Date: Thu, 21 Nov 1996 20:49:21 GMT Server: NCSA/1.5.2 Last-modified: Tue, 03 Sep 1996 18:37:53 GMT Content-type: text/html Content-length: 22052
Professor
Co-director, Adaptive NetWork Laboratory
Department of Computer Science
University of Massachusetts
Amherst, MA
01003 USA
barto@cs.umass.edu
Office: Lederle A265
413-545-2109, fax:413-545-1249
1984: R. S. Sutton, ``Temporal Credit Assignment in Reinforcement Learning."
1986: C. W. Anderson, ``Learning and Problem Solving with Multilayer Connectionist Systems."
1988: J. S. Judd, ``Neural Network Design and the Complexity of Learning."
1990: R. A. Jacobs, ``Task Decomposition through Competition in a Modular Connectionist Architecture."
1992: J. R. Bachrach, ``Connectionist Modeling and Control of Finite State Environments."
1992: V. Gullapalli, ``Reinforcement Learning and Its Application to Control."
1993: S. P. Singh, ``Learning to Solve Markovian Decision Processes."
1994: S. J. Bradtke, ``Incremental Dynamic Programming for On-Line Adaptive Optimal Control."
R. Crites and A. Barto. Improving Elevator Performance Using Reinforcement Learning . To appear in Advances in Neural Information Processing Systems 8 (NIPS8). (nips8.ps.Z: 58525 bytes)
R. Crites and A. Barto. An Actor/Critic Algorithm that is Equivalent to Q-Learning . Advances in Neural Information Processing Systems 7 (NIPS7), G. Tesauro, D. S. Touretzky, and T. K. Leen (Eds.), Cambridge, MA: MIT Press, 1995, pp. 401-408. (nips7.ps.Z: 64695 bytes)
A. G. Barto, S. J. Bradtke and S. P. Singh. ``Learning to act using real-time dynamic programming." Artificial Intelligence, Special Volume: Computational Research on Interaction and Agency, 72(1): 81-138, 1995.
S. P. Singh, A. G. Barto, R. Grupen and C. Connolly. ``Robust Reinforcement Learning in Motion Planning." Neural Information Processing Systems 6 (NIPS6), J. D. Cowan, G. Tesauro, and J. Alspector (Eds.), San Mateo: Morgan Kaufmann, 1994, pp. 655-662.
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.
V. Gullapalli and A. G. Barto. ``Convergence of Indirect Adaptive Value Iteration." Neural Information Processing Systems 6 (NIPS6), J. D. Cowan, G. Tesauro, and J. Alspector (Eds.), San Mateo: Morgan Kaufmann, 1994, pp. 695-662.
J. T. Buckingham, J. C. Houk, and A. G. Barto. ``Controlling a Nonlinear Spring-Mass System with a Cerebellar Model." Proceedings of the 8th Yale Workshop on Adaptive and Learning Systems. Yale University, 1994, pp. 1-6.
S. J. Bradtke, A. G. Barto and B. E. Ydstie. ``A Reinforcement Learning Method for Direct Adaptive Linear Quadratic Control." Proceedings of the 8th Yale Workshop on Adaptive and Learning Systems. Yale University, 1994, pp. 85-96.
A. G. Barto and M. Duff. ``Monte-Carlo Matrix Inversion and Reinforcement Learning." Neural Information Processing Systems 6 (NIPS6), J. D. Cowan, G. Tesauro, and J. Alspector (Eds.), San Mateo: Morgan Kaufmann, 1994, pp. 687-662.
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.
N.E. Berthier, S.P. Singh, A.G. Barto, and J.C. Houk. ``Distributed Representations of Limb Motor Programs in Arrays of Adjustable Pattern Generators" Journal of Cognitive Neuroscience, 5 (1): 56-78, 1993.
V. Gullapalli, R. A. Grupen, and A. G. Barto. ``Learning Reactive Admittance Control." Proceedings of the 1992 IEEE International Conference on Robotics and Automation. Nice, France, May 1992, pp. 1475-1480.
V. Gullapalli and A. G. Barto. ``Shaping as a Method for Accelerating Reinforcement Learning." Proceedings of the 1992 IEEE International Symposium on Intelligent Control. Glasgow, Scotland, August 1992, pp. 554-559.
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." Neural Information Processing Systems 4 (NIPS4), J. E. Moody, S. J. Hanson, and R. P. Lippmann (Eds.). San Mateo: Morgan Kaufmann, 1992, pp. 611-618.
