- Dan Pelleg, Andrew Moore
**Using Tarjan's Red Rule for Fast Dependency Tree Construction**Neural Information Processing Systems, 2002 (NIPS2002). - Scott Davies and Andrew Moore
**Interpolating Conditional Density Trees**. In proceedings of UAI-2002: The Eighteenth Conference on Uncertainty in Artificial Intelligence. - Weng-Keen Wong, Andrew Moore, Gregory Cooper, and Michael Wagner
**Rule-Based Anomaly Pattern Detection for Detecting Disease Outbreaks**To appear in the Eighteenth National Conference on Artificial Intelligence (AAAI) 2002 - Weng-Keen Wong and Andrew Moore
**Efficient Algorithms for Non-Parametric Clustering with Clutter**Interface, 2002 - Jeremy Kubica, Andrew Moore, Jeff Schneider and Yiming Yang
**Stochastic Link and Group Detection**To appear in the Eighteenth National Conference on Artificial Intelligence (AAAI) 2002 - Andrew Moore and Jeff Schneider
**Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes**To appear in the Conference of Uncertainty in Artificial Intelligence (UAI) 2002 - Malcolm Strens, Andrew Moore
**Direct Policy Search using Paired Statistical Tests**International Conference on Machine Learning, 2001 (ICML2001). - Dan Pelleg, Andrew Moore
**Mixtures of Rectangles: Interpretable Soft Clustering**International Conference on Machine Learning, 2001 (ICML2001). - Peter Sand, Andrew Moore
**Repairing Faulty Mixture Models using Density Estimation**International Conference on Machine Learning, 2001 (ICML2001). -
J. Andrew Bagnell, Jeff Schneider
**Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods**International Conference on Robotics and Automation, 2001. -
Alexander Gray and Andrew Moore,
**'N-Body' Problems in Statistical Learning**, Advances in Neural Information Processing Systems 13 (Submitted May 2000, Proceedings published May 2001). -
Remi Munos and Andrew Moore,
**Rates of Convergence for Variable Resolution Schemes in Optimal Control**, International Conference on Machine Learning, 2000 (ICML2000). -
Paul Komarek and Andrew Moore,
**A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets**, International Conference on Machine Learning, 2000 (ICML2000). PDF version of article -
Geoff Gordon and Andrew Moore,
**Learning Filaments**, International Conference on Machine Learning, 2000 (ICML2000) -
Dan Pelleg and Andrew Moore,
**X-means: Extending K-means with Efficient Estimation of the Number of Clusters**, International Conference on Machine Learning, 2000 (ICML2000) -
Scott Davies and Andrew Moore,
**Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks with Mixed Continuous and Discrete Variables**, In proceedings of UAI-2000: The Sixteenth Conference on Uncertainty in Artificial Intelligence -
Brigham Anderson, Andrew Moore, and David Cohn
**A Nonparametric Approach to Noisy and Costly Optimization**, International Conference on Machine Learning, 2000 (ICML2000) -
Andrew W. Moore et al,
**Cached Sufficient Statistics: What are they? A short white paper.**(PDF Version) -
Andrew W. Moore,
**The Anchors Hierarchy: Using the Triangle Inequality to Survive High Dimensional Data**, In proceedings of UAI-2000: The Sixteenth Conference on Uncertainty in Artificial Intelligence -
Andrew W. Moore, Leemon Baird and Leslie Pack Kaelbling,
**Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs**, International Joint Conference on Artificial Intelligence, 1999 (IJCAI99). - Scott Davies and Andrew Moore.
**Bayesian Networks for Lossless Dataset Compression,**Conference on Knowledge Discovery in Databases 1999, (KDD99) - Remi Munos and Andrew Moore.
**Influence and Variance of a Markov Chain : Application to Adaptive Discretization in Optimal Control,**Conference on Decision and Control 1999, (CDC99) - Dan Pelleg and Andrew Moore.
**Accelerating Exact k-means Algorithms with Geometric Reasoning**Conference on Knowledge Discovery in Databases 1999, (KDD99) - Remi Munos and Andrew Moore.
