Abstract
Self-organisation is pervasive: neuronal ensembles self-organise into complex spatio-temporal spike patterns which facilitate synaptic plasticity and long-term consolidation of information; large-scale natural or social systems, as diverse as forest fires, landslides, or epidemics, produce spontaneous scale-invariant behaviour; robotic modules self-organise into coordinated motion patterns; individuals within a swarm achieve collective coherence out of isolated actions; and so on. Selforganisation is also valuable: the resultant increase in an internal organisation brings benefits to the (collective) organism, be it a learning brain, a co-evolving ecosystem, an adapting modular robot, or a re-configuring swarm. These benefits are typically realised in increased resilience to external disturbances, adaptivity to novel tasks, and scalability with respect to new challenges. However, self-organisation is difficult to engineer on demand: the intricate fabric of interactions within a self-organising system cannot follow a simple-minded blueprint and resists crude interventions.
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References
Ay, N., Bernigau, H., Der, R., Prokopenko, M.: Information-driven self-organization: the dynamical system approach to autonomous robot behavior. Theory in Biosciences 131, 161–179 (2012)
Ay, N., Bertschinger, N., Der, R., Güttler, F., Olbrich, E.: Predictive information and explorative behavior of autonomous robots. European Journal of Physics B 63, 329–339 (2008)
Ay, N., Der, R., Prokopenko, M.: Guided self-organization: perception-action loops of embodied systems. Theory in Biosciences, 1–3 (2011)
Beer, R.: Dynamical systems and embedded cognition, ch. 12. Cambridge University Press (2013)
Beer, R.: Dynamical analysis of evolved agents: A primer. MIT Press (2014)
Bell, A.J., Sejnowski, T.J.: An information-maximisation approach to blind separation and blind deconvolution. Neural Computation 7, 1129–1159 (1995)
Bialek, W., Nemenman, I., Tishby, N.: Predictability, complexity and learning. Neural Computation 13, 2409–2463 (2001)
Butko, N.J., Triesch, J.: Exploring the role of intrinsic plasticity for the learning of sensory representations. In: ESANN 2006 Proceedings - 14th European Symposium on Artificial Neural Networks Bruges. Neurocomputing., pp. 467–472 (2005)
Capdepuy, P., Polani, D., Nehaniv, C.: Maximization of potential information flow as a universal utility for collective behaviour. In: 2007 IEEE Symposium on Artificial Life, pp. 207–213. IEEE (2007)
Crooks, G.: Measuring thermodynamic length. Physical Review Letters, 99(10), 100602+ (2007)
Crutchfield, J.P.: Computational mechanics: Natural computation and self-organization. In: Calude, C.S., Costa, J.F., Dershowitz, N., Freire, E., Rozenberg, G. (eds.) UC 2009. LNCS, vol. 5715, pp. 3–3. Springer, Heidelberg (2009)
Der, R., Martius, G.: The Playful Machine - Theoretical Foundation and Practical Realization of Self-Organizing Robots. Springer (2012)
Dewar, R.: Information theory explanation of the fluctuation theorem, maximum entropy production and self-organized criticality in non-equilibrium stationary states. J. Phys. A: Math. Gen. 36(3), 631–641 (2003)
Dewar, R.: Maximum entropy production and the fluctuation theorem. J. Phys. A: Math. Gen. 38, 371–381 (2005)
Dini, P., Nehaniv, C.L., Egri-Nagy, A., Schilstra, M.J.: Exploring the concept of interaction computing through the discrete algebraic analysis of the Belousov-Zhabotinsky reaction. Biosystems 112(2), 145–162 (2013)
Egri-Nagy, A., Nehaniv, C.L.: Symmetries of automata. In: Dömösi, P., Iván, S. (eds.) AFL, p. 391 (2011)
Friston, K.: The free-energy principle: a rough guide to the brain? Trends Cogn. Sci. 13(7), 293–301 (2009)
Gershenson, C.: Guiding the self-organization of random boolean networks. Theory in Biosciences 131(3), 181–191 (2012)
Grinstein, G., Linsker, R.: Comments on a derivation and application of the ‘maximum entropy production’ principle. J. Phys. A: Math. Theor. 40, 9717–9720 (2007)
Gros, C.: Complex and adaptive dynamical systems: a primer. Springer, Heidelberg (2008)
Jung, T., Polani, D., Stone, P.: Empowerment for continuous agent-environment systems. Adaptive Behaviour 19(1), 16–39 (2011)
Klyubin, A.S., Polani, D., Nehaniv, C.L.: All else being equal be empowered. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 744–753. Springer, Heidelberg (2005a)
Klyubin, A.S., Polani, D., Nehaniv, C.L.: Empowerment: a universal agent-centric measure of control. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 1, pp. 128–135. IEEE (2005b)
Langton, C.G.: Computation at the edge of chaos: phase transitions and emergent computation. Physica D 42(1-3), 12–37 (1990)
Lazar, A., Pipa, G., Triesch, J.: The combination of STDP and intrinsic plasticity yields complex dynamics in recurrent spiking networks. In: ESANN 2006 Proceedings - 14th European Symposium on Artificial Neural Networks Bruges, pp. 647–652 (2006)
