Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baldassarre, G., Parisi, D., and Nolfi, S. (2007). Measuring coordination as entropy decrease in groups of linked simulated robots. In Bar-Yam, Y., editor, Proceedings of the 5th International Conference on Complex Systems (ICCS2004) Google Scholar
  2. Bialek, W., Nemenman, I., and Tishby, N. (2001). Complexity through nonextensivity. Physica A, 302:89–99.MATHCrossRefMathSciNetGoogle Scholar
  3. Bonabeau, E., Theraulaz, G., Deneubourg, J.-L., and Camazine, S. (1997). Self-organisation in social insects. Trends in Ecology and Evolution, 12(5):188–193.CrossRefGoogle Scholar
  4. Boschetti, F., Prokopenko, M., Macreadie, I., and Grisogono, A.-M.(2005). Defining and detecting emergence in complex networks. In Khosla, R., Howlett, R. J., and Jain, L. C., editors, Knowledge-Based Intelligent Information and Engineering Systems, 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, Proceedings, Part IV, volume 3684 of Lecture Notes in Computer Science, pages 573–580. Springer, Berlin.Google Scholar
  5. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., and Bonabeau, E. (2001). Self-Organization in Biological Systems. Princeton University Press, Princeton, NJ.Google Scholar
  6. Casti, J. L. (1991). Chaos, Gödel and Truth. In Casti, J. L. and Karlqvist, A., editors, Beyond Belief: Randomness, Prediction, and Explanation in Science. CRC Press, Boca Raton, Fla.Google Scholar
  7. Correia, L. (2006). Self-organisation: a case for embodiment. In Gershenson, C. and Lenaerts, T., editors, The Evolution of Complexity Workshop at Artificial Life X: Proceedings of the 10th International Conference on the Simulation and Synthesis of Living Systems, pages 111–116.Google Scholar
  8. Crutchfield, J. P. (1994). The calculi of emergence: computation, dynamics, and induction. Physica D, 75:11–54.MATHCrossRefGoogle Scholar
  9. Crutchfield, J. P., Mitchell, M., and Das, R. (1998). The design of collective computation in cellular automata.Technical Report 98-09-080, Santa Fe Institute Working Paper, available at http://www.santafe.edu/projects/evca/Papers/EvDesign.html.Google Scholar
  10. Czap, H., Unland, R., Branki, C., and Tianfield, H. (2005). Self-Organization and Autonomic Informatics (I), volume 135 of Frontiers in Artificial Intelligence and Applications. IOS, Amsterdam.Google Scholar
  11. De Wolf, T., and Holvoet, T. (2005). Emergence versus self-organisation: Different concepts but promising when combined. In Brueckner, S., Serugendo, G. D. M., Karageorgos, A., and Nagpal, R., editors, Engineering Self-Organising Systems, pages 1–15. Springer, Berlin.Google Scholar
  12. Der, R., Steinmetz, U., and Pasemann, F. (1999). Homeokinesis: A new principle to back up evolution with learning. Concurrent Systems Engineering Series, 55:43–47.Google Scholar
  13. Haken, H. (1983a). Advanced Synergetics: Instability Hierarchies of Self-Organizing Systems and Devices. Springer, Berlin.MATHGoogle Scholar
  14. Haken, H. (1983b). Synergetics, an Introduction: Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology, 3rd rev. enl. ed. Springer, New York.MATHGoogle Scholar
  15. Haken, H. (1988). Information and Self-Organization: A Macroscopic Approach to Complex Systems. Springer, Berlin.Google Scholar
  16. Heylighen, F. (2000). Self-organization. In Heylighen, F., Joslyn, C., and Turchin, V., editors, Principia Cybernetica Web. Principia Cybernetica, Brussels, available at http://pespmc1.vub.ac.be/SELFORG.html.Google Scholar
  17. Hofstadter, D. R. (1989). Gödel, Escher, Bach: An Eternal Golden Braid. Vintage, New York.Google Scholar
  18. Hubbell, S. P., Johnson, L. K., Stanislav, E., Wilson, B., andFowler, H. (1980). Foraging by bucket-brigade in leafcutter ants. Biotropica, 12(3):210–213.CrossRefGoogle Scholar
