On the Complex Behaviour of Natural and Artificial Machines and Systems

  • H. ZenilEmail author
Part of the Cognitive Systems Monographs book series (COSMOS, volume 36)


One of the most important aims of the fields of robotics, artificial intelligence and artificial life is the design and construction of systems and machines as versatile and as reliable as living organisms at performing high level human-like tasks. But how are we to evaluate artificial systems if we are not certain how to measure these capacities in living systems, let alone how to define life or intelligence? Here I survey a concrete metric towards measuring abstract properties of natural and artificial systems, such as the ability to react to the environment and to control one’s own behaviour.


Natural computing Systems’ behaviour Controllability Programmability Turing test Compressibility Kolmogorov complexity Randomness Robotics Artificial life 


  1. 1.
    Zenil, H., Gershenson, C., Marshall, J.A.R., Rosenblueth, D.: Life as thermodynamic evidence of algorithmic structure in natural environments. Entropy 14(11), 2173–2191 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Ciresan, D.C., Meier, U., Masci, J., Schmidhuber, J.: Multi-column deep neural network for traffic sign classification. Neural Netw. 32, 333–338 (2012)CrossRefGoogle Scholar
  3. 3.
    Wolfram, S.: A New Kind of Science. Wolfram Media (2002)Google Scholar
  4. 4.
    Cook, M.: Universality in elementary cellular automata. Complex Syst. 15, 1–40 (2004)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Zenil, H.: Compression-based investigation of the behaviour of cellular automata and other systems. Complex Syst. 19(2) (2010)Google Scholar
  6. 6.
    Perlis, A.J.: Epigrams on programming. SIGPLAN Not. 17(9), 7–13 (1982)CrossRefGoogle Scholar
  7. 7.
    Cronin, L., Krasnogor, N., Davis, B.G., Alexander, C., Robertson, N., Steinke, J.H.G., Schroeder, S.L.M., Khlobystov, A.N., Cooper, G., Gardner, P.M., Siepmann, P., Whitaker, B.J., Marsh, D.: The imitation game—a computational chemical approach to recognizing life. Nat. Biotechnol. 24, 1203–1206 (2006)Google Scholar
  8. 8.
    Zenil, H., Ball, G., Tegnér, J.: Testing biological models for non-linear sensitivity with a programmability test. In: Liò, P., Miglino, O., Nicosia, G., Nolfi, S., Pavone, M. (eds.) Advances in Artificial Intelligence, ECAL 2013, pp. 1222–1223. MIT Press, Cambridge (2013).
  9. 9.
    Maier, R., Zimmer, R., Kü ffner, R.: A Turing test for artificial expression data. Bioinformatics 29(20), 2603–2609 (2013)Google Scholar
  10. 10.
    Zenil, H.: What is nature-like computation? A behavioural approach and a notion of programmability. Philos. Technol. (2012). Scholar
  11. 11.
    Zenil, H.: A turing test-inspired approach to natural computation. In: Primiero, G., De Mol, L. (eds.) Turing in Context II, Historical and Contemporary Research in Logic, Computing Machinery and Artificial Intelligence. Proceedings by the Royal Flemish Academy of Belgium for Science and the Arts, Belgium (2013)Google Scholar
  12. 12.
    Osawa, H., Tobita, K., Kuwayama, Y., Imai, M., Yamada, S.: Behavioral turing test using two-axis actuators. In: IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication (2012)Google Scholar
  13. 13.
    Chaitin, G.J.: On the length of programs for computing finite binary sequences: statistical considerations. J. ACM 16(1), 145–159 (1969)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Kolmogorov, A.N.: Three approaches to the quantitative definition of information. Probl. Inf. Trans. 1(1), 1–7 (1965)MathSciNetGoogle Scholar
  15. 15.
    Delahaye, J.-P., Zenil, H.: Numerical evaluation of the complexity of short strings: a glance into the innermost structure of algorithmic randomness. Appl. Math. Comput. 219, 63–77 (2012)zbMATHGoogle Scholar
  16. 16.
    Soler-Toscano, F., Zenil, H., Delahaye, J.-P., Gauvrit, N.: Calculating Kolmogorov complexity from the output frequency distributions of small turing machines. PLoS ONE 9(5), e96223 (2014)CrossRefGoogle Scholar
  17. 17.
    Zenil, H.: On the dynamic qualitative behaviour of universal computation. Complex Syst. 20(3) (2012)Google Scholar
  18. 18.
    Zenil, H.: Programmability for natural computation and the game of life as a case study. J. Exp. Theor. Artif. Intell. (in press)
  19. 19.
    Floridi, L.: Enveloping the world: risks and opportunities in the development of increasingly smart technologies. CONNECT (ed.), 03 Jun 2011. Accessed 15 July 2014
  20. 20.
    Prokopenko, X., Gerasimov, V., Tanev, I.: Measuring spatiotemporal coordination in a modular robotic system. In: Proceedings of Artificial Life X (2006)Google Scholar
  21. 21.
    Levin, L.: Laws of information conservation (non-growth) and aspects of the foundation of probability theory. Probl. Inf. Trans. 10(3), 206–210 (1974)Google Scholar
  22. 22.
    Terrazas, G., Zenil, H., Krasnogor, N.: Exploring programmable self-assembly in non DNA-based computing. Nat. Comput. 12(4), 499–515 (2013)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Gauvrit, N., Zenil, H., Soler-Toscano, F., Delahaye, J.-P.: Algorithmic complexity for short binary strings applied to psychology: a primer, Behavior Research Methods, 6 Dec 2013 (epub ahead of print)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Unit of Computational MedicineStockholmSweden
  2. 2.Algorithmic Nature Group, LABoRESParisFrance

Personalised recommendations