Novelty of behaviour as a basis for the neuro-evolution of operant reward learning

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 169-176

  Soltoggio, Andrea ^a   Jones, Ben ^b  

^a EPFL   (Switzerland)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Artificial life; Learning; Neuro evolution

Indexed keywords

ARTIFICIAL LIFE; COURSE OF ACTION; NEURAL CONTROL;

EDUCATION;

EID: 72749119270     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1569925     Document Type: Conference Paper

References (17)

1. Oxford English Dictionary. Oxford : Oxford University Press, 1989.
2. T. Bäck, D. B. Fogel, and Z. Michalevicz, editors. Handbook of Evolutionary Computation. Oxford University Press, Oxford, 1997.
3. J. Blynel and D. Floreano. Exploring the T-Maze: Evolving Learning-Like Robot Behaviors Using CTRNNs. In EvoWorkshops, pages 593-604, 2003.
4. R. Bogaz. Optimal decision-making theories: linking neurobiology with behaviour. Trends in Cognitive Sciences, 11:118-125, 2006.
5. Britannica. Animal learning. Encyclopedia Britannica 2007 Ultimate Reference Suite, 2007.
6. N. D. Daw, J. P. O'Doherty, P. Dayan, B. Seymour, and R. J. Dolan. Cortical substrates for exploratory decisions in humans. Nature, 441(15):876-879, June 2006.
7. D. Floreano and J. Urzelai. Evolution of plastic control networks. Auton. Robots, 11(3):311-317, 2001.
8. C. R. Gallistel. The Organization of Learning. MIT Press, 1993.
9. J. Lehman and K. O. Stanley. Exploiting Open-Endedness to Solve Problems Through the Search for Novelty. In Proceedings of the Eleventh International Conference on Artificial Life (ALIFE XI) Cambridge MA: MIT Press, 2008.
10. Y. Niv, D. Joel, I. Meilijson, and E. Ruppin. Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviours. Adaptive Behaviour, 10(1):5-24, 2002.
11. D. T. Pham and X. Liu. Neural Networks for Identification, Prediction and Control. Springer, London, 1995.
12. J. E. Rowe and D. Hidovic. An evolution strategy using a continuous version of the gray-code neighbourhood distribution. In GECCO (1), pages 725-736, 2004.
13. A. Soltoggio. Neural Plasticity and Minimal Topologies for Reward-based Learning Problems. In Proceeding of the 8th International Conference on Hybrid Intelligent Systems (HIS2008), 10-12 September, Barcelona, Spain, 2008.
14. A. Soltoggio, J. A. Bullinaria, C. Mattiussi, P. Dürr, and D. Floreano. Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios. In Proceedings of the Artificial Life XI Conference 2008. MIT Press., 2008.
15. K. O. Stanley and R. Miikkulainen. Evolving adaptive neural networks with and without adaptive synapses. In B. Rylander, editor, Genetic and Evolutionary Computation Conference Late Breaking Papers, pages 275-282, Chicago, USA, 12-16 July 2003.
16. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, USA, 1998.
17. A. M. S. Zalzala and A. S. Morris, editors. Neural Networks for Robotic Control: Theory and Applications. Ellis Horwood, London, 1996.