  19. Jirsa, V. K., Jantzen, K. J., Fuchs, A., and Kelso, J. A. (2002). Spatiotemporal forward solution of the EEG and MEG using network modeling. IEEE Transactions on Medical Imaging, 21(5):493–504.CrossRefGoogle Scholar
  20. Kauffman, S. A. (2000). Investigations. Oxford University Press, Oxford.Google Scholar
  21. Klyubin, A. S., Polani, D., and Nehaniv, C. L. (2004). Organization of the information flow in the perception-action loop of evolved agents. In Proceedings of 2004 NASA/DoD Conference on Evolvable Hardware, pages 177–180. IEEE Computer Society.Google Scholar
  22. Klyubin, A. S., Polani, D., and Nehaniv, C. L. (2005). All else being equal be empowered. In Capcarrère, M. S., Freitas, A. A., Bentley, P. J., Johnson, C. G., and Timmis, J., editors, Advances in Artificial Life, 8th European Conference, ECAL 2005, Canterbury, U.K., September 5-9, 2005, Proceedings, volume 3630 of Lecture Notes of Computer Science, pages 744–753. Springer, Berlin.Google Scholar
  23. Kuramoto, Y. (1984). Chemical Oscillations, Waves, and Turbulence. Springer, Berlin.MATHGoogle Scholar
  24. Langton, C. (1991). Computation at the edge of chaos: Phase transitions and emergent computation. In Forest, S., editor, Emergent Computation. MIT Press, Cambridge, MA.Google Scholar
  25. Liljenström, H., and Svedin, U. (2005). System features, dynamics, and resilience — some introductory remarks. In Liljenström, H. and Svedin, U., editors, MICRO-MESO-MACRO: Addressing Complex Systems Couplings, pages 1–16. World Scientific, Singapore.Google Scholar
  26. Miller, J. F., Job, D., and Vassilev, V. K. (2000). Principles in the evolutionary design of digital circuits - Part I. Journal of Genetic Programming and Evolvable Machines, 1(1):8–35.Google Scholar
  27. Mitchell, M., Hraber, P. T., and Crutchfield, J. P. (1993). Revisiting the edge of chaos: evolving cellular automata to perform computations. Complex Systems, 7:89–139.MATHGoogle Scholar
  28. Pikovsky, A., Rosenblum, M., and Kurths, J. (2001). Synchronization: A Universal Concept in Nonlinear Science. Cambridge University Press, Cambridge, UK.MATHGoogle Scholar
  29. Polani, D. (2003). Measuring self-organization via observers. In Banzhaf, W., Christaller, T., Dittrich, P., Kim, J. T., and Ziegler, J., editors, Advances in Artificial life - Proceedings of the 7th European Conference on Artificial Life (ECAL), Dortmund, pages 667–675. Springer, Heidelberg.Google Scholar
  30. Prigogine, I. (1980). From Being to Becoming: Time and Complexity in the Physical Sciences. W. H. Freeman, San Francisco.Google Scholar
  31. Prokopenko, M., Piraveenan, M., and Wang, P. (2005a). On convergence of dynamic cluster formation in multi-agent networks. In Capcarrère, M. S., Freitas, A. A., Bentley, P. J., Johnson, C. G., and Timmis, J., editors, Advances in Artificial Life, 8th European Conference, ECAL 2005, Canterbury, UK, September 5-9, 2005, Proceedings, volume 3630 of Lecture Notes in Computer Science, pages 884–894. Springer.Google Scholar
  32. Prokopenko, M., Wang, P., Foreman, M., Valencia, P., Price, D. C., and Poulton, G. T. (2005b). On connectivity of reconfigurable impact networks in ageless aerospace vehicles. Journal of Robotics and Autonomous Systems, 53(1):36–58.CrossRefGoogle Scholar
  33. Prokopenko, M., Wang, P., and Price, D. C. (2005c). Complexity metrics for self-monitoring impact sensing networks. In Lohn, J., Gwaltney, D., Hornby, G., Zebulum, R., Keymeulen, D., and Stoica, A., editors, Proceedings of 2005 NASA/DoD Conference on Evolvable Hardware (EH-05), Washington D.C., 29 June–1 July 2005, pages 239–246. IEEE Computer Society, Los Alamitos, CA.CrossRefGoogle Scholar
