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International Work-Conference on Artificial Neural Networks

IWANN 2001: Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence pp 822–829Cite as

Conjecturing the Cognitive Plausibility of an ANN Theorem-Prover

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2084))

Abstract

Many models in Artificial Neural Networks Systems seek inspiration from cognitive and biological mechanisms. Taking the opposite direction, this work conjectures on the cognitive plausibility of a propositional version of neural engine that finds proof by refutation using the Resolution Principle. We construct a parallel between the main characteristics of the computional system and several aspects of theories found in the psychology and neurocognitive literature. This way, the identification of an artificial neural explainer, already hypothesized by psychological and neurocognitive works, is of fundamental contribution to the development of the field of artificial cognition.

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References

  1. DeGregorio, M.: Integrating Inference and neural classification in a hybrid system for recognition tasks. Mathware & Soft Computing, Vol 3 (1996) 271–279

    Google Scholar 

  2. Gazzaniga, M. S., LeDoux, J. E.: The Integrated Mind. Plenum Press, New York (1979)

    Google Scholar 

  3. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, PAMI-6 (1984) 721–741

    Google Scholar 

  4. Grossberg, S.: The Attentive Brain. American Scientist 83 (1995) 438–449

    Google Scholar 

  5. Grossberg, S.: The Complementary Brain: A Unifying View of Brain Specialization and Modularity. Technical Report CAS/CNS-TR-98-003 (2000)

    Google Scholar 

  6. Hopfield, J. J.: Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences USA, 79 (1982) 2554–2558.

    Article  MathSciNet  Google Scholar 

  7. Kandel, E. R., Schwartz, J. H. Jessell, T. M. (eds.): Principles of Neural Sciences 4th edition. New York: McGraw-Hill. (2000)

    Google Scholar 

  8. Lima P. M. V.: Resolution-based Inference on Artificial neural Networks. Ph.D. Thesis, Department of Computing, Imperial College of Science, Technology and Medicine, London, UK (2000a).

    Google Scholar 

  9. Lima P. M. V.: A Neural Propositional Reasoner that is Goal-Driven and Works without Pre-Compiled Knowledge In: Proccedings of the VIth Brazilian Symposium on Neural Networks, Rio de Janeiro, RJ, Brazil, (2000b) 261–266

    Google Scholar 

  10. Luria, A. R.: Higher Cortical Functions in Man. Basic Books, Inc., Publishers, New York (1980)

    Google Scholar 

  11. Muggleton, S.: Inductive Logic Programming. New Generation Computing, Vol. 8 (1991) 295–318

    Article  MATH  Google Scholar 

  12. Piaget, J.: De la Logique de l’Enfant à Logique de l’Adolescent Press Universitaires de France (1970)

    Google Scholar 

  13. Piaget, J., Fraisse, P. (eds.): Traité de Psychologie Experimentale. Vol VII: l’Intelligence (1963)

    Google Scholar 

  14. Piaget, J.: Inconscient Affectif et Inconscient Cognitif. In: Problémes de Psychologie Génetique. éditions Denoel Paris (1972).

    Google Scholar 

  15. Piaget, J.: The Psychology of Intelligence. New York: Harcourt Brace & Co.. (1950)

    Google Scholar 

  16. Pinkas, G.: Energy minimization and the satisfiability of propositional calculus. Neural Computation Vol. 3, Morgan Kaufmann (1991) 282–291.

    Article  Google Scholar 

  17. Pinkas, G.: Reasoning, Non-Monotonicity and Learning in Connectionist Networks that Capture Propositional Knowledge. Artificial Intelligence, Elsevier Science Publishers, 77, (1995) 203–247.

    MATH  MathSciNet  Google Scholar 

  18. Robinson, J. A.: Logic: Form and Function. Edinburgh University Press (1979).

    Google Scholar 

  19. Sejnowski, T. J.: Higher-Order Boltzmann Machines. Proceedings of The American Institute of Physics Vol. 151, Snowbird, Utah (1986).

    Google Scholar 

  20. Vilela, I. M. O.: An Integrated Approach of Visual Computational Modelling. In: Proccedings of the Vith Brazilian Sympoosium on Neural Networks, Rio de Janeiro, RJ, Brazil, (2000) 293

    Chapter  Google Scholar 

  21. Vilela, I. M. O.: Abordagem Integrada para Modelagem Computacional de Percepção Visual. MSc Thesis, Programa de Engeharia de Sistemas e Computação, COPPE-Universidade Federal do Rio de Janeiro, Brazil (1998)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Programa de Engenharia de Sistemas e Computação- COPPE, Universidade Federal do Rio de Janeiro, Caixa postal 68511, 21.945-970, Rio de Janeiro, RJ, Brazil

    Iara M. O. Vilela

  2. Instituto de Computação, Universidade Federal Fluminense, Rua Passo da Pátria 156 - bl.E — sala 350, 24.210-240, Niterói, RJ, Brazil

    Priscila M. V. Lima

Authors
  1. Iara M. O. Vilela
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  2. Priscila M. V. Lima
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Vilela, I.M.O., Lima, P.M.V. (2001). Conjecturing the Cognitive Plausibility of an ANN Theorem-Prover. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_99

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  • DOI: https://doi.org/10.1007/3-540-45720-8_99

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

  • eBook Packages: Springer Book Archive

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