Modulated exploratory dynamics can shape self-organized behavior

Advances in Complex Systems

Volumn 12, Issue 3, 2009, Pages 273-291

  Hesse, Frank ^a,b   Der, Ralf ^c   Herrmann, J Michael ^d  

^a MAX PLANCK INSTITUTE FOR DYNAMICS AND SELF ORGANIZATION   (Germany)

^b UNIVERSITY OF GÖTTINGEN   (Germany)

^c MAX PLANCK INSTITUTE FOR MATHEMATICS IN THE SCIENCES   (Germany)

^d UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

Hebbian learning; Homeokinesis; Self organization

Indexed keywords

EID: 68349127099     PISSN: 02195259     EISSN: None     Source Type: Journal    
DOI: 10.1142/S0219525909002258     Document Type: Article

References (26)

1. Ashby, W. R., Design for a Brain (Chapman and Hill, London, 1954).
2. Berthouze, L. and Lungarella, M., Motor skill acquisition under environmental perturbations: On the necessity of alternate freezing and freeing, Adapt. Behav. 12 (2004) 47-631.
3. Cannon, W. B., The Wisdom of the Body (Norton, New York, 1939).
4. Cooper, S. J. and Donald, O., Hebb's synapse and learning rule: a history and commentary, Neurosci. Biobehav. Rev. 28 (2005) 851-874.
5. Der, R., Herrmann, M. and Liebscher, R., Homeokinetic approach to autonomous learning in mobile robots, in Robotik 2002, eds. Dillman, R., Schraft, R. D. and Wörn, H., number 1679 in VDI-Berichte (VDI, 2002), pp. 301-306.
6. Der, R., Hesse, F. and Liebscher, R., Self-organized exploration and automatic sensor integration from the homeokinetic principle, in Proc. SOAVE 04 (Ilmenau, 2004), pp. 220-230.
7. Der, R., Hesse, F. and Martius, G., Learning to feel the physics of a body, in Proc. Intl. Conf. Computational Intelligence for Modelling, Control and Automation and Intl. Conf. Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) (IEEE Computer Society, Washington, DC, USA, 2005), pp. 252-257.
8. Der, R., Hesse, F. and Martius, G., Rocking stamper and jumping snake from a dynamical system approach to artificial life, Adapt. Behav. 14 (2006) 105-115.
9. Der, R., Hesse, F. and Martius, G., Videos of self-organised robot behavior, http:// robot.informatik.uni-leipzig.de/Videos (2008).
10. Der, R., Martius, G. and Hesse, F., Let it roll - emerging sensorimotor coordination in a spherical robot, in Artificial Life X: Proc. Tenth Intl. Conf. Simulation and Synthesis of Living Systems, eds. Rocha, L. M., Yaeger, L. S., Bedau, M. A., Floreano, D., Goldstone, R. L. and Vespignani, A. (MIT Press, 2006), pp. 192-198.
11. Der, R., Steinmetz, U. and Pasemann, F., Homeokinesis - a new principle to back up evolution with learning, in Computational Intelligence for Modelling, Control, and Automation, Concurrent Systems Engineering Series, Vol. 55 (IOS Press, Amsterdam, 1999), pp. 43-47.
12. Di Paolo, E., Organismically-inspired robotics: Homeostatic adaptation and natural teleology beyond the closed sensorimotor loop, in Dynamical Systems Approach to Embodiment and Sociality, eds. Murase, K. and Asakura, T. (2003), pp. 19-42.
13. Herrmann, J. M., Dynamical systems for predictive control of autonomous robots, Theory Biosci. 120(3-4) (2001) 241-253.
14. Hebb, D., The Organization of Behavior: A Neuropsychological Theory (Wiley, New York, 1949).
15. Hesse, F., Self-Organizing Control for Autonomous Robots - A Dynamical Systems Approach based on the Principle of Homeokinesis, Dissertation (Georg-August-Universität, Göttingen, 2009).
16. Kaelbling, L. P., Littman, M. L. and Moore, A. W., Reinforcement learning: A survey, J. Artif. Intell. Res. 4 (1996) 237-285.
17. Kuniyoshi, Y. and Sangawa, S., Early motor development from partially ordered neural-body dynamics: Experiments with a cortico-spinal-musculo-skeletal model, Biol. Cybern. 95 (2006) 589-605.
18. Schöner, G. and Dose, M., A dynamical systems approach to task-level system integration used to plan and control autonomous vehicle motion, Rob. Auton. Syst. 10 (1992) 253-267.
19. Steels, L., A case study in the behavior-oriented design of autonomous agents, in SAB94: Proc. of the Third Int. Conf. on Simulation of Adaptive Behavior: From Animals to Animats 3 (MIT Press, Cambridge, MA, USA, 1994), ISBN 0-262-53122-4, pp. 445-452.
20. Sutton, R. S. and Barto, A. G., Reinforcement Learning: An Introduction (MIT Press/Bradford Books, 1998).
21. Tamosiunaite, M., Ainge, J., Kulvicius, T., Porr, B., Dudchenko, P. and Wörgötter, F., Path-finding in real and simulated rats: Assessing the influence of path characteristics on navigation learning, J. Comp. Nsci. 25 (2008) 562-582.
22. Tani, J. and Ito, M., Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment, IEEE Trans. Syst., Man, Cybern. Part A: Syst. and Humans 33 (2003) 481-488.
23. Williams, H., Homeostatic plasticity in recurrent neural networks, in From Animals to Animats: Proc. of the 8th Intl. Conf. On Simulation of Adaptive Behavior, eds. Schaal, S. and Ispeert, A., Vol. 8 (MIT Press, Cambridge MA, 2004), pp. 344-353.
24. Elizabeth, P., In nature, animals that stop and start win the race, Science 288 (2000) 83-85.
25. Vásqueza, R. A., Ebenspergerb, L. A. and Bozinovic, F., The influence of habitat on travel speed, intermittent locomotion, and vigilance in a diurnal rodent, Behav. Ecol. 13 (2002) 182-187.
26. Williams, T. M., Davis, R. W., Fuiman, L. A., Francis, J., Le Boeuf, B. J., Horning, M., Calambokidis, J. and Croll, D. A., Sink or swim: Strategies for cost-efficient diving by marine mammals, Science 288 (2000) 133-136