Exploring the T-maze: Evolving learning-like robot behaviors using CTRNNs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2611, Issue , 2003, Pages 593-604

  Blynel, Jesper ^a   Floreano, Dario ^a  

^a EPFL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CONTINUOUS TIME SYSTEMS; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; REINFORCEMENT LEARNING;

CONTINUOUS TIME RECURRENT NEURAL NETWORKS; INTERNAL NETWORK; NAVIGATION TASKS; NEURAL CONTROLLER; REAL ROBOT; ROBOT BEHAVIOR;

ROBOTS;

EID: 35248820581     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36605-9_54     Document Type: Article

References (13)

1. J. Blynel. Evolving reinforcement learning-like abilities for robots. In (to appear) Proceedings on the 5th International Conference on Evolvable Systems (ICES'03): From Biology to Hardware. Springer Verlag, Berlin, 2003.
2. J. Blynel and D. Floreano. Levels of dynamics and adaptive behaviour in evolutionary neural controllers. In Hallam et al. [4], pages 272-281.
3. W. J. Freeman. The physiology of perception. Scientific American, 264:78-85, 1991.
4. B. Hallam, D. Floreano, J. Hallam, G Hayes, and J-A Meyer, editors. From Animals to Animats 7: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior. MIT Press-Bradford Books, Cambridge, MA, 2002.
5. N. Jakobi. Evolutionary robotics and the radical envelope-of-noise hypothesis. Adaptive Behavior, 6(2):325-368, 1997.
6. O. Miglino, H. H. Lund, and S. Nolfi. Evolving mobile robots in simulated and real environments. Artificial Life, 2(4):417-434, 1995.
7. R. Pfeifer and C. Scheier. Understanding Intelligence. MIT Press, Cambridge, MA, 1999.
8. J. Tani. Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Transactions on Systems, Man, and Cybernetics - Part B, 26:421-436, 1996.
9. J. Tani and S. Nolfi. Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Networks, 12(7-8):1131-1141, 1999.
10. E. Tuci, I. Harvey, and M. Quinn. Evolving integrated controllers for autonomous learning robots using dynamic neural networks. In Hallam et al. [4], pages 282-291.
11. B. Yamauchi and R. D. Beer. Integrating reactive, sequential, and learning behaviour using dynamical neural networks. In D. Cliff, P. Husbands, J. Meyer, and S. W. Wilson, editors, From Animals to Animats III: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, pages 382-391. MIT Press-Bradford Books, Cambridge, MA, 1994.
12. B. Yamauchi and R. D. Beer. Sequential behavior and learning in evolved dynamical neural networks. Adaptive Behavior, 2(3):219-246, 1994.
13. T. Ziemke and M. Thieme. Neuromodulation of reactive sensorimotor mappings as a short-term memory mechanism in delayed response tasks. (to appear) Adaptive Behavior, 10(3-4), 2003.