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Stochastic programming
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121	Approximate Dynamic Programming via Iterated Bellman Inequalities Yang Wang and Stephen Boyd Information Systems Laboratory, Electrical Engineering, Stanford University Stochastic Control Add to Reading List Source URL: forum.stanford.edu Language: English - Date: 2010-05-03 18:13:35 Equations Operations research Dynamic programming Cybernetics Optimal control Bellman equation Control theory Mathematical optimization Systems theory
122	Journal of Artificial Intelligence Research Submitted 3/13; publishedA Survey of Multi-Objective Sequential Decision-Making Diederik M. Roijers Add to Reading List Source URL: jair.org Language: English - Date: 2013-10-18 15:20:49 Systems theory Mathematical optimization Operations research Equations Stochastic control Reinforcement learning Markov decision process Bellman equation Policy Statistics Control theory Dynamic programming
123	Monte Carlo Value Iteration with Macro-Actions Zhan Wei Lim David Hsu Wee Sun Lee Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2011-11-09 00:33:14 Stochastic control Control theory Partially observable Markov decision process Automated planning and scheduling Markov decision process Reinforcement learning Macro Algorithm Statistics Dynamic programming Markov processes
124	Tree-Structured Statistical Modeling via Convex Optimization James Saunderson, Venkat Chandrasekaran, Pablo A. Parrilo and Alan S. Willsky Abstract— We develop a semidefinite-programming-based approach to stochastic mo Add to Reading List Source URL: www.mit.edu Language: English - Date: 2012-07-29 14:44:51 Covariance and correlation Matrices Multivariate statistics Linear algebra Data analysis Semidefinite programming Matrix Covariance Positive-definite matrix Algebra Mathematics Statistics
125	DESPOT: Online POMDP Planning with Regularization Adhiraj Somani Nan Ye David Hsu Wee Sun Lee Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2014-05-28 05:12:40 Stochastic control Control theory Partially observable Markov decision process R-tree B-tree Tree traversal Markov decision process Statistics Dynamic programming Markov processes
126	Unmanned Aircraft Collision Avoidance using Continuous-State POMDPs Haoyu Bai∗ David Hsu∗ Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2011-05-30 06:03:31 Markov processes Stochastic control Systems theory Partially observable Markov decision process Monte Carlo POMDP Markov decision process Automated planning and scheduling Collision avoidance Traffic collision avoidance system Statistics Control theory Dynamic programming
127	Automatic Synthesis of Rules for Planning in Belief Space Leslie Pack Kaelbling MIT CSAIL Cambridge, MAEmail: Add to Reading List Source URL: faculty.cua.edu Language: English - Date: 2015-05-26 14:10:44 Stochastic control Control theory Logic in computer science Partially observable Markov decision process Fluent Probability Markov decision process Statistics Dynamic programming Markov processes
128	IN Proc. Robotics: Science & Systems, 2008 SARSOP: Efficient Point-Based POMDP Planning by Approximating Optimally Reachable Belief Spaces Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2008-07-11 05:03:32 Stochastic control Theoretical computer science Partially observable Markov decision process Motion planning Markov decision process Algorithm Symbol Normal distribution Statistics Dynamic programming Markov processes
129	Principles of AI Problem Solving Tutorial IJCAI-05 Adnan Darwiche (UCLA, Los Angeles) Rina Dechter (UCI, Irvine) H. Geffner (UPF, Barcelona) Add to Reading List Source URL: www.ics.uci.edu Language: English - Date: 2008-09-08 14:27:27 Dynamic programming Markov processes Stochastic control Constraint programming Constraint satisfaction problem Actor model Problem solving Constraint satisfaction STRIPS Statistics Artificial intelligence Automated planning and scheduling

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