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Department of Computer Science
University of Massachusetts Amherst

ALL Publications, 1981-1995 (September)

Following is a list of publications in chronological order. Some of these items appear both as published papers and as technical reports, but the title may have changed. In such cases, to avoid ambiguity, the citation shown in this list is the proper citation. We prefer that you cite the published version over the technical report.

Please contact the lab or a specific author if you have any questions. To obtain copies of specific papers, please send email to mitchell [at] cs [dot] umass [dot] edu

  1. A. G. Barto and R. S. Sutton. Landmark learning: An illustration of associative search. Biological Cybernetics, 42: 1-8, 1981.

  2. A. G. Barto, R. S. Sutton and P. S. Brouwer. Associative search network: A reinforcement learning associative memory. Biological Cybernetics, 40: 201-211, 1981.

  3. S. Bozinovski. Teaching space: A representation concept for adaptive pattern classification. COINS Technical Report 81-18, University of Massachusetts, 1981.

  4. R. S. Sutton and A. G. Barto. An adaptive network that constructs and uses an internal model of its environment. Cognition and Brain Theory, 4: 217-246, 1981.

  5. R. S. Sutton and A. G. Barto. Toward a modern theory of adaptive networks: Expectation and prediction. Psychological Review, 88: 135-171, 1981.

  6. C. W. Anderson. Feature generation and selection by a layered network of reinforcement learning elements: Some initial experiments. COINS Technical Report 82-12, University of Massachusetts, 1982.

  7. A. G. Barto, C. W. Anderson, and R. S. Sutton. Synthesis of nonlinear control surfaces by a layered associative search network. Biological Cybernetics, 43: 175-185, 1982.

  8. A. G. Barto and R. S. Sutton. Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element. Behavioural Brain Research, 4: 221-235, 1982.

  9. S. Bozinovski. A self-learning system using secondary reinforcement. In R. Trappl, editor, Cybernetics and Systems '82, Elsevier Science Publishers (North Holland), 1982.

  10. A. G. Barto and R. S. Sutton. Neural problem solving. COINS Technical Report 83-03, University of Massachusetts, 1983.

  11. A. G. Barto, R. S. Sutton and C. W. Anderson. Neuronlike elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics, 13: 835-846, 1983.

  12. A. G. Barto. Adaptive neural networks for learning control: Some computational experiments. In Proceedings of the IEEE Workshop on Intelligent Control, Rensselaer Polytechnic Institute, Troy, NY, August 1985.

  13. A. G. Barto. Learning by statistical cooperation of self-interested neuron-like computing elements. Human Neurobiology, 4: 229-256, 1985. [pdf]

  14. A. G. Barto and P. Anandan. Pattern recognizing stochastic learning automata. IEEE Transactions on Systems, Man, and Cybernetics, 15: 360-375, 1985.

  15. A. G. Barto and C. W. Anderson. Structural learning in connectionist systems. In Proceedings of the Seventh Annual Conference of the Cognitive Science Society, Irvine, CA, August 1985.

  16. O. Selfridge, R. S. Sutton and A. G. Barto. Training and tracking in robotics. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pp. 670-672. San Mateo, CA: Morgan Kaufmann, 1985.

  17. R. S. Sutton and B. Pinette. The learning of world models by connectionist networks. Proceedings of the Seventh Annual Conference of the Cognitive Science Society, Irvine, CA, 1985.

  18. A. G. Barto. Game-theoretic cooperativity in networks of self-interested units. In J. S. Denker, editor, Neural Networks for Computing, American Institute of Physics, New York, 1986.

  19. A. G. Barto, P. Anandan, and C. W. Anderson. Cooperativity in networks of pattern recognizing stochastic learning automata. In K. S. Narendra, editor, Adaptive and Learning Systems: Theory and Applications, Plenum, New York, 1986.

  20. M. I. Jordan. Attractor dynamics and parallelism in a connectionist sequential machine. Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA, 1986.

  21. J. W. Moore, J. E. Desmond, N. E. Berthier, E. J. Blazis, R. S. Sutton and A. G. Barto. Simulation of the classically conditioned nictitating membrane response by a neuron-like adaptive element. Response topography, neuronal firing, and interstimulus intervals. Behavioural Brain Research, 21: 143-154, 1986.

