Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control

Applied Soft Computing Journal

Volumn 7, Issue 1, 2007, Pages 189-202

  Kondo, Toshiyuki ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Evolutionary Robotics; Mobile robot control; Neural networks; Neuromodulation

Indexed keywords

BEHAVIORAL RESEARCH; CONTROL SYSTEMS; EVOLUTIONARY ALGORITHMS; NEURAL NETWORKS; PRODUCT DESIGN; ROBOTICS;

EVOLUTIONARY OPTIMIZATION PROCESSES; EVOLUTIONARY ROBOTICS; MOBILE ROBOT CONTROL; NEUROMODULATION;

MOBILE ROBOTS;

EID: 33750966249     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2005.05.004     Document Type: Article

References (24)

1. Beer R.D., Chiel H.J., and Sterling L. An artificial insect. American Scientist vol. 79 (1991) 444-452
2. Cliff D.T., Harvey I., and Husbunds P. Explorations in evolutionary robotics. Adaptive Behavior vol. 2, no. 1 (1993) 71-104
3. Reynolds C.W. An evolved, vision-based model of obstacle avoidance behavior. Artificial Life III (1994) pp. 327-346
4. Jacobi N., Husbands P., and Harvey I. Noise and the reality gap: the use of simulation in evolutionary robotics. Proceedings of the Third European Conference on Artificial Life (ECAL'95), Advances in Artificial Life. Springer (1995) 704-720
5. Miglino O., Lund H.H., and Nolfi S. Evolving mobile robots in simulated and real environments. Artificial Life II (1995) pp. 417-434
6. Floreano D., and Mondada F. Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot. Proceedings of the Third International Conference on Simulation of Adaptive Behavior (1994)
7. Floreano D., and Urzelai J. Evolution of neural controllers with adaptive synapses and compact genetic encoding. Proceedings of the Fifth European Conference on Artificial Life (ECAL'99), Advances in Artificial Life (1999) 183-194
8. Nolfi S., and Floreano D. Evolutionary Robotics-The Biology, Intelligence, and Technology of Self-Organizing Machines, A Bradford Book (2000), MIT Press, Cambridge, MA
9. Yamada S. Evolutionary behavior learning for action-based environment modeling by a mobile robot. Applied Soft Computing vol. 5, no. 2 (2005) 245-257
10. Holland J. Adaptation in Natural and Artificial Systems (1975), The University of Michigan Press, Ann Arbor
11. Back T., and Schwefel H. A survey of evolution strategies. In: Booker, and Belew (Eds). Proceedings of the Fourth International Conference on Genetic Algorithms. M. Kaufmann Publisher (1991) 2-9
12. Koza J. Genetic Programming: On the Programming of Computers by Means of Natural Selection (1992), MIT Press
13. Sutton R.S., and Barto A.G. Reinforcement Learning, A Bradford Book (1998), MIT Press, Cambridge, MA
14. Kondo T., and Ito K. A Reinforcement Learning with Evolutionary State Recruitment Strategy for Autonomous Mobile Robots Control, Robotics and Autonomous Systems vol. 46, no. 2 (2004), Elsevier pp. 111-124
15. Baldwin J.M. A new factor in evolution. American Naturalist vol. 30 (1896) pp. 441-451
16. Hinton G., and Nowlan S. How learning can guide evolution. Complex Systems vol. 1 (1987) pp. 495-502
17. Ackley D.H., and Littman M.L. Interactions between learning and evolution. Artificial Life II (1992), Addison-Wesley pp. 487-509
18. Belew R.K., McInemey J., and Schraudolph N.N. Evolving networks: using the Genetic Algorithm with connectionist learning. Proceedings of the Second Artificial Life Conference. Addison-Wesley (1991) 511-547
19. Meyrand P., Simmers J., and Moulins M. Construction of a pattern-generating circuit with neurons of different networks. Nature 351 May (2) (1991) 60-63
20. Hasselmo M. Neuromodulation and cortical function: modeling the physiological basis of behavior. Behavioral Brain Research vol. 67 (1995), Elsevier Science B.V. pp. 1-27
21. Kondo T., Ishiguro A., Tokura S., Uchikawa Y., and Eggenberger P. Realization of robust controllers in evolutionary robotics: a dynamically-rearranging neural network approach. Proceedings of the 1999 Congress of Evolutionary Computation, vol. 1 (1999) 366-373
22. Eggenberger P., Ishiguro A., Tokura S., Kondo T., and Uchikawa Y. Toward seamless transfer from simulated to real worlds: a dynamically-rearranging neural network approach. Lecture Notes in Computer Science vol. 1812 (2000), Springer pp. 44-60
23. Di Paolo E.A. Homeostatic adaptation to inversion of the visual field and other sensorimotor disruptions. From Animals to Animat 6 (2000), MIT Press pp. 440-449
24. Smith T.M.C., Husbands P., Philippides A., and O'Shea M. Neuronal plasticity and temporal adaptivity: GasNet robot control networks. Adaptive Behavior vol. 10, nos. 3-4 (2002) 161-183