Spatial and Temporal Abstractions in POMDPs Applied to Robot Navigation
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Leslie Pack Kaelbling | Sridhar Mahadevan | Georgios Theocharous | S. Mahadevan | L. Kaelbling | Georgios Theocharous
[1] Matthew Brand,et al. Structure Learning in Conditional Probability Models via an Entropic Prior and Parameter Extinction , 1999, Neural Computation.
[2] Richard Fikes,et al. On-line learning of predictive compositional hierarchies , 2002 .
[3] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[4] Abhijit Gosavi,et al. Self-Improving Factory Simulation using Continuous-time Average-Reward Reinforcement Learning , 2007 .
[5] Andrew Y. Ng,et al. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping , 1999, ICML.
[6] Dimitri P. Bertsekas,et al. Dynamic Programming: Deterministic and Stochastic Models , 1987 .
[7] Kevin P. Murphy,et al. Linear-time inference in Hierarchical HMMs , 2001, NIPS.
[8] Stephen E. Levinson,et al. Continuously variable duration hidden Markov models for automatic speech recognition , 1986 .
[9] Andreas Stolcke,et al. Hidden Markov Model} Induction by Bayesian Model Merging , 1992, NIPS.
[10] William S. Lovejoy,et al. Computationally Feasible Bounds for Partially Observed Markov Decision Processes , 1991, Oper. Res..
[11] Sridhar Mahadevan,et al. Learning Hierarchical Partially Observable Markov Decision Process Models for Robot Navigation , 2001 .
[12] Ben J. A. Kröse,et al. Learning from delayed rewards , 1995, Robotics Auton. Syst..
[13] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.
[14] Leslie Pack Kaelbling,et al. Learning models for robot navigation , 1999 .
[15] Monson H. Hayes,et al. An embedded HMM-based approach for face detection and recognition , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[16] Eric A. Hansen,et al. An Improved Grid-Based Approximation Algorithm for POMDPs , 2001, IJCAI.
[18] Svetha Venkatesh,et al. On the Recognition of Abstract Markov Policies , 2000, AAAI/IAAI.
[19] Roger K. Moore. Computer Speech and Language , 1986 .
[20] Maja J. Matarić,et al. A Distributed Model for Mobile Robot Environment-Learning and Navigation , 1990 .
[21] Earl D. Sacerdoti,et al. Planning in a Hierarchy of Abstraction Spaces , 1974, IJCAI.
[22] John J. Leonard,et al. Consistent, Convergent, and Constant-Time SLAM , 2003, IJCAI.
[23] Vladimir Solmon,et al. The estimation of stochastic context-free grammars using the Inside-Outside algorithm , 2003 .
[24] Hugh F. Durrant-Whyte,et al. Simultaneous Localization and Mapping with Sparse Extended Information Filters , 2004, Int. J. Robotics Res..
[25] Mari Ostendorf,et al. From HMM's to segment models: a unified view of stochastic modeling for speech recognition , 1996, IEEE Trans. Speech Audio Process..
[26] Detlef Prescher,et al. Inside-Outside Estimation Meets Dynamic EM , 2004, IWPT.
[27] Earl D. Sacerdoti,et al. The Nonlinear Nature of Plans , 1975, IJCAI.
[28] Sebastian Thrun,et al. Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..
[29] Peter Norvig,et al. Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.
[30] Ronald E. Parr,et al. Hierarchical control and learning for markov decision processes , 1998 .
[31] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[32] Leslie Pack Kaelbling,et al. Representing hierarchical POMDPs as DBNs for multi-scale robot localization , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.
[33] Carl de Marcken,et al. Unsupervised language acquisition , 1996, ArXiv.
[34] Sean R Eddy,et al. What is dynamic programming? , 2004, Nature Biotechnology.
[35] Leslie Pack Kaelbling,et al. Approximate Planning in POMDPs with Macro-Actions , 2003, NIPS.
[36] Thomas G. Dietterich. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..
[37] Doina Precup,et al. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..
[38] Yoram Singer,et al. The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.
[39] Joelle Pineau,et al. Policy-contingent abstraction for robust robot control , 2002, UAI.
[40] Nicholas Roy,et al. Exponential Family PCA for Belief Compression in POMDPs , 2002, NIPS.
[41] Milos Hauskrecht,et al. Value-Function Approximations for Partially Observable Markov Decision Processes , 2000, J. Artif. Intell. Res..
[42] Sridhar Mahadevan,et al. Hierarchical learning and planning in partially observable markov decision processes , 2002 .
[43] Mark A. Paskin,et al. Thin Junction Tree Filters for Simultaneous Localization and Mapping , 2002, IJCAI.
[44] L. R. Rabiner,et al. A probabilistic distance measure for hidden Markov models , 1985, AT&T Technical Journal.
[45] Tom E. Bishop,et al. Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.