All Else Being Equal Be Empowered

  • Alexander S. Klyubin
  • Daniel Polani
  • Chrystopher L. Nehaniv
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3630)


The classical approach to using utility functions suffers from the drawback of having to design and tweak the functions on a case by case basis. Inspired by examples from the animal kingdom, social sciences and games we propose empowerment, a rather universal function, defined as the information-theoretic capacity of an agent’s actuation channel. The concept applies to any sensorimotoric apparatus. Empowerment as a measure reflects the properties of the apparatus as long as they are observable due to the coupling of sensors and actuators via the environment.


Utility Function Mutual Information Channel Capacity Conditional Probability Distribution Average Short Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ashby, W.R.: An Introduction to Cybernetics. Chapman & Hall Ltd., Boca Raton (1956)zbMATHGoogle Scholar
  2. 2.
    Gibson, J.J.: The Ecological Approach to Visual Perception. Houghton Mifflin Company, Boston (1979)Google Scholar
  3. 3.
    Shannon, C.E.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423 (1948)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Cover, T.M., Thomas, J.A.: Elements of Information Theory. John Wiley & Sons, Inc., Chichester (1991)zbMATHCrossRefGoogle Scholar
  5. 5.
    Pearl, J.: Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge (2001)Google Scholar
  6. 6.
    Blahut, R.: Computation of channel capacity and rate distortion functions. IEEE Transactions on Information Theory 18(4), 460–473 (1972)zbMATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Touchette, H., Lloyd, S.: Information-theoretic approach to the study of control systems. Physica A 331(1–2), 140–172 (2004)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Klyubin, A.S., Polani, D., Nehaniv, C.L.: Tracking information flow through the environment: Simple cases of stigmergy. In: Pollack, J., Bedau, M., Husbands, P., Ikegami, T., Watson, R.A. (eds.) Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems, pp. 563–568. The MIT Press, Cambridge (2004)Google Scholar
  9. 9.
    Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method. In: Proceedings of the 37th Annual Allerton Conference on Communication, Control, and Computing, pp. 368–377 (1999)Google Scholar
  10. 10.
    Crutchfield, J.P., Young, K.: Inferring statistical complexity. Physical Review Letters 63(2), 105–108 (1989)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Shalizi, C.R., Crutchfield, J.P.: Information bottlenecks, causal states, and statistical relavance bases: How to represent relevant information in memoryless transduction. Advances in Complex Systems 5(1), 91–95 (2002)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Alexander S. Klyubin
    • 1
  • Daniel Polani
    • 1
  • Chrystopher L. Nehaniv
    • 1
  1. 1.Adaptive Systems Research Group, School of Computer Science, Faculty of Engineering and Information SciencesUniversity of HertfordshireCollege LaneUK

Personalised recommendations