  34. Prokopenko, M., Wang, P., Price, D. C., Valencia, P., Foreman, M., and Farmer, A. J. (2005d). Self-organizing hierarchies in sensor and communication networks. Artificial Life, Special Issue on Dynamic Hierarchies, 11(4):407–426..Google Scholar
  35. Prokopenko, M., Gerasimov, V., and Tanev, I. (2006a). 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., and Parisi, D., editors, From Animals to Animats 9: 9th International Conference on the Simulation of Adaptive Behavior (SAB 2006), Rome, Italy, September 25-29 2006, volume 4095 of Lecture Notes in Computer Science, pages 558–569.Google Scholar
  36. Prokopenko, M., Gerasimov, V., and Tanev, I. (2006b). Measuring spatiotemporal coordination in a modular robotic system. In Rocha, L., Yaeger, L., Bedau, M., Floreano, D., Goldstone, R., and Vespignani, A., editors, Artificial Life X: Proceedings of the 10th International Conference on the Simulation and Synthesis of Living Systems, pages 185–191, MIT Press, Bloomington IN.Google Scholar
  37. Prokopenko, M., Poulton, G. T., Price, D. C., Wang, P., Valencia,P., Hoschke, N., Farmer, A. J., Hedley, M., Lewis, C., and Scott, D. A. (2006c). Self-organising impact sensing networks in robust aerospace vehicles. In Fulcher, J., editor, Advances in Applied Artificial Intelligence, pages 186–233. Idea Group, Hershey, PA.Google Scholar
  38. Prokopenko, M., Boschetti, F., and Ryan, A. (2007). An information-theoretic primer on complexity, self-organisation and emergence. Advances in Complex Systems, under review.Google Scholar
  39. Sahin, E. and Spears, W. M. (2004). Swarm Robotics, Proccedings of SAB-2004 International Workshop, Santa Monica, CA, July 17, 2004, Revised Selected Papers, volume 3342 of Lecture Notes in Computer Science.Google Scholar
  40. Scaruffi, P. (2003). Thinking About Thought: A Primer on the New Science of Mind, Towards a Unified Understanding of Mind, Life and Matter. Writers Club Press, Lincoln, Neb.Google Scholar
  41. Shalizi, C. (2001). Causal Architecture, Complexity and Self-Organization in Time Series and Cellular Automata. PhD thesis, University of Michigan, available at http://www.cscs.umich.edu/crshalizi/thesis/.Google Scholar
  42. Shalizi, C. R., Shalizi, K. L., and Haslinger, R. (2004). Quantifying self-organization with optimal predictors. Physical Review Letters, 93(11):118701–1.–4.CrossRefGoogle Scholar
  43. Tanev, I., Ray, T., and Buller, A. (2005). Automated evolutionary design, robustness, and adaptation of sidewinding locomotion of a simulated snake-like robot. IEEE Transactions on Robotics, 21:632–645.CrossRefGoogle Scholar
  44. Volman, M. J. M. (1997). Rhythmic Coordination Dynamics in Children with and without a Developmental Coordination Disorder. PhD dissertation, University of Groningen, available at http://irs.ub.rug.nl/ppn/163776687.Google Scholar
  45. Wagner, A. (2005). Robustness and Evolvability in Living Systems. Princeton University Press, Princeton, NJ.Google Scholar
  46. Woese, C. R. (2004). A new biology for a new century. Microbiology and Molecular Biology Reviews, 68(2):173–186.CrossRefGoogle Scholar
  47. Wolfram, S. (1984). Universality and complexity in cellular automata. Physica D, 10.Google Scholar
  48. Wuensche, A. (1999). Classifying cellular automata automatically: Finding gliders, filtering, and relating space-time patterns, attractor basins, and the z parameter. Complexity, 4(3):47–66..CrossRefMathSciNetGoogle Scholar
  49. Zambonelli, F., and Rana, O. F. (2005). Self-organization in distributed systems engineering. Special Issue of IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 35(3).Google Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Mikhail Prokopenko

There are no affiliations available

Personalised recommendations