learning Research[removed]Submitted 11/10; Published - Deterministic-Probabilistic Models For Partially Observable Reinforcement Learning Problems Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2010-11-25 21:48:41 Stochastic control Partially observable Markov decision process Reinforcement learning Bayesian statistics Markov model Markov decision process Automated planning and scheduling Algorithm Machine learning Statistics Dynamic programming Markov processes
112	Gaussian Belief Space Planning with Discontinuities in Sensing Domains Sachin Patil, Yan Duan, John Schulman, Ken Goldberg, Pieter Abbeel Abstract— Discontinuities in sensing domains are common when planning for many r Add to Reading List Source URL: ieor.berkeley.edu Language: English - Date: 2014-02-16 20:56:29 Robot control Cartography Kalman filter Motion planning Simultaneous localization and mapping Partially observable Markov decision process Laplace transform Robotic mapping Trajectory optimization Mathematical analysis Statistics Control theory
113	G:AcadsAutonialarmgraphsother formatspsdistr.eps Add to Reading List Source URL: www.cs.cmu.edu Language: English - Date: 2005-01-21 17:02:57 Partially observable Markov decision process Stochastic control Bayesian statistics CUSUM Maximum likelihood Prior probability Normal distribution Thresholding Statistics Estimation theory Dynamic programming
114	Fast Online Policy Gradient Learning with SMD Gain Vector Adaptation∗ Nicol N. Schraudolph Jin Yu Douglas Aberdeen Statistical Machine Learning, National ICT Australia, Canberra {nic.schraudolph,douglas.aberdeen}@nicta Add to Reading List Source URL: nic.schraudolph.org Language: English - Date: 2011-07-24 10:20:10 Neural networks Stochastic optimization Dynamic programming Markov processes Stochastic control Partially observable Markov decision process Conjugate gradient method Backpropagation Reinforcement learning Numerical analysis Statistics Mathematics
115	Gaussian Belief Space Planning for Imprecise Articulated Robots Alex Lee Sachin Patil John Schulman Zoe McCarthy Jur van den Berg Ken Goldberg Pieter Abbeel Abstract— For many emerging applications, actuators are being Add to Reading List Source URL: ieor.berkeley.edu Language: English - Date: 2013-04-30 13:17:31 Control theory Motion planning Trajectory optimization Partially observable Markov decision process Kalman filter Collision detection Optimal control Normal distribution Robot Mathematical optimization Statistics Applied mathematics
116	Unifying Perception, Estimation and Action for Mobile Manipulation via Belief Space Planning Leslie Pack Kaelbling and Tom´as Lozano-P´erez Abstract— In this paper, we describe an integrated strategy for planning, pe Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-06-11 20:18:09 Motion planning Theoretical computer science Pose Occupancy grid mapping Robot Partially observable Markov decision process Artificial intelligence Fluent Causality Computer vision Statistics Applied mathematics
117	Robust Belief-Based Execution of Manipulation Programs Kaijen Hsiao, Tom´ as Lozano-P´erez, and Leslie Pack Kaelbling Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, {kjh Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2008-12-03 20:20:17 Stochastic control Dynamical system Motion planning Robot Kinematics Normal distribution Mobile robot Statistics Dynamic programming Partially observable Markov decision process
118	Artificial Intelligence[removed]–134 Planning and acting in partially observable stochastic domains Leslie Pack Kaelbling a,∗,1,2 , Michael L. Littman b,3 , Anthony R. Cassandra c,1 Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2004-07-01 07:47:55 Stochastic control Markov models Partially observable Markov decision process Markov decision process Reinforcement learning Automated planning and scheduling Markov chain Kalman filter Statistics Markov processes Dynamic programming
119	Approximate Planning in POMDPs with Macro-Actions Georgios Theocharous MIT AI Lab 200 Technology Square Cambridge, MA 02139 Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2004-07-01 07:47:54 Stochastic control Markov models Partially observable Markov decision process Models of computation Markov decision process Automated planning and scheduling Reinforcement learning Algorithm Q-learning Statistics Markov processes Dynamic programming
120	Collision Avoidance for Unmanned Aircraft using Markov Decision Processes∗ Selim Temizer†, Mykel J. Kochenderfer‡, Leslie P. Kaelbling§, Tom´as Lozano-P´erez¶, and James K. Kuchark Before unmanned aircraft can Add to Reading List Source URL: people.csail.mit.edu Language: English - Date: 2012-06-11 20:16:46 Warning systems Dynamic programming Stochastic control Safety Control theory Traffic collision avoidance system Collision avoidance Partially observable Markov decision process Airborne collision avoidance system Statistics Avionics Aircraft collision avoidance systems

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