Linsker, R.: Self-organization in a perceptual network. Computer 21(3), 105–117 (1988)
Lizier, J.T., Flecker, B., Williams, P.L.: Towards a synergy-based approach to measuring information modification. In: IEEE Symposium Series on Computational Intelligence (SSCI 2013) — IEEE Symposium on Artificial Life, Singapore. IEEE Press (April 2013)
Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: Local information transfer as a spatiotemporal filter for complex systems. Physical Review EÂ 77(2), 026110 (2008)
Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: Information modification and particle collisions in distributed computation. Chaos 20(3), 037109 (2010)
Lizier, J.T., Prokopenko, M., Zomaya, A.Y.: Local measures of information storage in complex distributed computation. Information Sciences 208, 39–54 (2012)
Lungarella, M., Sporns, O.: Map** information flow in sensorimotor networks. PLoS Comput. Biol. 2(10), e144 (2006)
Martius, G., Der, R., Ay, N.: Information driven self-organization of complex robotic behaviors. PLoS ONEÂ 8(5), e63400 (2013)
Piraveenan, M., Prokopenko, M., Zomaya, A.Y.: Local assortativity and growth of Internet. European Physical Journal B 70(2), 275–285 (2009)
Piraveenan, M., Prokopenko, M., Zomaya, A.Y.: Assortative mixing in directed biological networks. IEEE/ACM Trans. Comput. Biology Bioinform. 9(1), 66–78 (2012)
Polani, D.: Information: currency of life? HFSP Journal 3(5), 307–316 (2009)
Polani, D., Prokopenko, M., Yaeger, L.S.: Information and self-organization of behavior. Advances in Complex Systems (ACS)Â 16(02) (2013)
Polani, D., Sporns, O., Lungarella, M.: How information and embodiment shape intelligent information processing. In: Lungarella, M., Iida, F., Bongard, J.C., Pfeifer, R. (eds.) 50 Years of Aritficial Intelligence. LNCS (LNAI), vol. 4850, pp. 99–111. Springer, Heidelberg (2007)
Prokopenko, M.: Guided self-organization. HFSP Journal 3(5), 287–289 (2009)
Prokopenko, M., Boschetti, F., Ryan, A.J.: An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15(1), 11–28 (2009)
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 (2006a)
Prokopenko, M., Gerasimov, V., Tanev, I.: Measuring spatiotemporal coordination in a modular robotic system. In: Rocha, L.M., Yaeger, L.S., Bedau, M.A., Floreano, D., Goldstone, R.L., Vespignani, A. (eds.) Proceedings of the 10th International Conference on the Simulation and Synthesis of Living Systems (ALifeX), Bloomington, Indiana, USA, pp. 185–191. MIT Press (2006b)
Prokopenko, M., Lizier, J.T., Obst, O., Wang, X.R.: Relating Fisher information to order parameters. Physical Review EÂ 84(4), 041116 (2011)
Prokopenko, M., Lizier, J.T., Price, D.C.: On thermodynamic interpretation of transfer entropy. Entropy 15(2), 524–543 (2013)
Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52(3), 1059–1069 (2010)
Salge, C., Glackin, C., Polani, D.: Approximation of empowerment in the continuous domain. Advances in Complex Systems 16(1/2), 1250079 (2012)
Schreiber, T.: Measuring information transfer. Physical Review Letters 85(2), 461–464 (2000)
Still, S.: Information-theoretic approach to interactive learning. EPL (Europhysics Letters) 85(2), 28005–28010 (2009)
Still, S., Sivak, D.A., Bell, A.J., Crooks, G.E.: Thermodynamics of prediction. Phys. Rev. Lett. 109, 120604 (2012)
Tishby, N., Polani, D.: Information theory of decisions and actions. In: Cutsuridis, V., Hussain, A., Taylor, J. (eds.) Perception-Action Cycle: Models, Architecture and Hardware, pp. 601–636. Springer (2011)
Touchette, H., Lloyd, S.: Information-theoretic limits of control. Phys. Rev. Lett. 84, 1156 (2000)
Triesch, J.: A gradient rule for the plasticity of a neuron’s intrinsic excitability. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 65–70. Springer, Heidelberg (2005)
Williams, P.L., Beer, R.D.: Information dynamics of evolved agents. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, J.-A., Mouret, J.-B. (eds.) SAB 2010. LNCS, vol. 6226, pp. 38–49. Springer, Heidelberg (2010)
Wissner-Gross, A.D., Freer, C.E.: Causal entropic forces. Phys. Rev. Lett. 110, 168702 (2013)
Yaeger, L.S.: Identifying neural network topologies that foster dynamical complexity. Advances in Complex Systems (ACS)Â 16(02) (2013)
Zahedi, K., Ay, N., Der, R.: Higher coordination with less control – A result of information maximization in the sensorimotor loop. Adaptive Behavior 18(3-4), 338–355 (2010)
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Prokopenko, M., Polani, D., Ay, N. (2014). On the Cross-Disciplinary Nature of Guided Self-Organisation. In: Prokopenko, M. (eds) Guided Self-Organization: Inception. Emergence, Complexity and Computation, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53734-9_1
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