Abstract
We have seen that new techniques allow to design automata networks, capable of learning how to solve problems of high order. Those techniques clearly go beyond the limitations of the perceptron and are very different, in their spirit, from the techniques usually used in Artificial Intelligence.
They are based on a representation of knowledge which is distributed, fault tolerant, and an inference mechanism which is dynamical. These aspects make this approach look more like the way the brain works than the usual Al approach.
In the case of the learning-from-examples problem, it is possible to envision these methods as capable of automatically generate predicates characteristic of the set of examples, which would allow to use them as first steps in more classical Al systems (Expert systems for example).
Preview
Unable to display preview. Download preview PDF.
7. References
D.H. ACKLEY, O.E. HINTON, T.J. SEJNOWSKI: A Learning Algorithm for Boltzmann Machines. Cognitive Science, 9, pp 147–169, 1985.
J.A. ANDERSON: Cognitive Capabilities of a Parallel System. In [3], pp 209–226.
E. BIENENSTOCK, F. FOGELMAN SOULIE, G. WEISBUCH Eds: «Disordered systems and biological organization», Springer-Verlag, NATO Asi Series in Systems and Computer Science, F20, 1986.
R.O. DUDA, P.E. HART: Pattern classification and scene analysis. Wiley, 1973.
J.A. FELDMAN: Connections. Byte, pp 277–283, april 1985.
F. FOGELMAN SOULIE: Pattern Recognition by Threshold Networks. In «Actes du Colloque International d'Intelligence Artificielle, Marseille», 1984.
F. FOGELMAN SOULIE: Brains and Machines: architectures for to-morrow? «Cognitive 85», CESTA-AFCET Ed., forum, (in french), 1985.
F. FOGELMAN SOULIE, E. GOLES-CHACC: Knowledge representation by automata networks. In «Computers and Computing», P. Chenin, C. di Crescenzo, F. Robert Eds, Masson-Wiley, pp 175–180, 1986.
F. FOGELMAN SOULIE, G. WEISBUCH: Random iterations of threshold networks and associative memory. SIAM J. on Computing, to appear.
F. FOGELMAN SOULIE, P. GALLINARI, S. THIRIA: Learning and associative memory. In «Pattern Recognition, Theory and Applications», P.A. Devijver Ed., NATO ASI Series in Computer Science, Springer-Verlag, to appear.
F. FOGELMAN SOULIE, P. GALLINARI, Y. LE CUN, S. THIRIA: Automata Networks and Artificial Intelligence. In «Computing on Automata Networks», F. Fogelman Soulié, Y. Robert, M. Tchuente Eds, Manchester Univ. Press, to appear.
E. GOLES-CHACC: Comportement Dynamique de résaaux d'Automates. Thesis, Grenoble, 1985.
E. GOLES-CHACC: this volume.
T.N.E. GREVILLE: Some applications of the pseudo inverse of a matrix. SIAM Rev. 2, pp 15–22, 1960.
D.O. HEBB: The Organization of Behavior. Wiley, 1949.
G.E. HINTON: Learning in Parallel Networks. Byte, pp 265–273, april 1985.
G.E. HINTON, J.A. ANDERSON (Eds): Parallel Models of Associative Memory. Hillsdale, Erlbaum, 1981.
J.J. HOPFIELD: Neural Networks and Physical Systems with Emergent Collective Computational Abilities, P.N.A.S. USA, vol 79, pp 2554–2558, 1982.
T. KOHONEN: Self-Organization and Associative Memory. Springer Series in Information Sciences, vol 8, Springer-Verlag, 1984.
Y. LE CUN: A learning scheme for assymetric threshold network. In «Cognitive 85», CESTA-AFCET Ed., pp 599–604, 1985.
Y. LE CUN: Learning process in an asymmetric threshold network. In «Disordered systems and biological organization», E. Bienenstock, F. Fogelman Soulié, G. Weisbuch Eds, Springer-Verlag, NATO Asi series in systems and computer science, no20, pp 233–240, 1986.
Y. LE CUN, F. FOGELMAN SOULIE: Modèles Connexionnistes de l'Apprentissage. Special issue on "Apprentissage et Machine", Intellectica, to appear.
W. SMAC CULLOCH, W. PITTS: A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophysics, 5, pp 115–133, 1943.
R.S. MICHALSKI, L.G. CARBONELL, T.M. MITCHELL: Machine Learning. Tioga, 1983.
M. MINSKY, S. PARERT: Perceptrons, an Introduction to Computational Geometry. Cambridge, MIT Press, 1969.
J. von NEUMANN: Theory of self reproducing automata. A.W. Burks Ed. Univ. Illinois Press, 1966.
D.C. PLAUT, S.J. NOWLAN, G.E. HINTON: Experiments on Learning by Back Propagation. Carnegie Mellon University Technical Report, CMU-CS-86-126, 1986.
F. ROSENBLATT: Principles of Neurodynamics. Sparton, 1962.
D.E. RUMELHART, G.E. HINTON, R.J. WILLIAMS: Learning internal representations by error propagation. In [30].
D.E. RUMELHART, J.L. MAC CLELLAND Eds: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, Foundations. MIT Press, 1986.
D.E. RUMELHART, J.L. MAC CLELLAND: On learning the past tenses of english verbs. In [30].
T.J. SEJNOWSKI, G.E. HINTON: Separating figure from ground with a Boltzmann machine. In «Vision, brain and cooperative computation». Arbib M.A., Hanson A.R. Eds, Cambridge, MIT Press, 1985.
T.J. SEJNOWSKI, P.K. KIENKER, G.E. HINTON: Learning symmetry groups with hidden units: beyond the perceptron. to appear in Physica D.
T.J. SEJNOWSKI, C.H. ROSENBERG: NETtalk: a parallel network that learns to read aloud. Johns Hopkins Technical report JHU/EECS-86/01.
D.L. WALTZ, J.B. POLLACK: Massively Parallel Parsing: a Strongly Interactive Model of Natural Language Interpretation. Cognitive Science. no 9, pp 51–74, 1985.
G. WEISBUCH, F. FOGELMAN SOULIE: Scaling laws for the attractors of Hopfield networks. J. Phys. Lett., 46, 623–630, 1985.
B. WIDROW, M.E. HOFF: Adaptive switching circuits. IRE WESCON Conv. Record, part 4, pp 96–104, 1960.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1988 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Soulie, F.F. (1988). Representation of knowledge and learning on automata networks. In: Choffrut, C. (eds) Automata Networks. LITP 1986. Lecture Notes in Computer Science, vol 316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-19444-4_17
Download citation
DOI: https://doi.org/10.1007/3-540-19444-4_17
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-19444-6
Online ISBN: 978-3-540-39270-5
eBook Packages: Springer Book Archive