Learning Situation-Dependent Rules: Improving Task Planning for an Incompletely Modelled Domain
暂无分享,去创建一个
[1] Karen Zita Haigh,et al. Interleaving Planning and Robot Execution for Asynchronous User Requests , 1998, Auton. Robots.
[2] Douglas J. Pearson. Learning Procedural Planning Knowledge in Complex Environments , 1996, AAAI/IAAI, Vol. 2.
[3] Andrew W. Moore,et al. Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..
[4] K. Haigh,et al. A Layered Architecture for Ooce Delivery Robots , 1997 .
[5] Andrew W. Moore,et al. Generalization in Reinforcement Learning: Safely Approximating the Value Function , 1994, NIPS.
[6] Eugene Fink,et al. Integrating planning and learning: the PRODIGY architecture , 1995, J. Exp. Theor. Artif. Intell..
[7] Karen Zita Haigh,et al. A Layered Architecture for O ce Delivery Robots , 1997 .
[8] Karen Zita Haigh,et al. Situation-dependent learning for interleaved planning and robot execution , 1998 .
[9] Karen Zita Haigh,et al. A layered architecture for office delivery robots , 1997, AGENTS '97.
[10] Andrew McCallum,et al. Reinforcement learning with selective perception and hidden state , 1996 .
[11] Karen Zita Haigh,et al. Learning situation-dependent costs: improving planning from probabilistic robot execution , 1998, AGENTS '98.
[12] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[13] Xuemei Wang,et al. Learning Planning Operators by Observation and Practice , 1994, AIPS.
[14] Wei-Min Shen,et al. Autonomous learning from the environment , 1994 .
[15] Cristina Baroglio,et al. Learning Controllers for Industrial Robots , 2005, Machine Learning.
[16] Ming Tan,et al. Cost-sensitive robot learning , 1991 .
[17] Dean A. Pomerleau,et al. Neural Network Perception for Mobile Robot Guidance , 1993 .
[18] R. R. Murphy,et al. Learning the expected utility of sensors and algorithms , 1994, Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems.
[19] Leemon C. Baird,et al. Residual Algorithms: Reinforcement Learning with Function Approximation , 1995, ICML.