Locally Weighted Learning for Control

Artificial Intelligence Review

Volumn 11, Issue 1-5, 1997, Pages 75-113

  Atkeson, Christopher G ^a,c   Moore, Andrew W ^b   Schaal, Stefan ^a,c  

^a Georgia Institute of Technology   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c ADVANCED TELECOMMUNICATIONS RESEARCH INSTITUTE INTERNATIONAL   (Japan)

Author keywords

Dynamic programming; Forward models; Inverse models; Lazy learning; Least commitment learning; Linear quadratic regulation (LQR); Locally weighted regression; LOESS; LWR; Memory based learning; Shifting setpoint algorithm

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; COMPUTER SIMULATION; DATA STORAGE EQUIPMENT; DYNAMIC PROGRAMMING; INTELLIGENT CONTROL; LARGE SCALE SYSTEMS; LEARNING ALGORITHMS; LINEAR CONTROL SYSTEMS; REGRESSION ANALYSIS;

LAZY LEARNING; LEAST COMMITMENT LEARNING; LINEAR QUADRATIC REGULATION (LQR); LOCALLY WEIGHTED LEARNING; LOCALLY WEIGHTED REGRESSION; MEMORY BASED LEARNING; SHIFTING SETPOINT ALGORITHM;

LEARNING SYSTEMS;

EID: 0031073475     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/978-94-017-2053-3_3     Document Type: Article

References (53)

