ALL Publications, 1978-1993
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] edu1993
- A. G. Barto and V. Gullapalli
Neural networks and adaptive control
Neuroscience: From Neural Networks to Artificial Intelligence, p. 471-493, Research Notes in Neural Computation, Vol. 4, Springer-Verlag, 1993.
[pdf] - V. Gullapalli
Robust control under extreme uncertainty
Advances in Neural Information Processing Systems 5, p.327-334, 1993. - 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.
[pdf] - S. J. Bradtke
Reinforcement Methods Applied to Linear Quadratic Regulation
Advances in Neural Information Processing Systems 5, p. 295-302, 1993. - S. Guzmán-Lara
Adjusting connections using reflexes as guidance
Technical Report 8. Center for the Study of Neuronal Populations and Behavior. August 1993. - 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.
[pdf] - S. P. Singh
Learning to Solve Markovian Decision Processes
(Ph.D. Thesis) CMPSCI Technical Report 93-77, University of Massachusetts, November 1993.
1992
- J. C. Houk and A. G. Barto
Distributed sensorimotor learning
Tutorials in Motor Behavior II, Elsevier Science Publishers B.V.: Amsterdam, 1992, pp. 71-100.
- V. Gullapalli, R. A. Grupen and A. G. Barto
Learning reactive admittance control
Proceedings of the 1992 IEEE Conference on Robotics and Automation, p. 1475-1480, Nice, France, May 1992
[pdf]
- R. S. Sutton, A. G. Barto and R. J. Williams
Reinforcement learning is direct adaptive optimal control
Control Systems, IEEE 12.2: p. 19-22, 1992.
[pdf]
- J. W. Moore
A mechanism for timing conditioned responses
Technical Report 92-3, Computer Science Dept., University of Massachusetts, January 1992.
- S.P. Singh
Transfer of learning by composing solutions for elemental sequential tasks
Machine Learning, 8: 323-339, May 1992.
- 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, p. 611-618, 1992.
- 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.
[pdf]
- S. P. Singh
Scaling reinforcement learning algorithms by learning variable temporal resolution models
Proceedings of the Ninth Machine Learning Conference, p. 406-415, Aberdeen, Scotland, 1992.
- 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.
- V. Gullapalli
Reinforcement Learning and its Application to Control
(Ph.D. Thesis) COINS Technical Report 92-10, University of Massachusetts, Amherst. January 1992.
- S. P. Singh
Reinforcement learning with a hierarchy of abstract models
Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), p. 202-207, San Jose, CA, July 1992.
- S. P. Singh
The efficient learning of multiple task sequences
Advances in Neural Information Processing Systems 4, p. 251-258, 1992.
- V. Gullapalli
Dynamic systems control via associative reinforcement learning
Dynamic, Genetic, and Chaotic Programming: The Sixth Generation, p. 27-64, 1992.
- R. Yee
Abstraction in control learning
COINS Technical Report 92-16, University of Massachusetts, March 1992.
- A. G. Barto
Reinforcement learning and adaptive critic methods
Handbook of Intelligent Control, New York: Van Nostrand Reinhold, p. 469-491, 1992.
- 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, p. 143-148, New Haven, CT, May 1992.
1991
- 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.
[pdf]
- 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.
- J.R. Bachrach
A connectionist learning control architecture for navigation
Advances in Neural Information Processing 3, p. 457-463, 1991.
- A.G. Barto
Some learning tasks from a control perspective
1990 Lectures in Complex Systems, Addison-Wesley, 1991.
[pdf]
- S.P. Singh
Transfer of learning across compositions of sequential tasks
Machine Learning: Proceedings of the Eighth International Workshop, p. 348-352, 1991.
- 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, p. 394-399, Arlington, VA, August 1991.
- 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.
1990
- 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.
[pdf]
- 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, August 1990. pp. 83-88.
- 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, pp. 35-44, 1990.
[pdf]
- 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.
[pdf]
- A. G. Barto, R. S. Sutton and C. J. C. H. Watkins
Sequential decision problems and neural networks
Advances in Neural Information Processing Systems 2, p. 686-693, San Mateo, CA, 1990.
[pdf]
- V. Gullapalli
Associative reinforcement learning of real-valued functions
COINS Technical Report 90-129, University of Massachusetts, May 1990.