  22. A. G. Barto. An approach to learning control surfaces by connectionist systems. In M. A. Arbib and A. R. Hanson, editors, Vision, Brain and Cooperative Computation, MIT Press/ Bradford Books, Cambridge, MA, 1987.

  23. A. G. Barto and M. I. Jordan. Gradient following without back-propagation in layered networks. Proceedings of the First IEEE Annual Conference on Neural Networks, San Diego, CA, June 1987, pp. II-629-II-636.

  24. D.E.J. Blazis and J.W. Moore. Simulation of a classically conditioned response: Components of the input trace and a cerebellar neural network implementation of the Sutton-Barto-Desmond model. COINS Technical Report 87-74, University of Massachusetts, 1987.

  25. S. Judd. Learning in networks is hard. Proceedings of the First IEEE Annual Conference on Neural Networks, San Diego, CA, June 1987.

  26. J. S. Judd. Complexity of connectionist learning with various node functions. COINS Technical Report 87-60, University of Massachusetts, 1987.

  27. N. A. Schmajuk and J. W. Moore. Two attentional models of classical conditioning: Variations in CS effectiveness revisited. COINS Technical Report 87-29, University of Massachusetts, 1987.

  28. R. S. Sutton and A. G. Barto. A temporal-difference model of classical conditioning. TR87-509.2, Computer &Intelligent Systems Laboratory, GTE Laboratories Inc., Waltham, MA, 1987. [Also in Proceedings of the Ninth Annual Conference of the Cognitive Science Society, July, 1987.]

  29. J. E. Desmond. Temporally adaptive conditioned responses: Representation of the stimulus trace in neural-network models. COINS Technical Report 88-80, University of Massachusetts, 1988.

  30. V. Gullapalli. A stochastic algorithm for learning real-valued functions via reinforcement feedback. COINS Technical Report 88-91, University of Massachusetts, 1988.

  31. R. A. Jacobs. Increased rates of convergence through learning rate adaptation. Neural Networks, 1: 295-307, 1988.

  32. R. A. Jacobs. Initial experiments on constructing domains of expertise and hierarchies in connectionist systems. Proceedings of the 1988 Connectionist Model Summer School, San Mateo, CA: Morgan Kaufmann, 1988.

  33. M. I. Jordan. Supervised learning and systems with excess degrees of freedom. COINS Technical Report 88-27, University of Massachusetts.

  34. M. I. Jordan and D. A. Rosenbaum. Action. COINS Technical Report 88-26, University of Massachusetts, 1988. [Also to appear in M. I. Posner, editor, Handbook of Cognitive Science, Cambridge, MA: MIT Press.]

  35. S. Judd. On the complexity of loading shallow neural networks. Journal of Complexity, 1988.

  36. R. S. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3: 9-44, 1988.

  37. A. Barto. From chemotaxis to cooperativity: Abstract exercises in neuronal learning strategies. In R. Durbin, C. Miall and G. Mitchison, editors, The Computing Neuron, pp. 73-98. Wokingham, England: Addison-Wesley, 1989. [pdf]

  38. A. G. Barto. Connectionist learning for control. In W. T. Miller, R. S. Sutton and P. J. Werbos, editors, Neural Networks for Control, pp. 5-58. Cambridge, MA: The MIT Press, 1990. [Also appeared as COINS Technical Report 89-89, University of Massachusetts, 1989.]

  39. A. G. Barto and S. P. Singh. Reinforcement learning and dynamic programming. In Proceedings of the Sixth Yale Workshop on Adaptive and Learning Systems, New Haven, CT, August 1990. pp. 83-88.

  40. A. G. Barto and S. P. Singh. On the computational economics of reinforcement learning. In D.S. Touretzky, J.L. Elman, T.J. Sejnowski and G.E. Hinton, editors, Proceedings of the 1990 Connectionist Models Summer School. San Mateo, CA: Morgan Kaufmann, 1990. pp. 35-44.

