Evolvability of neuromodulated learning for robots

Proceedings of the 2008 ECSIS Symposium on Learning and Adaptive Behaviors for Robotic Systems, LAB-RS 2008

Volumn , Issue , 2008, Pages 41-46

  Dürr, Peter ^a   Mattiussi, Claudio ^a   Soltoggio, Andrea ^b   Floreano, Dario ^a  

^a EPFL   (Switzerland)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE SYSTEMS; ARTIFICIAL INTELLIGENCE; EDUCATION; NETWORK PROTOCOLS; RAPID SOLIDIFICATION; ROBOTICS; ROBOTS; SENSOR NETWORKS;

BIOLOGICAL NEURAL NETWORKS; EVOLUTIONARY METHODS; EVOLVABILITY; LEARNING AND MEMORY; NEUROMODULATION; ROBOTIC SYSTEMS; UNDERLYING PRINCIPLES;

NEURAL NETWORKS;

EID: 53849093760     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/LAB-RS.2008.22     Document Type: Conference Paper

References (12)

1. C. H. Bailey, M. Giustetto, Y.-Y. Huang, R. D. Hawkins, and E. R. Kandel. Is heterosynaptic modulation essential for stabilizing Hebbian plasticity and memory? Nature Reviews Neuroscience, 1(1):11-20, October 2000.
2. M. Bonani. e-puck education robot, http://www.e-puck.org/ as online, the 10.05.08.
3. T. Kondo. Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control. Appl. Soft Comput., 7(1):189-202, 2007.
4. C. Mattiussi, P. Dürr, and D. Floreano. Center of Mass Encoding: A self-adaptive representation with adjustable redundancy for real-valued parameters. In Proceedings of the 2007 conference on genetic and evolutionary computation (GECCO 2007), University College, London., pages 1304-1311, New York, 2007. ACM Press.
5. C. Mattiussi and D. Floreano. Analog genetic encoding for the evolution of circuits and networks. IEEE Transaction on Evolutionary Computation, 11 (5):596-607, Oct. 2007.
6. R. McGill, J. W. Tukey, and. W. A. Larsen. Variations of box plots. The American Statistician, 32(1): 12-16, Feb. 1978.
7. Y. Niv, D. Joel, I. Meilijson, and E. Ruppin. Evolution of reinforcement learning in uncertain environments: A simple explanation for complex foraging behaviors. Adaptive Behavior, 10(1):5-24, Jan. 2002.
8. J. Reisinger and R. Miikkulainen. Acquiring evolvability through adaptive representations. In Proceedings of the 2007 conference on genetic and evolutionary computation (GECCO 2007), University College, London., pages 1045-1052, New York, 2007. ACM Press.
9. N. Schweighofer, K. Doya, and S. Kuroda. Cerebellar aminergic neuromodulation: towards a functional understanding. Brain Research Reviews, 44(2-3): 103-116, March 2004.
10. T. Smith, P. Husbands, A. Philippides, and M. O'Shea. Neuronal Plasticity and Temporal Adaptivity: GasNet Robot Control Networks. Adaptive Behavior, 10(3-4): 161-183, 2002.
11. A. Soltoggio, J. A. Bullinaria, C. Mattiussi, P. Dürr, and D. Floreano. Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios. In Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems, 2008.
12. A. Soltoggio, P. Dürr, C. Mattiussi, and D. Floreano. Evolving neuromodulatory topologies for reinforcement learning-like problems. In Proceedings of the 2007 Congress on Evolutionary Computation. IEEE Press, 2007.