Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios
This paper introduces a novel framework for designing multi-agent systems, called “Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios” (DAEDALUS). Traditional approaches to designing multi-agent systems are offline (in simulation), and assume the presence of a global observer. In the online (real world), there may be no global observer, performance feedback may be delayed or perturbed by noise, agents may only interact with their local neighbors, and only a subset of agents may experience any form of performance feedback. Under these circumstances, it is much more difficult to design multi-agent systems. DAEDALUS is designed to address these issues, by mimicking more closely the actual dynamics of populations of agents moving and interacting in a task environment. We use two case studies to illustrate the feasibility of this approach.
KeywordsGenetic Algorithm Obstacle Avoidance Performance Feedback Dynamic Adaptation Credit Assignment
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- 1.Balch, T., Hybinette, M.: Social Potentials for Scalable Multi-Robot Formations. In: IEEE International Conference on Robotics and Automation (2000)Google Scholar
- 2.Grefenstette, J.: A system for learning control strategies with genetic algorithms. In: Proceedings of the Third International Conference on Genetic Algorithms, pp. 183–190. Morgan Kaufmann, San Francisco (1989)Google Scholar
- 3.Grefenstette, J.: Credit Assignment in Rule Discovery Systems Based on Genetic Algorithms, vol. 3, pp. 225–245. Springer, Heidelberg (1988)Google Scholar
- 4.Hettiarachchi, S., Spears, W.: Moving Swarm Formations Through Obstacle Fields. In: International Conference on Artificial Intelligence., vol. 1, pp. 97–103. CSREA Press (2005)Google Scholar
- 6.Spears, W.: Simple Subpopulation Schemes. In: Proceedings of the Evolutionary Programming Conference, pp. 296–307. World Scientific, Singapore (1994)Google Scholar
- 8.Watson, R., Ficici, S., Pollack, J.: Embodied Evolution: Distributing an evolutionary algorithm in a population of robots. In: Robotics and Autonomous Systems, vol. 39, pp. 1–18. Elsevier, Amsterdam (2002)Google Scholar
- 9.Wu, A., Schultz, A., Agah, A.: Evolving control for distributed micro air vehicles. In: Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 174–179. IEEE Press, Los Alamitos (1999)Google Scholar