- V. Gullapalli
Modeling cortical area 7a using stochastic real-valued (SRV) units
Proceedings of the 1990 Connectionist Models Summer School, p. 363-368. San Mateo, CA, 1990.
- V. Gullapalli
A stochastic reinforcement learning algorithm for learning real-valued functions
Neural Networks, 3:671-692, 1990.
- 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
Neural Networks for Control, p. 301-348. Cambridge, MA: MIT Press, 1990.
- J. C. Houk
Cooperative control of limb movements by the motor cortex, brainstem and cerebellum
Models of Brain Function, Cambridge University Press, 1990.
- 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.
- M. I. Jordan and R. A. Jacobs
Learning to control an unstable system with forward modeling
Advances in Neural Information Processing Systems 2, p. 324-331, 1990.
- M. C. Mozer and J. Bachrach
Discovering the structure of a reactive environment by exploration
Advances in Neural Information Processing Systems 2, p. 439-446, 1990.
- M. C. Mozer and J. R. Bachrach
Discovering the structure of a reactive environment by exploration
Neural Computation, 2: 447-457, 1990.
- R. S. Sutton and A. G. Barto
Time-derivative models of Pavlovian reinforcement
Learning and Computational Neuroscience, p. 497-537, The MIT Press: Cambridge, MA, 1990.
[pdf]
- B. E. Ydstie
Forecasting and control using adaptive connectionist networks
Computers in Chemical Engineering, 14: 583-299, 1990.
- 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 8th National Conference on Artificial Intelligence, p. 882-888, AAAI Press/MIT Press, 1990.
[pdf]
1989
- 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]
1988
- 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.
- V. Gullapalli
A stochastic algorithm for learning real-valued functions via reinforcement feedback
COINS Technical Report 88-91, University of Massachusetts, 1988.
- R. A. Jacobs
Increased rates of convergence through learning rate adaptation
Neural Networks, 1:295-307, 1988.
- 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.
- M. I. Jordan
Supervised learning and systems with excess degrees of freedom
COINS Technical Report 88-27, University of Massachusetts.
- M. I. Jordan and D. A. Rosenbaum
Action
Handbook of Cognitive Science,Cambridge, MA: MIT Press, 1988.
- S. Judd
On the complexity of loading shallow neural networks
Journal of Complexity,1988.
- R. S. Sutton
Learning to predict by the methods of temporal differences
Machine Learning,3: 9-44, 1988.
1987
- A. G. Barto
An approach to learning control surfaces by connectionist systems
Vision, Brain and Cooperative Computation, MIT Press/Bradford Books, Cambridge, MA, 1987.
- 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, p. II-629-II-636.
[pdf]
- 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.
- S. Judd
Learning in networks is hard
Proceedings of the First IEEE Annual Conference on Neural Networks, San Diego, CA, June 1987.
- J. S. Judd
Complexity of connectionist learning with various node functions
COINS Technical Report 87-60, University of Massachusetts, 1987.
- 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.
- 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, July, 1987.
[pdf]
1986
- A. G. Barto
Game-theoretic cooperativity in networks of self-interested units
Neural Networks for Computing, 151(1): 41-46, American Institute of Physics, New York, 1986.
[pdf]
- A. G. Barto, P. Anandan, and C. W. Anderson
Cooperativity in networks of pattern recognizing stochastic learning automata
Adaptive and Learning Systems: Theory and Applications, Plenum, New York, 1986.
[pdf]
- 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.
- 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.
[pdf]
1985
- 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
Learning by statistical cooperation of self-interested neuron-like computing elements
Human Neurobiology,4:229-256, 1985.
[pdf]
- A. G. Barto and P. Anandan
Pattern recognizing stochastic learning automata
IEEE Transactions on Systems, Man, and Cybernetics, 15:360-375, 1985.
[pdf]
- 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.
- O. Selfridge, R. S. Sutton and A. G. Barto
Training and tracking in robotics
Proceedings of the Ninth International Joint Conference on Artificial Intelligence, p. 670-672. San Mateo, CA: Morgan Kaufmann, 1985.
[pdf]
- 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.
[pdf]
- Moore, J., Desmond, J., Berthier, N., Blazis, D., Sutton, R.S., and Barto, A.G.
Connectionist learning in real time: Sutton-Barto adaptive element and classical conditioning of the nictitating membrane response
Seventh Annual Conference of the Cognitive Science Society, p. 318-322, 1985.
