Skip to main content
SpringerLink
Log in

Preliminary Considerations for a Quantitative Theory of Networked Embodied Intelligence

  • Chapter
50 Years of Artificial Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4850))

Abstract

This paper exposes and discusses the concept of ’networked embodied cognition’, based on natural embodied neural networks, with some considerations on the nature of natural collective intelligence and cognition, and with reference to natural biological examples, evolution theory, neural network science and technology results, network robotics. It shows that this could be the method of cognitive adaptation to the environment most widely used by living systems and most fit to the deployment of artificial robotic networks. Some preliminary ideas about the development of a quantitative framework are shortly discussed. On the basis of the work of many people a few approximate simple quantitative relations are derived between information metrics of the phase space behavior of the agent dynamical system and those of the cognition system perceived by an external observer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Shannon, C.E.: The Mathematical Theory of Communication, Bell Sys. Tech. J. 27, 379, 623 (1948)

    Google Scholar 

  2. Kolmogorov, A.N.: Three approaches to the quantitative definition of information. Problems Inform. Transmission 1(1), 1–7 (1965)

    MathSciNet  Google Scholar 

  3. Chaitin, G.J.: On the length of programs for computing finite binary sequences: statistical considerations. J. Assoc. Comput. Mach. 16, 145–159 (1969)

    MATH  MathSciNet  Google Scholar 

  4. Brooks, R.: A Robust Layered Control System for A Mobile Robot. IEEE Journal of Robotics and Automation (1986)

    Google Scholar 

  5. Pfeifer, R.: Cheap designs: exploiting the dynamics of the system-environment interaction. Three case studies on navigation. In: Conference on Prerational Intelligence — Phenomonology of Complexity Emerging in Systems of Agents Interacting Using Simple Rules. Center for Interdisciplinary Research, University of Bielefeld, pp. 81–91 (1993)

    Google Scholar 

  6. Pfeifer, R., Iida, F.: Embodied artificial intelligence: Trends and challenges. In: Iida, F., Pfeifer, R., Steels, L., Kuniyoshi, Y. (eds.) Embodied Artificial Intelligence. LNCS (LNAI), vol. 3139, pp. 1–26. Springer, Heidelberg (2004)

    Google Scholar 

  7. Touchette, H., Lloyd, S.: Information-theoretic approach to the study of control systems. Physica A 331, 140–172 (2004)

    Article  MathSciNet  Google Scholar 

  8. Brudno, A.A.: Entropy and the Complexity of the Trajectories of a Dynamical System. Transactions of the Moscow Mathematical Society, Moscow (1983)

    Google Scholar 

  9. Bak, P., Tang, C., Wiesenfeld, K.: Self-Organized Criticality: An Explanation of 1/f Noise. Phys. Rev. Letter 59(4), 381–384 (1987)

    Article  MathSciNet  Google Scholar 

  10. Gomez, G., Lungarella, M., Tarapore, D.: Information-theoretic approach to embodied category learning. In: Proc. of 10th Int. Conf. on Artificial Life and Robotics, pp. 332–337 (2005)

    Google Scholar 

  11. Philipona, D., O’ Regan, J.K., Nadal, J.-P, Coenen, O.J.-M.D.: Perception of the structure of the physical world using unknown multimodal sensors and effectors. In: Advances in Neural Information Processing Systems (2004)

    Google Scholar 

  12. Olsson, L., Nehaiv, C.L., Polani, D.: Information Trade-Offs and the Evolution of Sensory Layouts. In: Proc. Artificial Life IX (2004)

    Google Scholar 

  13. Gacs, P.: The Boltzmann Entropy and Randomness Tests. In: Proc. 2nd IEEE Workshop on Physics and Computation (PhysComp 1994), pp. 209–216 (1994)

    Google Scholar 

  14. Gruenwald, P., Vitanyi, P.: Shannon Information and Kolmogorov Complexity. IEEE Transactions on Information Theory (2004)

    Google Scholar 

  15. Garcia, M., Chatterjee, A., Ruina, A., Coleman, M.: The Simplest Walking Model: Stability, Complexity, and Scaling, Transactions of the ASME. Journal of Biomechanical Engineering 120, 281–288 (1998)

