A GROWING FUNCTIONAL MODULE DESIGNED TO TRIGGER CAUSAL INFERENCE

Jérôme Leboeuf Pasquier

2007

Abstract

“Growing Functional Modules” constitutes a prospective paradigm founded on the epigenetic approach whose proposal consists in designing a distributed architecture, based on interconnected modules, that allows the automatic generation of an autonomous and adaptive controller (artificial brain). The present paper introduces a new module designed to trigger causal inference; its functionality is discussed and its behavior is illustrated applying the module to solve the problem of a dynamic maze.

References

  1. Piaget, J., 1970. Genetic Epistemology. Series of Lectures, Columbia University Press, Columbia, New York.
  2. Wai-Ki, C. & Michael, K.N., 2006. Markov Chains: Models, Algorithms and Applications. Springer Eds.
  3. Jansen, F.V., 2001. Bayesian Networks and Decision Graphs, Springer Eds.
  4. Leboeuf, J., 2005. Growing Functional Modules, a Prospective Paradigm for Epigenetic Artificial Intelligence. Lecture Notes in Computer Science 3563. Springer, pp. 465-471.
  5. Leboeuf, J., 2006. A Growing Functional Module Designed to Perform Basic Real Time Automatic Control. Publication pending in Lecture Notes in Computer Science, Springer.
  6. Leboeuf, J., 2006. Improving the RTR Growing Functional Module. Proceedings of the 32nd Annual Conference of the IEEE Industrial Electronics Society, IECON'06 Paris, pp. 3945-3950.
  7. Leboeuf, J., 2005. Applying the GFM Prospective Paradigm to the Autonomous and Adaptive Control of a Virtual Robot. Lecture Notes in Artificial Intelligence, Springer 3789, pp. 959-969.
  8. Dijkstra, E.W., 1959. A Note on Two Problems in Connexion with Graphs. Numerische Mathematik 1, pp. 269-271.
  9. Fredman, M.L. & Tarjan, R.E., 1987. Fibonacci Heaps and their Use in Improved Network Optimization. Journal of the ACM Vol. 34, pp 596-615.
  10. Matias, Y., Vitter J.S. &Young, N.E., 1994. Approximate Data Structure with Applications. Proceedings of the ACM-SIAM Symposium, pp. 187-194
  11. Cooper, C., Frieze, A., Mehlhorn, K. & Priebe, V., 2000, Average-Case Complexity of Shortest Path Problems in the Vertex-Potential Model.
  12. Anderson, J.R., 1999. Learning and Memory, an Integrated Approach. Wiley Eds.
Download


Paper Citation


in Harvard Style

Leboeuf Pasquier J. (2007). A GROWING FUNCTIONAL MODULE DESIGNED TO TRIGGER CAUSAL INFERENCE . In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-972-8865-82-5, pages 456-463. DOI: 10.5220/0001643704560463


in Bibtex Style

@conference{icinco07,
author={Jérôme Leboeuf Pasquier},
title={A GROWING FUNCTIONAL MODULE DESIGNED TO TRIGGER CAUSAL INFERENCE},
booktitle={Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2007},
pages={456-463},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001643704560463},
isbn={978-972-8865-82-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A GROWING FUNCTIONAL MODULE DESIGNED TO TRIGGER CAUSAL INFERENCE
SN - 978-972-8865-82-5
AU - Leboeuf Pasquier J.
PY - 2007
SP - 456
EP - 463
DO - 10.5220/0001643704560463