[pdf]
1983
- A. G. Barto and R. S. Sutton
Neural problem solving
COINS Technical Report 83-03, University of Massachusetts, 1983.
[pdf ]
- 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.
[pdf ]
1982
- 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.
- 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.
[pdf ]
- 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.
[pdf ]
- S. Bozinovski
A self-learning system using secondary reinforcement.
Cybernetics and Systems, Elsevier Science Publishers (North Holland), 1982.
- 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, pp. 204-206, 1982.
1981
- A. G. Barto and R. S. Sutton
Landmark learning: An illustration of associative search
Biological Cybernetics,42:1-8, 1981.
[pdf]
- A. G. Barto, R. S. Sutton and P. S. Brouwer
Associative search network: A reinforcement learning associative memory
Biological Cybernetics,40:201-211, 1981.
[pdf]
- S. Bozinovski
Teaching space: A representation concept for adaptive pattern classification
COINS Technical Report 81-18, University of Massachusetts, 1981.
- 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.
[pdf]
- R. S. Sutton and A. G. Barto
Toward a modern theory of adaptive networks: Expectation and prediction
Psychological Review, 88:135-170, 1981.
[pdf]
- A. G. Barto and R.S. Sutton
Goal seeking components for adaptive intelligence: An initial assessment
AFWAL-TR-81-1070, Publisher Air Force Wright Aeronautical Laboratories, 1981.
[pdf ]
1978
- A. G. Barto
Discrete and continuous models
International Journal of General Systems, 4:163-177, 1978.
[pdf]
- A.G. Barto
A note on pattern reproduction in tesselation structures
Journal of Computer and Systems Sciences, 16:445-455, 1978.
[pdf]
- A.G. Barto
Structurally invariant linear models of structurally varying linear systems
Applied General Systems Research NATO Conference Series Volume 5, Springer, pp. 435-451, 1978.
[ pdf ]
Distributed sensorimotor learning
Tutorials in Motor Behavior II, Elsevier Science Publishers B.V.: Amsterdam, 1992, pp. 71-100.
Learning reactive admittance control
Proceedings of the 1992 IEEE Conference on Robotics and Automation, p. 1475-1480, Nice, France, May 1992
[pdf]
Reinforcement learning is direct adaptive optimal control
Control Systems, IEEE 12.2: p. 19-22, 1992.
[pdf]
A mechanism for timing conditioned responses
Technical Report 92-3, Computer Science Dept., University of Massachusetts, January 1992.
Transfer of learning by composing solutions for elemental sequential tasks
Machine Learning, 8: 323-339, May 1992.
A cortico-cerebellar model that learns to generate distributed motor commands to control a kinematic arm
Neural Information Processing Systems 4, p. 611-618, 1992.
Shaping as a method for accelerating reinforcement learning
Proceedings of the 1992 IEEE International Symposium on Intelligent Control, Glasgow, Scotland, August 1992.
[pdf]
Scaling reinforcement learning algorithms by learning variable temporal resolution models
Proceedings of the Ninth Machine Learning Conference, p. 406-415, Aberdeen, Scotland, 1992.
Connectionist Modeling and Control of Finite State Environments
(Ph.D. Thesis) COINS Technical Report 92-6, University of Massachusetts, Amherst. January 1992.
Reinforcement Learning and its Application to Control
(Ph.D. Thesis) COINS Technical Report 92-10, University of Massachusetts, Amherst. January 1992.
Reinforcement learning with a hierarchy of abstract models
Proceedings of the Tenth National Conference on Artificial Intelligence (AAAI-92), p. 202-207, San Jose, CA, July 1992.
The efficient learning of multiple task sequences
Advances in Neural Information Processing Systems 4, p. 251-258, 1992.
Dynamic systems control via associative reinforcement learning
Dynamic, Genetic, and Chaotic Programming: The Sixth Generation, p. 27-64, 1992.
Abstraction in control learning
COINS Technical Report 92-16, University of Massachusetts, March 1992.
Reinforcement learning and adaptive critic methods
Handbook of Intelligent Control, New York: Van Nostrand Reinhold, p. 469-491, 1992.
