Action-selection and crossover strategies for self-modeling machines

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 198-205

  Bongard, Josh ^a  

^a University of Vermont   (United States)

Author keywords

Artificial intelligence; Evolutionary robotics; Self modeling

Indexed keywords

ADAPTIVE SYSTEMS; ARTIFICIAL INTELLIGENCE; DATA STRUCTURES; ERROR ANALYSIS; FUNCTION EVALUATION;

EVOLUTIONARY ROBOTICS; SELF-MODELING; TRAINING DATA;

EVOLUTIONARY ALGORITHMS;

EID: 34548104409     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277004     Document Type: Conference Paper

References (24)

1. H. Andrew. System identification using genetic programming. In Proceedings of the Second Intl. Conf. on Adaptive Computing in Engineering Design and Control, pages 57-62, 1996.
2. Y. Baram, R. El-Yaniv, and K. Luz. Online choice of active learning algorithms. Journal of Machine Learning Research, 5:255-291, 2004.
3. J. Bongard and H. Lipson. Once more unto the breach: Co-evolving a robot and its simulator. In Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), pages 57-62, 2004.
4. J. Bongard and H. Lipson. Active coevolutionary learning of deterministic finite automata. Journal of Machine Learning Research, 6(Oct):1651-1678, 2005.
5. J. Bongard and H. Lipson. 'Managed challenge' alleviates disengagement in co-evolutionary system identification. In Proceedings of the 2005 Genetic and Evolutionary Computation Conference, pages 531-538, Washington DC, 2005.
6. J. Bongard and H. Lipson. Nonlinear system identification using coevolution of models and tests. IEEE Transactions on Evolutionary Computation, 9(4):361-384, 2005.
7. J. Bongard, V. Zykov, and H. Lipson. Resilient machines through continuous self-modeling. Science, 314:1118-1121, 2006.
8. J. C. Bongard and H. Lipson. Automated robot function recovery after unanticipated failure or environmental change using a minimum of hardware trials. In Proceedings of The 2004 NASA/DoD Conference on Evolvable Hardware, pages 169-176, Seattle, WA, 2004.
9. J. C. Bongard and H. Lipson. Automating genetic network inference with minimal physical experimentation using coevolution. In Proceedings of The 2004 Genetic and Evolutionary Computation Conference, Seattle, WA, 2004.
10. D. Cliff, P. Husbands, and I. Harvey. Evolving visually guided robots. In J.-A. Meyer, H. Roitblat, and S. Wilson, editors, Proceedings of the Second International Conference on the Simulation of Adaptive Behaviour, Boston, MA, 1993. MIT Press.
11. D. Floreano and F. Mondada. Hardware solutions for evolutionary robotics. In P. Husbands and J.-A. Meyer, editors, EvoRobots, pages 137-151, 1998.
12. G. Gray, D. Murray-Smith, Y. Li, K. Sharman, and T. Weinbrenner. Nonlinear model structure identification using genetic programming. Control Engineering Practice, 6:1341-1352, 1998.
13. W. D. Hillis. Co-evolving parasites improve simulated evolution as an optimization procedure. Physica D, 42:228-234, 1990.
14. N. Jakobi. Evolutionary robotics and the radical envelope of noise hypothesis. Adaptive Behavior, 6(1):131-174, 1997.
15. E. D. Jong and J. Pollack. Ideal evaluation from coevolution. Evolutionary Computation, 12(2):159-192, 2004.
16. R. D. King, K. E. Whelan, F. M. Jones, P. G. K. Reiser, C. H. Bryant, S. H. Muggleton, D. B. Kell, and S. G. Oliver. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427:247-252, 2004.
17. B. Kouchmeshky, W. Aquino, H. Lipson, and J. C. Bongard. Coevolutionary strategy for structural damage identification using minimal physical testing. International Journal for Numerical Methods in Engineering, 69(5):1085-1107, 2006.
18. J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Boston, MA, 1992.
19. A. Livnat and N. Pippenger. An optimal brain can be composed of conflicting agents. PNAS, 103(9):3198-3202, 2006.
20. L. Ljung. System Identification: Theory for the User. Prentice-Hall, Inc., Englewood Cliffs, NJ, 1999.
21. S. Nolfi and D. Floreano. Evolutionary Robotics. MIT Press, Boston, MA, 2000.
22. J. B. Pollack, H. Lipson, S. Ficici, P. Funes, G. Hornby, and R. Watson. Evolutionary techniques in physical robotics. In J. Miller, editor, Evolvable Systems: from biology to hardware, pages 175-186. Springer-Verlag, 2000.
23. H. S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the Fifth Workshop on Computational Learning Theory, pages 287-294, New York: ACM Press, 1992.
24. R. Tedrake, T. Zhang, and H. Seung. Learning to walk in 20 minutes. In Proceedings of the Fourteenth Yale Workshop on Adaptive and Learning Systems, Yale University, New Haven, CT, 2005.