A. G. Barto and S. J. Bradtke. ``Learning to Solve Stochastic Shortest Path Problems using Real-Time Dynamic Programming." Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems. New Haven CT, 1992, pp. 143-148.
N. Berthier, A. Barto, and J. Moore. ``Linear systems analysis of the relationship between firing of deep cerebellar neurons and the classically conditioned nictitating membrane response in rabbits." Biological Cybernetics, 65: 99-105, 1991.
A.G. Barto and S.P. Singh, ``Reinforcement learning and dynamic programming," Proceedings of the Sixth Yale Workshop on Adaptive and Learning Systems. New Haven, CT, 1990, pp. 83-88.
R.A. Jacobs, M.I. Jordan and A.G. Barto, ``Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Task," Cognitive Science, 15: 219-250, 1991.
R.C. Yee, S. Saxena, P.E. Utgoff and A.G. Barto, ``Explaining temporal differences to create useful concepts for evaluating states," Proceedings of the Eighth National Conference on Artificial Intelligence. Cambridge, MA, August 1990, pp. 882-888.
T. Sinkjær, C.H. Wu, A.G. Barto, and J.C. Houk, ``Cerebellar control of endpoint position - A simulation model," Proceedings of the 1990 International Joint Conference on Neural Networks. San Diego, CA, June 1990, pp. II-705-II-710.
A.G. Barto, R.S. Sutton and C. Watkins, ``Sequential decision problems and neural networks," Advances in Neural Information Processing 2 (NIPS2), D. Touretzky (Ed.). San Mateo, CA: Morgan Kaufmann, 1990, pp. 686-693.
R.S. Sutton and A.G. Barto, ``A temporal-difference model of classical conditioning," Proceedings of the Ninth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Erlbaum, 1987.
A.G. Barto and M.I. Jordan, ``Gradient following without back-propagation in layered networks," Proceedings of the IEEE First Annual Conference on Neural Networks. San Diego, CA, June 1987, pp. II-629-II-636.
A.G. Barto, ``Game-theoretic cooperativity in networks of self-interested units," in Neural Networks for Computing. J.S. Denker (Ed.). New York: American Institute of Physics, 1986, pp. 41-46.
A.G. Barto, ``Learning by statistical cooperation of self-interested neuron-like computing elements," Human Neurobiology, 4: 219-250, 1985.
J.W. Moore, J.E. Desmond, N.E. Berthier, D.E.J. Blazis, R.S. Sutton and A.G. Barto, ``Connectionistic learning in real time: Sutton-Barto adaptive element and classical conditioning of the nictitating membrane response," Proceedings of the Seventh Annual Conference of the Cognitive Science Society. Irvine, CA, August 1985.
O. Selfridge, R.S. Sutton and A.G. Barto, ``Training and tracking in robotics," Proceedings of the Ninth International Joint Conference in Artificial Intelligence. 1985, San Mateo, CA: Morgan Kaufmann, pp. 670-672.
A.G. Barto and C.W. Anderson, ``Structural Learning in Connectionist Systems, " Proceedings of the Seventh Annual Conference of the Cognitive Science Society. Irvine, CA, August 1985, pp. 43-54.
A.G. Barto and P. Anandan, ``Pattern recognizing stochastic learning automata," IEEE Trans. on Systems, Man, and Cybernetics, 15: 360-375, 1985.
A.G. Barto, R.S. Sutton and C.W. Anderson, ``Neuron-like adaptive elements that can solve difficult learning control problems," IEEE Trans. on Systems, Man, and Cybernetics, SMC-13: 834-846, 1983. (Reprinted in Neurocomputing: Foundations of Research, J.A. Anderson and E. Rosenfeld (Eds.), Cambridge, MA: The MIT Press, 1988, pp. 537-549.)
A.G. Barto, R.S. Sutton and C.W. Anderson, ``Spatial learning simulation systems," Proceedings of the 10th IMACS World Congress on Systems Simulation and Scientific Computation, 1982, pp. 204-206.
A.G. Barto, C.W. Anderson and R.S. Sutton, ``Synthesis of nonlinear control surfaces by a layered associative network," Biological Cybernetics, 43: 175-185, 1982.
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.
A.G. Barto and R.S. Sutton, ``An adaptive network that constructs and uses an internal model of its environment," Cognition and Brain Theory, 4: 217-246, 1981.
A.G. Barto and R.S. Sutton, ``Landmark learning: An illustration of associative search," Biological Cybernetics, 42: 1-8, 1981.