Learning to solve stochastic optimal path problems using real-time dynamic programming
Proceedings of the Seventh Yale Workshop on Adaptive and Learning Systems, p. 143-148, New Haven, CT, May 1992.
Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks
Cognitive Science, 15:219-250, 1991.
[pdf]
SLUG: A connectionist architecture for inferring the structure of finite-state environments
Machine Learning, 7(2/3): 139-160, 1991.
A connectionist learning control architecture for navigation
Advances in Neural Information Processing 3, p. 457-463, 1991.
Some learning tasks from a control perspective
1990 Lectures in Complex Systems, Addison-Wesley, 1991.
[pdf]
Transfer of learning across compositions of sequential tasks
Machine Learning: Proceedings of the Eighth International Workshop, p. 348-352, 1991.
A comparison of supervised and reinforcement learning methods on a reinforcement learning task
Proceedings of the 1991 IEEE International Symposium on Intelligent Control, p. 394-399, Arlington, VA, August 1991.
Associative reinforcement learning of real-valued functions
Proceedings of the 1991 IEEE International Conference on Systems, Man, and Cybernetics, Charlottesville, VA, October 1991.
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.
[pdf]
Reinforcement learning and dynamic programming
Proceedings of the Sixth Yale Workshop on Adaptive and Learning Systems, New Haven, CT, August 1990. pp. 83-88.
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, pp. 35-44, 1990.
[pdf]
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.
[pdf]
Sequential decision problems and neural networks
Advances in Neural Information Processing Systems 2, p. 686-693, San Mateo, CA, 1990.
[pdf]
Associative reinforcement learning of real-valued functions
COINS Technical Report 90-129, University of Massachusetts, May 1990.
Modeling cortical area 7a using stochastic real-valued (SRV) units
Proceedings of the 1990 Connectionist Models Summer School, p. 363-368. San Mateo, CA, 1990.
A stochastic reinforcement learning algorithm for learning real-valued functions
Neural Networks, 3:671-692, 1990.
An adaptive sensorimotor network inspired by the anatomy and physiology of the cerebellum
Neural Networks for Control, p. 301-348. Cambridge, MA: MIT Press, 1990.
Cooperative control of limb movements by the motor cortex, brainstem and cerebellum
Models of Brain Function, Cambridge University Press, 1990.
Task Decomposition Through Competition in a Modular Connectionist Architecture
(Ph.D. Thesis) COINS Technical Report 90-44, University of Massachusetts at Amherst, May 1990.
Learning to control an unstable system with forward modeling
Advances in Neural Information Processing Systems 2, p. 324-331, 1990.
Discovering the structure of a reactive environment by exploration
Advances in Neural Information Processing Systems 2, p. 439-446, 1990.
Discovering the structure of a reactive environment by exploration
Neural Computation, 2: 447-457, 1990.
Time-derivative models of Pavlovian reinforcement
Learning and Computational Neuroscience, p. 497-537, The MIT Press: Cambridge, MA, 1990.
[pdf]
Forecasting and control using adaptive connectionist networks
Computers in Chemical Engineering, 14: 583-299, 1990.
Explaining temporal differences to create useful concepts for evaluating states
Proceedings of the 8th National Conference on Artificial Intelligence, p. 882-888, AAAI Press/MIT Press, 1990.
[pdf]
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]
Temporally adaptive conditioned responses: Representation of the stimulus trace in neural-network models
COINS Technical Report 88-80, University of Massachusetts, 1988.
A stochastic algorithm for learning real-valued functions via reinforcement feedback
COINS Technical Report 88-91, University of Massachusetts, 1988.
Increased rates of convergence through learning rate adaptation
Neural Networks, 1:295-307, 1988.
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.
Supervised learning and systems with excess degrees of freedom
COINS Technical Report 88-27, University of Massachusetts.
Action
Handbook of Cognitive Science,Cambridge, MA: MIT Press, 1988.
On the complexity of loading shallow neural networks
Journal of Complexity,1988.
Learning to predict by the methods of temporal differences
Machine Learning,3: 9-44, 1988.
An approach to learning control surfaces by connectionist systems
Vision, Brain and Cooperative Computation, MIT Press/Bradford Books, Cambridge, MA, 1987.
Gradient following without back-propagation in layered networks
Proceedings of the First IEEE Annual Conference on Neural Networks, San Diego, CA, June 1987, p. II-629-II-636.
[pdf]
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.