  41. A. G. Barto, R. S. Sutton and C. J. C. H. Watkins. Learning and sequential decision making. In M. Gabriel and J. Moore, editors, Learning and Computational Neuroscience,The MIT Press, Cambridge, MA, 1990, pp. 539-602. [Also appeared as COINS Technical Report 89-95, University of Massachusetts, Amherst, MA, 1989.]

  42. A. G. Barto, R. S. Sutton and C. J. C. H. Watkins. Sequential decision problems and neural networks. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems 2, pp. 686-693. Morgan Kaufmann Publishers, San Mateo, CA, 1990.

  43. V. Gullapalli. Associative reinforcement learning of real-valued functions. COINS Technical Report 90-129, University of Massachusetts, May 1990.

  44. V. Gullapalli. Modeling cortical area 7a using stochastic real-valued (SRV) units. In D.S. Touretzky, J.L. Elman, T.J. Sejnowski and G.E. Hinton, editors, Proceedings of the 1990 Connectionist Models Summer School, pp. 363-368. San Mateo, CA: Morgan Kaufmann, 1990.

  45. V. Gullapalli. A stochastic reinforcement learning algorithm for learning real-valued functions. Neural Networks, 3: 671-692, 1990.

  46. J. C. Houk, S. P. Singh, C. Fisher and A. G. Barto. An adaptive sensorimotor network inspired by the anatomy and physiology of the cerebellum. In W. T. Miller, R. S. Sutton and P. J. Werbos, editors, Neural Networks for Control, pp. 301-348. Cambridge, MA: MIT Press, 1990. [Also appeared as COINS Technical Report 89-108, University of Massachusetts, 1989.]

  47. J. C. Houk. Cooperative control of limb movements by the motor cortex, brainstem and cerebellum. COINS Technical Report 89-118, University of Massachusetts, 1989. [Also in R.M.J. Cotterill, editor, Models of Brain Function, Cambridge University Press, 1990.]

  48. R. A. Jacobs. Task Decomposition Through Competition in a Modular Connectionist Architecture. (Ph.D. Thesis) COINS Technical Report 90-44, University of Massachusetts at Amherst, May 1990.

  49. R. A. Jacobs, M. I. Jordan and A. G. Barto. Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. Cognitive Science, 15: 219-250, 1991. [Also appeared as COINS Technical Report 90-27, University of Massachusetts at Amherst, March 1990.]

  50. M. I. Jordan and R. A. Jacobs. Learning to control an unstable system with forward modeling. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems 2, pp. 324-331. Morgan Kaufmann Publishers, San Mateo, CA, 1990.

  51. M. C. Mozer and J. Bachrach. Discovering the structure of a reactive environment by exploration. In D. S. Touretzky, editor, Advances in Neural Information Processing Systems 2, pp. 439-446. Morgan Kaufmann Publishers, San Mateo, CA, 1990.

  52. M. C. Mozer and J. R. Bachrach. Discovering the structure of a reactive environment by exploration. Neural Computation, 2: 447-457, 1990.

  53. R. S. Sutton and A. G. Barto. Time-derivative models of Pavlovian reinforcement. In M. Gabriel and J. Moore, editors, Learning and Computational Neuroscience,The MIT Press: Cambridge, MA, 1990, pp. 497-537.

  54. B. E. Ydstie. Forecasting and control using adaptive connectionist networks. Computers in Chemical Engineering., 14: 583-299, 1990.

  55. R. C. Yee, S. Saxena, P. E. Utgoff and A. G. Barto. Explaining temporal differences to create useful concepts for evaluating states. In Proceedings of the 8th National Conference on Artificial Intelligence, pp. 882-888. AAAI Press/MIT Press, 1990.

  56. M. C. Mozer and J. Bachrach. SLUG: A connectionist architecture for inferring the structure of finite-state environments. Machine Learning, 7 (2/3): 139-160, 1991.

  57. J.R. Bachrach. A connectionist learning control architecture for navigation. In R. Lippmann, J. Moody and D. Touretzky, editors, Advances in Neural Information Processing 3. Morgan Kaufmann: San Mateo, CA, 1991. pp. 457-463.

  58. A.G. Barto. Some learning tasks from a control perspective. In L. Nadel and D. Stein, editors, 1990 Lectures in Complex Systems, Addison-Wesley, 1991. pp. 195-223. [Also appeared as COINS Technical Report 90-122, University of Massachusetts at Amherst, December 1990.]