A.G. Barto, R.S. Sutton and P. Brouwer, ``Associative search network: A reinforcement learning associative memory," Biological Cybernetics, 40: 201-211, 1981.
R.S. Sutton and A.G. Barto, ``Toward a modern theory of adaptive networks: Expectation and prediction," Psychological Review, 88: 135-170, 1981.
A.G. Barto, ``Invariant linear models of varying linear systems," NATO Conference Series, Series II, Systems Science, 5, G. Klir (Ed.), Plenum, New York, 1978.
A.G. Barto, ``A note on pattern reproduction in tesselation structures," Journal of Computer and Systems Sciences, 16: 445-455, 1978.
A.G. Barto, ``Discrete and continuous models," International Journal of General Systems, 4: 163-177, 1978.
A.G. Barto, ``A neural network simulation method using the Fast Fourier Transform," IEEE Transactions on Systems, Man, and Cybernetics, SMC-5: 863-867, 1976.
A. G. Barto. ``Learning as hillclimbing in weight space." In Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), Cambridge: MIT Press, 1995.
A. G. Barto. ``Reinforcement learning in motor control." In Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), Cambridge: MIT Press, 1995.
A. G. Barto. ``Reinforcement learning." In Handbook of Brain Theory and Neural Networks, M.A. Arbib (Ed.), Cambridge: MIT Press, 1995.
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." 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.
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.
A. G. Barto and V. Gullapalli. ``Neural networks and adaptive control." In P. Rudomin, M.A. Arbib and 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.
A. G. Barto, ``Reinforcement Learning and Adaptive Critic Methods." In Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches. D. A. White and D. A. Sofge (Eds.). New York: Van Nostrand Reinhold 1992, pp. 469-491.
A.G. Barto. ``Learning algorithms." In Encyclopedia of Learning and Memory. L.R. Squire (Ed.). New York: MacMillan, 1992.
A.G. Barto. ``Reinforcement learning and adaptive critic methods." In Handbook of Intelligent Control. D.A. white and D.A. Sofgee (Eds.), New York: Van Nostrand Reinhold, 1992, pp. 469-491.
J.C. Houk and A.G. Barto. ``Distributed sensorimotor learning." In Tutorials in Motor Behavior II. G.E. Stelmach and J. Requin (Eds.). Amsterdam: Elsevier Science Publishers, 1992, pp. 71-100.
A.G. Barto, ``Some learning problems from the perspective of control." In 1990 Lectures in Complex Systems. L. Nadel and D.L. Stein (Eds.). Redwood City: Addison-Wesley, 1991, pp. 195-223.
A.G. Barto and S.P. Singh, ``On the computational economics of reinforcement learning." In Proceedings of the 1990 Connectionist Models Summer School. D.S. Touretzky, J.L. Elman, T.J. Sejnowski, and G.E. Hinton (Eds.). San Mateo, CA: Morgan Kaufmann, 1990, pp. 35-44.
J.C. Houk, S.P. Singh, C. Fisher and A.G. Barto, ``An adaptive network inspired by the anatomy and physiology of the cerebellum." In Neural Networks for Control. T. Miller, R.S. Sutton, and P.J. Werbos (Eds.), Cambridge, MA: MIT Press, 1990, pp. 301-348.
A.G. Barto, ``Connectionist learning for control: An overview." In Neural Networks for Control. T. Miller, R.S. Sutton, and P.J. Werbos (Eds.), Cambridge, MA: MIT Press, 1990, pp. 5-58.
R.S. Sutton and A.G. Barto, ``A time-derivative theory of Pavlovian conditioning." In Learning and Computational Neuroscience. M. Gabriel and J.W. Moore (Eds.), Cambridge, MA: MIT Press, 1990, pp. 497-537.
A.G. Barto, R.S. Sutton and C. Watkins, ``Learning and sequential decision making." In Learning and Computational Neuroscience. M. Gabriel and J.W. Moore (Eds.), Cambridge, MA: MIT Press, 1990, pp. 539-602.
A.G. Barto, ``From chemotaxis to cooperativity: Abstract exercises in neuronal learning strategies." In The Computing Neuron. R. Durbin, R. Maill, and G. Mitchison (Eds.), Reading, MA: Addison-Wesley, 1989, pp. 73-98.
A.G. Barto, ``An approach to learning control surfaces by connectionist systems." In Vision, Brain and Cooperative Computation. M.A. Arbib and A.R. Hanson (Eds.), Cambridge, MA: MIT Press, 1987, pp. 665-701.