Learning in networks is hard
Proceedings of the First IEEE Annual Conference on Neural Networks, San Diego, CA, June 1987.
Complexity of connectionist learning with various node functions
COINS Technical Report 87-60, University of Massachusetts, 1987.
Two attentional models of classical conditioning: Variations in CS effectiveness revisited
COINS Technical Report 87-29, University of Massachusetts, 1987.
A temporal-difference model of classical conditioning
Proceedings of the Ninth Annual Conference of the Cognitive Science Society, July, 1987.
[pdf]
Game-theoretic cooperativity in networks of self-interested units
Neural Networks for Computing, 151(1): 41-46, American Institute of Physics, New York, 1986.
[pdf]
Cooperativity in networks of pattern recognizing stochastic learning automata
Adaptive and Learning Systems: Theory and Applications, Plenum, New York, 1986.
[pdf]
Attractor dynamics and parallelism in a connectionist sequential machine
Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA, 1986.
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.
[pdf]
Adaptive neural networks for learning control: Some computational experiments
Proceedings of the IEEE Workshop on Intelligent Control, Rensselaer Polytechnic Institute, Troy, NY, August 1985.
Learning by statistical cooperation of self-interested neuron-like computing elements Human Neurobiology,4:229-256, 1985.
[pdf]
Pattern recognizing stochastic learning automata
IEEE Transactions on Systems, Man, and Cybernetics, 15:360-375, 1985.
[pdf]
Structural learning in connectionist systems
Proceedings of the Seventh Annual Conference of the Cognitive Science Society, Irvine, CA, August 1985.
Training and tracking in robotics
Proceedings of the Ninth International Joint Conference on Artificial Intelligence, p. 670-672. San Mateo, CA: Morgan Kaufmann, 1985.
[pdf]
The learning of world models by connectionist networks
Proceedings of the Seventh Annual Conference of the Cognitive Science Society, Irvine, CA, 1985.
[pdf]
Connectionist learning in real time: Sutton-Barto adaptive element and classical conditioning of the nictitating membrane response
Seventh Annual Conference of the Cognitive Science Society, p. 318-322, 1985.
[pdf]
Neural problem solving
COINS Technical Report 83-03, University of Massachusetts, 1983.
[pdf ]
Neuronlike elements that can solve difficult learning control problems
IEEE Transactions on Systems, Man, and Cybernetics, 13:835-846, 1983.
[pdf ]
Feature generation and selection by a layered network of reinforcement learning elements: Some initial experiments.
COINS Technical Report 82-12, University of Massachusetts, 1982.
Synthesis of nonlinear control surfaces by a layered associative search network
Biological Cybernetics,43:175-185, 1982.
[pdf ]
Simulation of anticipatory responses in classical conditioning by a neuron-like adaptive element
Behavioural Brain Research, 4:221-235, 1982.
[pdf ]
A self-learning system using secondary reinforcement.
Cybernetics and Systems, Elsevier Science Publishers (North Holland), 1982.
Spatial learning simulation systems
Proceedings of the 10th IMACS World Congress on Systems Simulation and Scientific Computation, pp. 204-206, 1982.
Landmark learning: An illustration of associative search
Biological Cybernetics,42:1-8, 1981.
[pdf]
Associative search network: A reinforcement learning associative memory
Biological Cybernetics,40:201-211, 1981.
[pdf]
Teaching space: A representation concept for adaptive pattern classification
COINS Technical Report 81-18, University of Massachusetts, 1981.
An adaptive network that constructs and uses an internal model of its environment
Cognition and Brain Theory, 4:217-246, 1981.
[pdf]
Toward a modern theory of adaptive networks: Expectation and prediction
Psychological Review, 88:135-170, 1981.
[pdf]
Goal seeking components for adaptive intelligence: An initial assessment
AFWAL-TR-81-1070, Publisher Air Force Wright Aeronautical Laboratories, 1981.
[pdf ]
Discrete and continuous models
International Journal of General Systems, 4:163-177, 1978.
[pdf]
A note on pattern reproduction in tesselation structures
Journal of Computer and Systems Sciences, 16:445-455, 1978.
[pdf]
Structurally invariant linear models of structurally varying linear systems
Applied General Systems Research NATO Conference Series Volume 5, Springer, pp. 435-451, 1978.
[ pdf ]