Bibliography

1

M. I. Jordan.
Supervised learning and systems with excess degrees of freedom.
Technical Report COINS TR 88-27, Massachusetts Institute of Technology, 1988.

2

P. W. Munro.
A dual back-propagation scheme for scalar reinforcement learning.
Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, WA, pages 165-176, 1987.

3

F. Nake.
Ästhetik als Informationsverarbeitung.
Springer, 1974.

4

Nguyen and B. Widrow.
The truck backer-upper: An example of self learning in neural networks.
In Proceedings of the International Joint Conference on Neural Networks, pages 357-363. IEEE Press, 1989.

5

A. J. Robinson and F. Fallside.
Static and dynamic error propagation networks with application to speech coding.
Proceedings of Neural Information Processing Systems, American Institute of Physics, 1987.

6

T. Robinson and F. Fallside.
Dynamic reinforcement driven error propagation networks with application to game playing.
In Proceedings of the 11th Conference of the Cognitive Science Society, Ann Arbor, pages 836-843, 1989.

7

J. Schmidhuber.
Learning algorithms for networks with internal and external feedback.
In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, editors, Proc. of the 1990 Connectionist Models Summer School, pages 52-61. Morgan Kaufmann, 1990.

8

J. Schmidhuber.
An on-line algorithm for dynamic reinforcement learning and planning in reactive environments.
In Proc. IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 2, pages 253-258, 1990.

9

J. Schmidhuber.
Recurrent networks adjusted by adaptive critics.
In Proc. IEEE/INNS International Joint Conference on Neural Networks, Washington, D. C., volume 1, pages 719-722, 1990.

10

J. Schmidhuber.
Reinforcement learning with interacting continually running fully recurrent networks.
In Proc. INNC International Neural Network Conference, Paris, volume 2, pages 817-820, 1990.

11

J. Schmidhuber and R. Huber.
Learning to generate focus trajectories for attentive vision.
Technical Report FKI-128-90, Institut für Informatik, Technische Universität München, 1990.

12

P. J. Werbos.
Backpropagation and neurocontrol: A review and prospectus.
In IEEE/INNS International Joint Conference on Neural Networks, Washington, D.C., volume 1, pages 209-216, 1989.

13

R. J. Williams.
On the use of backpropagation in associative reinforcement learning.
In IEEE International Conference on Neural Networks, San Diego, volume 2, pages 263-270, 1988.

14

R. J. Williams and D. Zipser.
Experimental analysis of the real-time recurrent learning algorithm.
Connection Science, 1(1):87-111, 1989.

Back to Active Learning - Exploration - Curiosity page