  59. S.P. Singh. Transfer of learning across compositions of sequential tasks. In L.A. Barnbaum and G.C. Collins, editors, Machine Learning: Proceedings of the Eighth International Workshop (ML91), Morgan Kaufmann: San Mateo, CA, 1991. pp. 348-352.

  60. R. S. Sutton, A. G. Barto and R. J. Williams. Reinforcement learning is direct adaptive optimal control. Proceedings of the 1991 American Control Conference, June 26-28, Boston, MA. pp. 2143-2146.

  61. J. C. Houk and A. G. Barto. Distributed sensorimotor learning. In G. E. Stelmach and J. Requin, editors, Tutorials in Motor Behavior II, Elsevier Science Publishers B.V.: Amsterdam, 1992, pp. 71-100. (Also appeared as NPB Technical Report #1, Center for Neuroscience Research on Neuronal Populations and Behavior, Northwestern University, Dec. 1991.)

  62. V. Gullapalli. A comparison of supervised and reinforcement learning methods on a reinforcement learning task. Proceedings of the 1991 IEEE International Symposium on Intelligent Control, Arlington, VA, August 1991, pp. 394-399.

  63. V. Gullapalli. Associative reinforcement learning of real-valued functions. Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics, Charlottesville, VA, October 1991.

  64. V. Gullapalli, R. A. Grupen and A. G. Barto. Learning reactive admittance control. In Proceedings of the 1992 IEEE Conference on Robotics and Automation. Nice, France, May 1992, pp. 1475-1480.

  65. J. W. Moore. A mechanism for timing conditioned responses. Technical Report 92-3, Computer Science Dept., University of Massachusetts, January 1992.

  66. S.P. Singh. Transfer of learning by composing solutions for elemental sequential tasks. Machine Learning, 8: 323-339, May 1992.

  67. N.E. Berthier, S.P. Singh, A.G. Barto and J.C. Houk. A cortico-cerebellar model that learns to generate distributed motor commands to control a kinematic arm. In Neural Information Processing Systems 4, J.E. Moody, S.J. Hanson, and R.P. Lippmann (Eds.), Morgan Kaufmann: San Mateo, 1992, pp. 611-618.

  68. V. Gullapalli and A. G. Barto. Shaping as a method for accelerating reinforcement learning. In Proceedings of the 1992 IEEE International Symposium on Intelligent Control, Glasgow, Scotland, August 1992.

  69. S. P. Singh. Scaling reinforcement learning algorithms by learning variable temporal resolution models. In Proceedings of the Ninth Machine Learning Conference, Aberdeen, Scotland, 1992. Morgan Kaufmann, pp. 406-415.

  70. J. R. Bachrach. Connectionist Modeling and Control of Finite State Environments. (Ph.D. Thesis) COINS Technical Report 92-6, University of Massachusetts, Amherst. January 1992.

  71. V. Gullapalli, Reinforcement Learning and its Application to Control. (Ph.D. Thesis) COINS Technical Report 92-10, University of Massachusetts, Amherst. January 1992.

  72. S. P. Singh. Reinforcement learning with a hierarchy of abstract models. In Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), San Jose, CA, July 1992. AAAI Press/MIT Press, pp. 202-207.

  73. S. P. Singh. The efficient learning of multiple task sequences. In Advances in Neural Information Processing Systems 4, J.E. Moody, S.J. Hanson, and R.P. Lippmann, editors, Morgan Kaufmann: San Mateo, 1992. pp. 251-258.

  74. V. Gullapalli. Dynamic systems control via associative reinforcement learning. In B. Soucek and the IRIS Group, editors, Dynamic, Genetic, and Chaotic Programming: The Sixth Generation. New York, NY: John Wiley &Sons, 1992, pp. 27-64.

  75. R. Yee. Abstraction in control learning. COINS Technical Report 92-16, University of Massachusetts, March 1992.

  76. A. G. Barto. Reinforcement learning and adaptive critic methods. In Handbook of Intelligent Control, D.A. White and D.A. Sofge, editors. New York: Van Nostrand Reinhold, 1992, pp. 469-491.

