The algorithmic beauty of plants
The algorithmic beauty of plants
A System for Learning Control Strategies with Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Distributed, Physics-Based Control of Swarms of Vehicles
Autonomous Robots
Distributed genetic evolution in WSN
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
A generalized graph-based method for engineering swarm solutions to multiagent problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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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.