A.G. Barto and R.S. Sutton, ``Neural problem solving." In Synaptic Modification, Neuron Selectivity, and Nervous System Organization. W. B. Levi, J. A. Anderson and S. Lehmkuhle (Eds.), Hillsdale, NJ: Erlbaum, 1983, pp. 123-152.
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.
A. G. Barto. `` Reinforcement learning and dynamic programming." Proceedings of the 6th IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Man-Machine Systems. Cambridge, MA, June 1995. pp. 469-474.
A. G. Barto. ``Reinforcement learning control." Current Opinion in Neurobiology, 4: 888-893, December 1994.
A. G. Barto. Forward for Adaptive, Learning and Pattern Recogintion Systems: Theory and Applications, Second Edition. J. M. Mendel and K. S. Fu (Eds.). To appear.
R.S. Sutton, A.G. Barto, and R.J. Williams. ``Reinforcement Learning is Direct Adaptive Optimal Control." Proceedings of the 1991 American Control Conference. American Automatic Control Council, 1991, pp. 2143-2146.
A.G. Barto, ``Learning and Incremental Dynamic Programming." Commentary on C. W. Clark's ``Modeling Behavioral Adaptations," Behavioral Brain Science, Vol. 14, 1991, pp. 94-95.
N.E. Berthier, A.G. Barto, and J.C. Houk. ``A Network Model of the Cerebellum that Uses a Trained Set of Pattern Generators to Control a Single Degree-of-Freedom Joint." Society for Neuroscience Abstracts. Vol. 17, p. 1382, 1991.
A.G. Barto, N.E. Berthier, S.P. Singh, and J.C. Houk, ``Network model of the cerebellum and motor cortex that learns to control planar limb movements." Abstract, Society of Neuroscience Abstracts, Vol. 16, Part 2, p. 1223, 1990.
V. Srinivasan, A.G. Barto and B.E. Ydstie, ``Pattern recognition and feedback via parallel distributed processing." Abstract, Annual Meeting of the AIChE, Washington DC, November, 1988.
A.G. Barto (editor), ``Multilayer networks of self-interested adaptive units." Final Technical Report AFWAL-TR-87-1052, Avionics Laboratory (AFWAL/AAAT), Air Force Wright Aeronautical Laboratories, Wright-Patterson Air Force Base, OH 45433, 1987.
A.G. Barto, ``Adaptive neural networks for learning control: Some computational experiments." Proceedings of the IEEE Workshop on Intelligent Control, Rensselaer Polytechnic Institute, Troy, NY, August 1985.
A.G. Barto, P. Anandan and C.W. Anderson, ``Cooperativity in networks of pattern recognizing stochastic learning automata." Proceedings of the Fourth Yale Workshop on Applications of Adaptive Systems Theory, New Haven, CT, May 1985 (an extended version appears in Adaptive and Learning Systems, K.S. Narendra (Ed.), New York: Plenum Press, 1986, pp. 235-246).
A.G. Barto (editor), ``Simulation experiments with goal-seeking adaptive elements." Final Technical Report AFWAL-TR-84-1022, Avionics Laboratory (AFWAL/AAAT), Air Force Wright Aeronautical Laboratories, Wright-Patterson Air Force Base, Ohio 45433, 1984.
A.G. Barto, Review of S. Grossberg's Studies of Mind and Brain, Mathematical Biosciences, 70, New York: D. Reidel Publishing Company, 1982, pp. 111-113.
A.G. Barto and S. Epstein, ``Adaptive networks and sensorimotor control." Proceedings of the Second Workshop on Visuomotor Coordination in Frog and Toad: Theory and Experiment, November 1982, Mexico City, Mexico.
A.G. Barto and R.S. Sutton, ``Goal seeking components for adaptive intelligence: An initial assessment." Final Technical Report AFWAL-TR-81-1070, Avionics Laboratory (AFWAL/AAAT), Air Force Wright Aeronautical Laboratories, Wright-Patterson Air Force Base, Ohio 45433, 1981.
B.P. Zeigler and A.G. Barto, ``Alternative formalisms for biosystem and ecosystem modelling." In New Directions in the Analysis of Ecological Systems, Part 2, G. Innis (Ed.), Simulation Councils Proceedings Series, 5, 1977, pp. 167-178.
A.G. Barto, ``Cellular automata as models of natural systems." Ph.D. Thesis, Logic of Computers Group Technical Report, University of Michigan, 1975.
A.G. Barto, ``Simulation of networks using multidimensional Fast Fourier Transforms." ACM Simuletter, 5, July 1974.