Can Machines Ever Learn from Their Own Mistakes?

Barto A. G. Sutton R. S. Anderson C. W. (1983). Neuronlike Adaptive Elements that can Solve Difficult Learning Control Problems IEEE Trans. Syst. Man. Cybern. Vol. SMC-13, 834–846.

Carpenter G. A. Grossberg S , (1987a), A Massively Parallel Architecture for a Self-organizing Neural Pattern Recognition Machine, Computer Vision, Graphics, and Image Processing, 37, 54–115.

Carpenter G. A. Grossberg S. Rosen D. B. , (1991), Fuzzy ART: Fast Stable Learning and Categorization of Analog Patterns by an Adaptive Resonance System. Neural Networks, 4, 759–771.

Carpenter G. A. Grossberg S. Markuzon N. Reynolds J. H. Rosen D. B. , (1992), FuzzyARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps, IEEE Transactions on Neural Networks, 3, 698–712.

Fujita O. (1992), Optimization of the Hidden Unit Function in Feedforward Neural Networks Neural Networks, 5, 755–764.

Kandirkamanathan V. Niranjan M (1992), Technical Report CUED/FINFENG/TR. 111, Cambridge University, Cambridge, UK.

Haykin S. (1994), Neural Networks: a Comprehensive Foundation Macmillan NY.

Platt J. C. (1991), A Resource Allocating Network for Function Interpolation. Neural Computation 3 (2), 215–225.

Marriott S. Harrison R. F. (1995) A Self-Organising State-space Decoder for Reinforcement Learning, Research Report No 582, The University of Sheffield, UK.

Michie D. Chambers R.A. , (1968). BOXES: An Experiment in Adaptive Control, in Machine Intelligence 2, Dale E. Michie D. Eds. Edinburgh: Oliver and Boyd.

Kohonen T. (1989), Self-Organisation and Associative Memory, (3rd Edn.) Springer-Verlag, Berlin.

Kohonen T. (1995), Self-Organizing Maps Springer series in information Sciences Springer-Verlag Berlin.

Moore B. , (1989), ART 1 and Pattern Clustering. In Touretzky D. et al (Eds.), Proceedings of the 1988 Connectionist Models Summer School, (174–185) San Mateo, CA, Morgan Kaufmann Publishers.

Myers C. E. (1992) Delay Learning in Artificial neural Networks, Chapman and Hall, London.

Sammut C. Cribb J. (1990), Is Learning Rate a Good Performance Criterion for Learning? Proceedings of the Seventh International Workshop On Machine Learning. Morgan Kaufmann 170–178.

Saridis G. N. (1989), Analytic Formulation of the Principle of Increasing Precision with Decreasing Intelligence for Intelligent Machines. Automatica 25, 3, 461–467.

Sharkey N. E. Sharkey A. J. C. (1994), Understanding Catastrophic Interference in Neural Nets. Research Report CS-94-4, University of Sheffield, U. K.

Sutton R. S. (1988), Learning to Predict by the Methods of Temporal differences, Machine Learning, 3, 9–44.

Sutton R. S. Barto A. G. (1981), Towards a Modern Theory of Adaptive Networks: Expectation and Prediction, Psychological Review, 88 (2), 135–170.

Sutton R. S. Barto A. G. Williams R. J. (1992), Reinforcement Learning is Direct Adaptive Optimal control, IEEE Control Systems Magazine, April, 19–22.

Watkins C.J.C.H (1989) Ph.D. Thesis University of Cambridge.

Widrow B Smith F. W. (1963). Pattern recognizing Control Systems. Computer Information Sciences (COINS) Symposium.

Widrow B. (1987) The Original Adaptive Neural Net Broom-balancer. International Symposium on Circuits and Systems. New York: IEEE. 351–357.