  77. A. G. Barto and S. J. Bradtke. Learning to solve stochastic optimal path problems using real-time dynamic programming. Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems, New Haven, CT, May 1992, pp. 143-148.

  78. A. G. Barto and V. Gullapalli. Neural networks and adaptive control. In P. Rudomin, M. A. Arbib, F. Cervantes-Perez, and R. Romo, editors, Neuroscience: From Neural Networks to Artificial Intelligence, Research Notes in Neural Computation, Vol. 4, Springer-Verlag, 1993. pp. 471-493. [Preprint appeared as NPB Technical Report 6, Center for Neuroscience Research on Neuronal Populations and Behavior, Northwestern University, March 1992.]

  79. V. Gullapalli. Robust control under extreme uncertainty. In Advances in Neural Information Processing Systems 5, C. L. Giles, S. J. Hanson, and J. D. Cowan (Eds), San Mateo: Morgan Kauffmann, 1993. pp. 327-334.

  80. N. E. Berthier, S. P. Singh, A. G. Barto, and J. C. Houk. Distributed representation of limb motor programs in arrays of adjustable pattern generators. Journal of Cognitive Neuroscience, 5 (1): 56-78, 1993. [Also appeared as NPB Technical Report #3, Center for Neuroscience Research on Neuronal Populations and Behavior, Northwestern University, 1991.]

  81. S. J. Bradtke. Reinforcement Methods Applied to Linear Quadratic Regulation. In Advances in Neural Information Processing Systems 5, C. L. Giles, S. J. Hanson, and J. D. Cowan (Eds), San Mateo: Morgan Kauffmann, 1993. pp. 295-302.

  82. S. Guzmán-Lara. Adjusting connections using reflexes as guidance. Technical Report 8. Center for the Study of Neuronal Populations and Behavior. August 1993.

  83. J. C. Houk, J. Kiefer, and A. G. Barto. Distributed motor commands in the limb premotor network. Trends in Neuroscience 16 (1): 27-33, 1993.

  84. S. P. Singh. Learning to Solve Markovian Decision Processes. (Ph.D. Thesis) CMPSCI Technical Report 93-77, University of Massachusetts, November 1993.

  85. V. Gullapalli, J. A. Franklin and H. Benbrahim. Acquiring robot skills via reinforcement learning. IEEE Control Systems Special Issue on Robotics: Capturing Natural Motion, 4(1): 13-24, Feb. 1994.

  86. V. Gullapalli, A. G. Barto and R. A. Grupen. Learning admittance mappings for force-guided assembly. Proceedings of the 1994 International Conference on Robotics and Automation,1994, pp. 2633-2638.

  87. S. P. Singh, A. G. Barto, R. Grupen, and C. Connolly. Robust reinforcement learning in motion planning. In Advances in Neural Information Processing Systems 6,J.D. Cowan, G. Tesauro and J. Alspector (Eds.), San Francisco: Morgan Kauffmann, 1994. pp. 655-662.

  88. A. Barto and M. Duff. Monte Carlo matrix inversion and reinforcement learning. In Advances in Neural Information Processing Systems 6,J.D. Cowan, G. Tesauro and J. Alspector (Eds.), San Francisco: Morgan Kauffmann, 1994. pp. 687-694.

  89. V. Gullapalli and A. Barto. Convergence of indirect adaptive asynchronous value iteration algorithms. In Advances in Neural Information Processing Systems 6,J.D. Cowan, G. Tesauro and J. Alspector (Eds.), San Francisco: Morgan Kauffmann, 1994. pp. 695-702.

  90. S. J. Bradtke, A. G. Barto, and B. E. Ydstie. A reinforcement learning method for direct adaptive linear quadratic control. 8th Yale Workshop on Adaptive and Learning Systems, Yale University, June 1994. pp. 85-96.

  91. A. G. Barto. Adaptive critics and the basal ganglia. In Models of Information Processing in the Basal Ganglia, J. C. Houk, J. Davis and D. Beiser (Eds.), Cambridge, MA: MIT Press, 1995, pp. 215-232.