**Variable resolution discretization for high-accuracy solutions of optimal control problems.**, International Joint Conference on Artificial Intelligence, 1999 (IJCAI99). - Remi Munos, Leemon Baird, and Andrew Moore.
**Gradient Descent Approaches to Neural-Net-Based Solutions of the Hamilton-Jacobi-Bellman Equation.**IJCNN99. - Jeff Schneider, Weng-Keen Wong, Andrew Moore, Martin Riedmiller
**Distributed Value Functions**, International Conference on Machine Learning, 1999 -
Andrew W. Moore,
**Very Fast EM-based Mixture Model Clustering using Multiresolution kd-trees**, Advances in Neural Information Processing Systems 11, (Submitted May 1998, Proceedings published May 1999). -
Leemon C. Baird and Andrew W. Moore
**Gradient descent for general reinforcement learning**, Advances in Neural Information Processing Systems 11, May 1999. -
Remi Munos and Andrew W. Moore,
**Barycentric Interpolators for Continuous Space and Time Reinforcement Learning**, Advances in Neural Information Processing Systems 11, (Submitted May 1998, Proceedings published May 1999). -
Andrew W. Moore and Jeff Schneider and Justin Boyan and
Mary Soon Lee,
**Q2: Memory-based active learning for optimizing noisy continuous functions**, To be presented at the International Conference of Machine Learning, Madison, June/July 1998. (Zipped Version) -
Andrew W. Moore and Mary Soon Lee,
**Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets**, (This paper introduces AD-Trees). (*In Volume 8 of Journal of Artificial Intelligence Research*). Revised version of CMU Robotics Institute Technical Report CMU-RI-TR-97-27, July 1997, (24 pages) -
Brigham S. Anderson and Andrew W. Moore
**ADtrees for Fast Counting and for Fast Learning of Association Rules**, To Appear in KDD98 (Knowledge Discovery from Databases, New York, August 1998) -
Boyan, J. A. and A. W. Moore. "Learning Evaluation Functions for Global
Optimization and Boolean Satisfiability." Fifteenth National Conference
on Artificial Intelligence (AAAI), 1998 (to appear)
**(Outstanding Paper Award)**. postscript (8 pages, 420K) -
Jeff Schneider and Justin Boyan and Andrew W. Moore,
**Value Function Based Production Scheduling**, To be presented at the International Conference of Machine Learning, Madison, June/July 1998 -
S. Davies and A. Y. Ng and A. W. Moore,
**Applying Online Search Techniques to Reinforcement Learning**Fifteenth National Conference on Artificial Intelligence (AAAI), 1998 (to appear). -
A. W. Moore,
**An introductory tutorial on kd-trees***Extract from A. W. Moore's Phd. thesis: Efficient Memory-based Learning for Robot Control, Computer Laboratory, University of Cambridge, Technical Report No. 209, 1991.* -
A. W. Moore and J. Schneider and K. Deng,
**Efficient Locally Weighted Polynomial Regression Predictions**, Proceedings of the 1997 International Machine Learning Conference, Morgan Kaufmann Publishers. -
C. G. Atkeson, S. A. Schaal and Andrew W, Moore,
**Locally Weighted Learning**, AI Review,*Volume 11, Pages 11-73 (Kluwer Publishers)*1997 -
Andrew W, Moore, C. G. Atkeson, S. A. Schaal,
**Locally Weighted Learning For Control**, AI Review,*Volume 11, Pages 75-113 (Kluwer Publishers)*1997 -
S. Davies,
**Multidimensional Interpolation and Triangulation for Reinforcement Learning**, NIPS-96, 1996 (8 pages) -
Jeff G. Schneider,
**Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning**, Neural Information Processing Systems 9, 1996 -
Kaelbling, L.P., Littman, M.L., and Moore, A.W. (1996)
**Reinforcement Learning: A Survey**, Journal of Artificial Intelligence Research Volume 4, pages 237-285. PostScript article version -
Boyan, J. A. and A. W. Moore. "Learning Evaluation Functions for Large
Acyclic Domains." In L. Saitta (ed.),
*Machine Learning: Proceedings of the Thirteenth International Conference.*Morgan Kaufmann, 1996. postscript (8 pages, 147K) -
