Coordinating multi-rover systems: Evaluation functions for dynamic and noisy environments

GECCO 2005 - Genetic and Evolutionary Computation Conference

Volumn , Issue , 2005, Pages 591-598

  Tumer, Kagan ^a   Agogino, Adrian ^a  

^a NASA AMES RESEARCH CENTER   (United States)

Author keywords

Genetic Algorithms; Multiagent Systems; Neural Networks

Indexed keywords

GENETIC ALGORITHMS; MULTI AGENT SYSTEMS; NEURAL NETWORKS; POPULATION STATISTICS;

EVALUATION FUNCTION; NOISY ENVIRONMENTS;

EVOLUTIONARY ALGORITHMS;

EID: 32444448208     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1068009.1068109     Document Type: Conference Paper

References (18)

1. A. Agah and G. A. Bekey. A genetic algorithm-based controller for decentralized multi-agent robotic systems. In In Proc. of the IEEE Int. Conf. of Evolutionary Computing, Nagoya, Japan, 1996.
2. A. Agogino, K. Stanley, and R. Miikkulainen. Online interactive neuro-evolution, Neural Processing Letters, 11:29-38, 2000.
3. A. Agogino and K. Tumer. Efficient evaluation functions for multi-rover systems. In The Genetic and Evolutionary Computation Conference, pages 1-12, Seatle, WA, June 2004.
4. G. Baldassarre, S. Nolfi, and D. Parisi. Evolving mobile robots able to display collective behavior. Artificial Life, pages 9: 255-267, 2003.
5. M. Dorigo and L. M. Gambardella. Ant colony systems: A cooperative learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1):53-66, 1997.
6. S. Farritor and S. Dubowsky. Planning methodology for planetary robotic exploration. In ASME Journal of Dynamic Systems, Measurement and Control, volume 124, pages 4: 698-701, 2002.
7. D. Floreano and F. Mondada. Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot. In Proc. of Conf. on Simulation of Adaptive Behavior, 1994.
8. F. Gomez and R. Miikkulainen. Active guidance for a unless rocket through neuroevolution. In Proceedings of the Genetic and Evolutionary Computation Conference, Chicago, Illinois, 2003.
9. F. Hoffmann, T.-J. Koo, and O. Shakernia. Evolutionary design of a helicopter autopilot. In Advances in Soft Computing - Engineering Design and Manufacturing, Part 3: Intelligent Control, pages 201-214, 1999.
10. A. Martinoli, A. J. Ijspeert, and F. Mondala. Understanding collective aggregation mechanisms: From probabilistic modelling to experiments with real robots. Robotics and Autonomous Systems, 1999.
11. M. J. Mataric. Coordination and learning in multi-robot systems. In IEEE Intelligent Systems, pages 6-8, March 1998.
12. K. Stanley and R. Miikkulainen. Efficient reinforcement learning through evolving neural network topologies. In GECCO-2002, San Francisco, CA, 2002.
13. K. Tumer and A. Agogino. Overcoming communication restrictions in collectives. In Proceedings of the International Joint Conference on Neural Networks, Budapest, Hungary, July 2004.
14. K. Tumer and D. Wolpert, eds. Collectives and the Design of Complex Systems. Springer, New York, 2004.
15. K. Tumer and D. Wolpert. A survey of collectives. In Collectives and the Design of Complex Systems, pages 1,42. Springer, 2004.
16. K. Tumer and D. H. Wolpert. Collective intelligence and Braess' paradox. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pages 104-109, Austin, TX, 2000.
17. D. Whitley, F. Gruau, and L. Pyeatt. Cellular encoding applied to neurocontrol. In International Conference on Genetic Algorithms, 1995.
18. D. H. Wolpert and K. Tumer. Optimal payoff functions for members of collectives. Advances in Complex Systems, 4(2/3):265-279, 2001.