  92. J. T. Buckingham, J. C. Houk and A. G. Barto. Controlling a nonlinear spring-mass system with a cerebellar model. 8th Yale Workshop on Adaptive and Learning Systems, Yale University, June 1994. pp. 1-6.

  93. J. C. Houk, J. L. Adams, and A. G. Barto. A model of how the basal ganglia generates and uses neural signals that predict reinforcement. In Models of Information Processing in the Basal Ganglia, J. C. Houk, J. Davis and D. Beiser (Eds.), Cambridge, MA: MIT Press, 1995, pp. 249-270.

  94. R. Grupen, J. Coelho, C. Connolly, V. Gullapalli, M. Huber, and K. Souccar. Towards Physical Interaction and Manipulation: Screwing in a Light Bulb. AAAI Spring Symposium Series. Stanford, 1994.

  95. S. Bradtke and M. Duff. Reinforcement learning methods for continuous-time Markov decision problems. 7th Annual Conference on Neural Information Processing Systems (NIPS7), November 1994.

  96. R. Crites and A. Barto. A class of actor/critic architectures that are equivalent to Q-learning. 7th Annual Conference on Neural Information Processing Systems (NIPS7), Denver, November 1994.

  97. S. J. Bradtke, B. E. Ydstie, and A. G. Barto. Adaptive linear quadratic control using policy iteration. CMPSCI Technical Report 94-49, University of Massachusetts, June 1994. Submitted to IEEE Transactions on Automatic Control, April 1994.

  98. M. Duff. Solving Bellman's Equation by the method of continuity. Proceedings of the 1994 American Control Conference, Baltimore, June 1994.

  99. A. G. Barto. Reinforcement learning. In Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), Cambridge: MIT Press, in press.

  100. A. G. Barto. Reinforcement learning in motor control. In Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), Cambridge: MIT Press, in press.

  101. A. G. Barto. Learning as hillclimbing in weight space. In Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), Cambridge: MIT Press, in press.

  102. S. J. Bradtke. Incremental Dynamic Programming for On-Line Adaptive Optimal Control. (Ph.D. Thesis) CMPSCI Technical Report 94-62, University of Massachusetts, August 1994.

  103. V. Gullapalli. Direct associative reinforcement learning methods for dynamic systems control. (Submitted to Neurocomputing.)

  104. N. Berthier, R. Clifton, V. Gullapalli, D. McCall, and D. Rubin. Visual information and object size in the control of reaching. (Submitted.)

  105. V. Gullapalli. Skillful Control Under Uncertainty via Direct Reinforcement Learning. (Submitted to Robotics and Autonomous Systems.)

  106. A. G. Barto. Reinforcement learning control. Current Opinion in Neurobiology, 4: 888-893, 1994.

  107. S. P. Singh and R. C. Yee. An upper bound on the loss from approximate optimal-value functions. Machine Learning, 16 (3): 227-233, 1994.

  108. S. J. Bradtke and A. G. Barto. New Algorithms for Temporal Difference Learning. Machine Learning, 108: Special Issue on Reinforcement Learning, to appear.

  109. T. W. Sandholm and R. H. Crites. Multiagent Reinforcement Learning in the Iterated Prisoner's Dilemma. Submitted to Biosystems Journal, November 1994.

  110. A. G. Barto, S. J. Bradtke and S. P. Singh. Learning to act using real-time dynamic programming. Artificial Intelligence, Special Volume on Computational Research on Interaction and Agency, 72(1): 81-138, 1995. [Also appeared as CMPSCI Technical Report 93-02, University of Massachusetts, January 1993. (Supercedes TR 91-57.)]

  111. A. G. Barto. Reinforcement learning and dynamic programming. To be presented at IFAC'95, Conference on Man-Machine Systems, Cambridge, MA, June 1995.

  112. M. Duff. Q-Learning for bandit problems. In A. Prieditis and S. Russell, editors, Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning (ML95), Morgan Kaufmann: Tahoe City, CA, 1995, pp. 209-217.

  113. J. T. Buckingham, A. G. Barto, and J. C. Houk. Adaptive Predictive Control with a Cerebellar Model. In Proceedings of the 1995 World Congress on Neural Networks, Volume 1, Lawrence Erlbaum Associates, Inc: Mahwah, NJ, 1995, pp. 373-380

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