A. W. Moore and J. Schneider,
**Memory-based Stochastic Optimization**, NIPS-95, 1995 (8 pages) -
A. W. Moore and C. G. Atkeson,
**The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces**, Machine Learning,*Volume 21,*December 1995 (36 pages) -
K. Deng and A. W. Moore,
**Multiresolution Instance-Based Learning**, Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1995 (7 pages) -
A. W. Moore, C. G. Atkeson, and S. Schaal,
**Memory-based Learning for Control**, CMU Robotics Institute Technical Report CMU-RI-TR-95-18, April 1995 (39 pages) -
Mary Soon Lee and Andrew Moore,
**Learning Automated Product Recommendations Without Observable Features: An Initial Investigation**, CMU Robotics Institute Technical Report CMU-RI-TR-95-17, April 1995 (35 pages) -
Justin Boyan and Andrew Moore,
**Generalization in Reinforcement Learning: Safely Approximating the Value Function**, Proceedings of Neural Information Processings Systems 7, Morgan Kaufmann, January 1995 (8 pages) -
A. W. Moore and M. S. Lee,
**Efficient Algorithms for Minimizing Cross Validation Error**, Proceedings of the 11th International Conference on Machine Learning, Morgan Kaufmann, 1994 (9 pages) -
A. W. Moore,
**Variable Resolution Reinforcement Learning**, Proceedings of the Eighth Yale Workshop on Adaptive and Learning Systems, 1994 -
A. W. Moore and C. G. Atkeson,
**Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time**, Machine Learning, Volume 13, October 1993 -
A. W. Moore,
**The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces**, Advances in Neural Information Processing Systems 6, Morgan Kaufmann, 1993 -
O. Maron and A. W. Moore,
**Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation**, Advances in Neural Information Processing Systems 6, Morgan Kaufmann, 1993 (8 pages) - A. W. Moore and C. G. Atkeson,
**Memory-based Reinforcement Learning: Converging with Less Data andLess Real Time**, Robot Learning, editors J. Connell and S. Mahadevan,Kluwer Academic Publishers, 1993 -
A. W. Moore, D. J. Hill, and M. P
. Johnson,
**An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators**, Computational Learning Theory and Natural Learning Systems, Volume 3, editors S. Hanson, S. Judd, and T. Petsche, MIT Press, 1994 (20 pages) -
A. W. Moore and C. G. Atkeson,
**Memory-based Reinforcement Learning: Efficient Computation with Prioritized Sweeping**, Advances in Neural Information Processing Systems 5, editors S. J. Hanson, J. D Cowan, and C. L. Giles, Morgan Kaufmann, 1992 -
A. W. Moore,
**Fast, Robust Adaptive Control by Learning only Forward Models**, Advances in Neural Information Processing Systems 4, editors J. E. Moody, S. J. Hanson, and R. P. Lippman, Morgan Kaufmann, 1991 -
A. W. Moore,
**Knowledge of Knowledge and Intelligent Experimentation for Learning Control**, Proceedings of the 1991 Seattle International Joint Conference on Neural Networks, July 1991 -
A. W. Moore,
**Variable Resolution Dynamic Programming: Efficiently Learning Action Maps in Multivariate Real-valued State-spaces**, Proceedings of the Eighth International Conference on Machine Learning, editors L. Birnbaum and G. Collins, Morgan Kaufman, June 1991 -
A. W. Moore,
**Efficient Memory-based Learning for Robot Control**, PhD. Thesis: University of Cambridge, Computer Science, Technical Report 209, March 1991 -
A. W. Moore,
**Acquisition of Dynamic Control Knowledge for a Robotic Manipulator**, Proceedings of the 7th International Conference on Machine Learning, Morgan Kaufman, June 1990 -
W. F. Clocksin and A. W. Moore
**Some Experiments in Adaptive State Space Robotics**, Proceedings of the 7th AISB Conference, Brighton, Morgan Kaufman, April 1989

Andrew Moore's 1991 PhD Thesis: **Efficient Memory Based Robot
Learning** (Technical Report 209, University of Cambridge).
Part 1,
Part 2,
Part 3,
Part 4