Approximate inference for planning in stochastic relational worlds
暂无分享,去创建一个
[1] L. P. Kaelbling,et al. Learning Symbolic Models of Stochastic Domains , 2007, J. Artif. Intell. Res..
[2] Kevin P. Murphy,et al. The Factored Frontier Algorithm for Approximate Inference in DBNs , 2001, UAI.
[3] Leslie Pack Kaelbling,et al. Action-Space Partitioning for Planning , 2007, AAAI.
[4] Marc Toussaint,et al. Probabilistic inference for solving discrete and continuous state Markov Decision Processes , 2006, ICML.
[5] Ben Taskar,et al. Introduction to statistical relational learning , 2007 .
[6] Ben Taskar,et al. Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) , 2007 .
[7] Matthew Botvinick,et al. Goal-directed decision making in prefrontal cortex: a computational framework , 2008, NIPS.
[8] Scott Sanner,et al. Approximate Solution Techniques for Factored First-Order MDPs , 2007, ICAPS.
[9] Luc De Raedt,et al. Bellman goes relational , 2004, ICML.
[10] Yishay Mansour,et al. A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes , 1999, Machine Learning.
[11] Geoffrey E. Hinton,et al. Using Expectation-Maximization for Reinforcement Learning , 1997, Neural Computation.
[12] Pedro M. Domingos,et al. Relational Dynamic Bayesian Networks , 2005, J. Artif. Intell. Res..
[13] Maurice Bruynooghe,et al. Online Learning and Exploiting Relational Models in Reinforcement Learning , 2007, IJCAI.
[14] Martijn van Otterlo,et al. The Logic of Adaptive Behavior - Knowledge Representation and Algorithms for Adaptive Sequential Decision Making under Uncertainty in First-Order and Relational Domains , 2009, Frontiers in Artificial Intelligence and Applications.