On the Causal Structure of the Sensorimotor Loop

Part of the Emergence, Complexity and Computation book series (ECC, volume 9)

Abstract

In recent years, the application of information theory to the field of embodied intelligence has turned out to be extremely fruitful. Here, several measures of information flow through the sensorimotor loop of an agent are of particular interest. There are mainly two ways to apply information theory to the sensorimotor setting.

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References

  1. Amari, S.: Natural gradient works efficiently in learning. Neural Computation 10(2), 251–276 (1998)CrossRefMathSciNetGoogle Scholar
  2. Ay, N., Bernigau, H., Der, R., Prokopenko, M.: Information driven self-organization: The dynamical system approach to autonomous robot behavior. Theory Biosci. (131), 161–179 (2012)Google Scholar
  3. Ay, N., Bertschinger, N., Der, R., Güttler, F., Olbrich, E.: Predictive information and explorative behavior of autonomous robots. EPJ B 63(3), 329–339 (2008)CrossRefMATHGoogle Scholar
  4. Ay, N., Polani, D.: Information flows in causal networks. Advances in Complex Systems 11(1), 17–41 (2008)CrossRefMATHMathSciNetGoogle Scholar
  5. Bialek, W., Nemenman, I., Tishby, N.: Predictability, complexity, and learning. Neural Computation 13(11), 2409–2463 (2001)CrossRefMATHGoogle Scholar
  6. Crutchfield, J.P., Young, K.: Inferring statistical complexity. Phys. Rev. Lett. 63(2), 105–108 (1989)CrossRefMathSciNetGoogle Scholar
  7. Der, R., Güttler, F., Ay, N.: Predictive information and emergent cooperativity in a chain of mobile robots. In: ALife XI. MIT Press (2008)Google Scholar
  8. Grassberger, P.: Toward a quantitative theory of self-generated complexity. International Journal of Theoretical Physics 25(9), 907–938 (1986)CrossRefMATHMathSciNetGoogle Scholar
  9. Kaiser, A., Schreiber, T.: Information transfer in continuous processes. Physica D: Nonlinear Phenomena 166(1-2), 43–62 (2002)CrossRefMATHMathSciNetGoogle Scholar
  10. Klyubin, A.S., Polani, D., Nehaniv, C.L.: Tracking information flow through the environment: Simple cases of stigmerg. In: Pollack, J. (ed.) Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems, pp. 563–568 (2004)Google Scholar
  11. Klyubin, A.S., Polani, D., Nehaniv, C.L.: Empowerment: A universal agent-centric measure of control. In: Proc. CEC. IEEE (2005)Google Scholar
  12. Lauritzen, S.L.: Graphical Models. Oxford University Press (1996)Google Scholar
  13. Lungarella, M., Sporns, O.: Information self-structuring: Key principle for learning and development. In: IEEE (ed.) Proc. the 4th International Conference on Development and Learning, pp. 25–30. IEEE Press, San Diego (2005)Google Scholar
  14. Lungarella, M., Sporns, O.: Mapping information flow in sensorimotor networks. PLoS Comp. Biol. 2(10), e144 (2006)Google Scholar
  15. Martius, G., Der, R., Ay, N.: Information driven self-organization of complex robotic behaviors. PLOS One 8(5) (2013), doi:10.1371/ journal.pone.0063400Google Scholar
  16. Massey, J.L.: Causality, feedback and directed information. In: Proc. 1990 Intl. Symp. on Info. Th. and its Applications, pp. 27–30 (1990)Google Scholar
  17. Pasemann, F.: Complex dynamics and the structure of small neural networks. Network: Computation in Neural Systems 13(2), 195–216 (2002)MATHGoogle Scholar
  18. Pearl, J.: Causality: Models, Reasoning and Inference. Cambridge University Press (2000)Google Scholar
  19. Pfeifer, R., Bongard, J.C.: How the Body Shapes the Way We Think: A New View of Intelligence. The MIT Press (Bradford Books) (2006)Google Scholar
  20. Polani, D., Nehaniv, C., Martinetz, T., Kim, J.T.: Relevant Information in Optimized Persistence vs. Progeny Strategies. In: Rocha, L.M., Bedau, M., Floreano, D., Goldstone, R., Vespignani, A., Yaeger, L. (eds.) Proc. Artificial Life X, pp. 337–343. MIT Press, Cambridge (2006)Google Scholar
  21. Prokopenko, M., Gerasimov, V., Tanev, I.: Evolving spatiotemporal coordination in a modular robotic system. In: Nolfi, S., Baldassarre, G., Calabretta, R., Hallam, J.C.T., Marocco, D., Meyer, J.-A., Miglino, O., Parisi, D. (eds.) SAB 2006. LNCS (LNAI), vol. 4095, pp. 558–569. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  22. Prokopenko, M., Lizier, J.T., Price, D.C.: On thermodynamic interpretation of transfer entropy. Entropy 15(2), 524–543 (2013)CrossRefMathSciNetGoogle Scholar
  23. Reichenbach, H.: The Direction of Time. University of California Press (1956)Google Scholar
  24. Schreiber, T.: Measuring information transfer. Physical Review Letters 85(2) (2000)Google Scholar
  25. Zahedi, K., Ay, N.: Quantifying morphological computation. Entropy 15(5), 1887–1915 (2013)CrossRefGoogle Scholar
  26. Zahedi, K., Ay, N., Der, R.: Higher coordination with less control – a result of information maximization in the sensori-motor loop. Adaptive Behavior 18(3-4), 338–355 (2010)CrossRefGoogle Scholar
  27. Zahedi, K., von Twickel, A., Pasemann, F.: YARS: A physical 3D simulator for evolving controllers for real robots. In: Carpin, S., Noda, I., Pagello, E., Reggiani, M., von Stryk, O. (eds.) SIMPAR 2008. LNCS (LNAI), vol. 5325, pp. 75–86. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Max Planck Institute for Mathematics in the SciencesLeipzigGermany
  2. 2.Santa Fe InstituteSanta FeUSA

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