1. Aha, D. W. & Salzberg, S. L. (1993). Learning to catch: Applying nearest neighbor algorithms to dynamic control tasks. In Proceedings of the Fourth International Workshop on Artificial Intelligence and Statistics, pp. 363-368, Ft. Lauderdale, FL.
2. Albus, J. S. (1981). Brains, Behaviour and Robotics. BYTE Books, McGraw-Hill.
3. Atkeson, C. G. (1990). Using local models to control movement. In Touretzky, D. S. (ed.), Advances in Neural Information Processing Systems 2, pp. 316-323. Morgan Kaufmann, San Mateo, CA.
4. Atkeson, C. G. (1994). Using local trajectory optimizers to speed up global optimization in dynamic programming. In Hanson, S. J., Cowan, J. D. & Giles, C. L. (eds.), Advances in Neural Information Processing Systems 6, pp. 663-670. Morgan Kaufmann, San Mateo, CA.
5. Atkeson, C. G., Moore, A. W. & Schaal, S. (1997). Locally weighted learning. Artificial Intelligence Review, this issue.
6. Barto, A. G., Sutton, R. S. & Watkins, C. J. C. H. (1990). Learning and Sequential Decision Making. In Gabriel, M. & Moore, J. W. (eds.), Learning and Computational Neuroscience, pp. 539-602. MIT Press, Cambridge, MA.
7. Barto, A. G., Bradtke, S. J. & Singh, S. P. (1995). Learning to act using real-time dynamic programming. Artificial Intelligence 72(1): 81-138.
8. Bellman, R. E. (1957). Dynamic Programming. Princeton University Press, Princeton, NJ.
9. Bertsekas, D. P. & Tsitsiklis, J. N. (1989). Parallel and Distributed Computation. Prentice Hall.
10. Cannon, R. H. (1967). Dynamics of Physical Systems. McGraw-Hill.
11. Cohn, D. A., Ghahramani, Z. & Jordan, M. I. (1995). Active learning with statistical models. In Tesauro, G., Touretzky, D. & Leen, T. (eds.), Advances in Neural Information Processing Systems 7. MIT Press.
12. Connell, M. E. & Utgoff, P. E. (1987). Learning to control a dynamic physical system. In Sixth National Conference on Artificial Intelligence, pp. 456-460, Seattle, WA. Morgan Kaufmann, San Mateo, CA.
13. Conte, S. D. & De Boor, C. (1980). Elementary Numerical Analysis, McGraw Hill.
14. Deng, K. & Moore, A. W. (1995). Multiresolution Instance-based Learning. In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1233-1239. Morgan Kaufmann.
15. Friedman, J. H. & Stuetzle, W. (1981). Projection Pursuit Regression. Journal of the American Statistical Association, 76(376): 817-823.
16. Grosse, E. (1989). LOESS: Multivariate Smoothing by Moving Least Squares. In C. K. Chul, L. L. S. & Ward, J. D. (eds.), Approximation Theory VI. Academic Press.
17. Hastie, T. & Loader, C. (1993). Local regression: Automatic kernel carpentry. Statistical Science 8(2): 120-143.
18. Jordan, M. I. & Jacobs, R. A. (1990). Learning to control an unstable system with forward modeling. In Touretzky, D. (ed.), Advances in Neural Information Processing Systems 2, pp. 324-331. Morgan Kaufmann, San Mateo, CA.
19. Jordan, M. I. & Rumelhart, D. E. (1992). Forward Models: Supervised Learning with a Distal Teacher. Cognitive Science 16: 307-354.
20. Kaelbling, L. P. (1993). Learning in Embedded Systems. MIT Press, Cambridge, MA.
21. Kuperstein, M. (1988). Neural Model of Adaptive Hand-Eye Coordination for Single Postures. Science 239: 1308-3111.
22. MacKay, D. J. C. (1992). Bayesian Model Comparison and Backprop Nets. In Moody, J. E., Hanson, S. J. & Lippman, R. P. (eds.), Advances in Neural Information Processing Systems 4, pp. 839-846. Morgan Kaufmann, San Mateo, CA.
23. Mahadevan, S. (1992). Enhancing Transfer in Reinforcement Learning by Building Stochastic Models of Robot Actions. In Machine Learning: Proceedings of the Ninth International Conference, pp. 290-299. Morgan Kaufmann.
24. Maron, O. & Moore, A. (1994). Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation. In Advances in Neural Information Processing Systems 6, pp. 59-66. Morgan Kaufmann, San Mateo, CA.
25. McCallum, R. A. (1995). Instance-based utile distinctions for reinforcement learning with hidden state. In Prieditis and Russell (1995), pp. 387-395.
26. Mel, B. W. (1989). MURPHY: A Connectionist Approach to Vision-Based Robot Motion Planning. Technical Report CCSR-89-17A, University of Illinois at Urbana-Champaign.
27. Miller, W. T. (1989). Real-Time Application of Neural Networks for Sensor-Based Control of Robots with Vision. IEEE Transactions on Systems, Man and Cybernetics 19(4): 825-831.
28. Moore, A. W. (1990). Acquisition of Dynamic Control Knowledge for a Robotic Manipulator. In Proceedings of the 7th International Conference on Machine Learning, pp. 244-252. Morgan Kaufmann.
29. Moore, A. W. (1991a). Knowledge of Knowledge and Intelligent Experimentation for Learning Control. In Proceedings of the 1991 Seattle International Joint Conference on Neural Networks.
30. Moore, A. W. (1991b). Variable Resolution Dynamic Programming: Efficiently Learning Action Maps in Multivariate Real-valued State-spaces. In Birnbaum, L. & Collins, G. (eds.), Machine Learning: Proceedings of the Eighth International Workshop, pp. 333-337. Morgan Kaufmann.
31. Moore, A. W. (1992). Fast, Robust Adaptive Control by Learning only Forward Models. In Moody, J. E., Hanson, S. J. & Lippman, R. P. (eds.), Advances in Neural Information Processing Systems 4, pp. 571-578. Morgan Kaufmann, San Mateo, CA.
32. Moore, A. W. & Atkeson, C. G. (1993). Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time. Machine Learning 13: 103-130.
33. Moore, A. W., Hill, D. J. & Johnson, M. P. (1992). An Empirical Investigation of Brute Force to Choose Features, Smoothers and Function Approximators. In Hanson, S., Judd, S. & Petsche, T. (eds.), Computational Learning Theory and Natural Learning Systems, Volume 3. MIT Press.
34. Moore, A. W. & Lee, M. S. (1994). Efficient Algorithms for Minimizing Cross Validation Error. In Proceedings of the 11th International Conference on Machine Learning, pp. 190-198. Morgan Kaufmann.
35. Moore, A. W. & Schneider, J. (1995). Memory-Based Stochastic Optimization. In Proceedings of Neural Information Processing Systems Conference.
36. Omohundro, S. M. (1987). Efficient Algorithms with Neural Network Behaviour. Journal of Complex Systems 1(2): 273-347.
37. Omohundro, S. M. (1991). Bumptrees for Efficient Function, Constraint, and Classification Learning. In Lippmann, R. P., Moody, J. E. & Touretzky, D. S. (eds.), Advances in Neural Information Processing Systems 3, pp. 693-699. Morgan Kaufmann, San Mateo, CA.
38. Ortega, J. M. & Rheinboldt, W. C. (1970). Iterative Solution of Nonlinear Equations in Several Variables. Academic Press.
39. Peng, J. (1995). Efficient memory-based dynamic programming. In Prieditis and Russell (1995), pp. 438-446.
40. Peng, J. & Williams, R. J. (1993). Efficient Learning and Planning Within the Dyna Framework. In Proceedings of the Second International Conference on Simulation of Adaptive Behavior. MIT Press.
41. Pomerleau, D. (1994). Reliability estimation for neural network based autonomous driving. Robotics and Autonomous Systems, 12.
42. Preparata, F. P. & Shamos, M. (1985). Computational Geometry. Springer-Verlag.
43. Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. (1988). Numerical Recipes in C. Cambridge University Press, New York, NY.
44. Prieditis, A. & Russell, S. (eds.) (1995). Twelfth International Conference on Machine Learning, Tahoe City, CA. Morgan Kaufmann, San Mateo, CA.
45. Quinlan, J. R. (1993). Combining Instance-Based and Model-Based Learning. In Machine Learning: Proceedings of the Tenth International Conference, pp. 236-243. Morgan Kaufmann.
46. Schaal, S. & Atkeson, C. (1994a). Robot Juggling: An Implementation of Memory-based Learning. Control Systems Magazine 14(1): 57-71.
47. Schaal, S. & Atkeson, C. G. (1994b). Assessing the Quality of Local Linear Models. In Cowan, J. D., Tesauro, G. & Alspector, J. (eds.), Advances in Neural Information Processing Systems 6, pp. 160-167. Morgan Kaufmann.
48. Stanfill, C. & Waltz, D. (1986). Towards Memory-Based Reasoning. Communications of the ACM 29(12): 1213-1228.
49. Stengel, R. F. (1986). Stochastic Optimal Control. John Wiley and Sons.
50. Sutton, R. S. (1988). Learning to Predict by the Methods of Temporal Differences. Machine Learning 3: 9-44.
51. Sutton, R. S. (1990). Integrated Architecture for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. In Proceedings of the 7th International Conference on Machine Learning, pp. 216-224. Morgan Kaufmann.
52. Watkins, C. J. C. H. (1989). Learning from Delayed Rewards. PhD. Thesis, King's College, University of Cambridge.
53. Zografski, Z. (1992). Geometric and neuromorphic learning for nonlinear modeling, control and forecasting. In Proceedings of the 1992 IEEE International Symposium on Intelligent Control, pp. 158-163. Glasgow, Scotland. IEEE catalog number 92CH3110-4.