    Google Scholar 

  16. Lloyd, S.: Use of mutual information to decrease entropy: Implication for the second law of thermodynamics. Phys. Rev. A 39(10), 5378–5386 (1989)

    Article  MathSciNet  Google Scholar 

  17. Lloyd, S.: Measures of Complexity: A Non exhaustive List. IEEE Control Systems Magazine (2001)

    Google Scholar 

  18. Wiener, N.: Cybernetics: or Control and Communication in the Animal and the Machine. MIT Press, Cambridge, MA (1948)

    Google Scholar 

  19. Maxwell, J.C.: Theory of Heat. Appleton, London (1871)

    Google Scholar 

  20. Rosenblatt, F.: The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory. Psychological Review 65(6), 386–408 (1958)

    Article  MathSciNet  Google Scholar 

  21. Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Networks 2, 359–366 (1989)

    Article  Google Scholar 

  22. Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2, 183–192 (1989)

    Article  Google Scholar 

  23. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc.of the IEEE Intl. Conf. On Neural Network, vol. 4, pp. 1942–1948. IEEE, Washington DC, USA (1995)

    Chapter  Google Scholar 

  24. Millonas, M.M.: Swarms, Phase transitions, and Collective Intelligence. In: Langton, C.G. (ed.) Artificial Life III. Santa Fe Institute Studies in the Sciences of the Complexity, vol. XVII, pp. 417–445. Addison-Wesley, Reading (1994)

    Google Scholar 

  25. Albert, R., Barabasi, A.L.: Statistical physics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)

    Article  MathSciNet  Google Scholar 

  26. Steels, L.: Semiotic dynamics for embodied agents. IEEE Intelligent Systems 32–38 (2006)

    Google Scholar 

  27. Baronchelli, A., Felici, M., Caglioti, E., Loreto, V., Steels, L.: Sharp Transitions towards Shared Vocabularies in Multi-Agent Systems, arxiv.org/pdf/physics/0509075 (2005)

    Google Scholar 

  28. Steels, L.: The Talking Heads Experiment. In: Words and Meanings, vol. 1 Laboratorium, Antwerpen (1999)

    Google Scholar 

  29. Bianconi, G., Barabasi, A.L.: Competition and multiscaling in evolving networks, arXiv:cond-mat/0011029 (2000)

    Google Scholar 

  30. Morpurgo, D., Serenità, R., Seiden, P., Celada, F.: Modelling thymic functions in a cellular automaton. International Immunology 7/4, 505–516 (1995)

    Article  Google Scholar 

  31. Turing, A.M.: Computing machinery and intelligence. Mind 59, 433–460 (1950)

    Article  MathSciNet  Google Scholar 

  32. Lungarella, M., Sporns, O.: Mapping Information Flow in Sensorimotor Networks. PLOS Computational Biology 2(10), 1301–1312 (2006)

    Article  Google Scholar 

  33. Piaget, J.: Introduction à l’Épistémologie Génétique (in French), Paris (1950)

    Google Scholar 

  34. Piaget, J., Inhelder, B.: The Growth of Logical Thinking from Childhood to Adolescence. Basic Books, New York (1958)

    Google Scholar 

  35. Merleau-Ponty, M.: Phenomenology of Perception (in French). Gallimard, Paris (1945)

    Google Scholar 

  36. Bateson, G.: Steps to an Ecology of Mind. University Of Chicago Press, Chicago (1972)

    Google Scholar 

  37. Marx, K.: Capital, vol. I (in German), Hamburg (1867)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Mechanics – University of Genova, Heron Robots srl, Via R.C.Ceccardi 1/18, 16121 Genova, Italy

    Fabio P. Bonsignorio

Authors
  1. Fabio P. Bonsignorio
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Max Lungarella Fumiya Iida Josh Bongard Rolf Pfeifer

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Bonsignorio, F.P. (2007). Preliminary Considerations for a Quantitative Theory of Networked Embodied Intelligence. In: Lungarella, M., Iida, F., Bongard, J., Pfeifer, R. (eds) 50 Years of Artificial Intelligence. Lecture Notes in Computer Science(), vol 4850. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77296-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-77296-5_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-77295-8

  • Online ISBN: 978-3-540-77296-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation