Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiagent Control of Self-Reconfigurable Robots
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Activation energy-based simulation for self-assembly of multi-shape tiles
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
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We describe a software system1 to model and visualize 3D or 2D self-assembly of groups of autonomous agents. The system makes a physically accurate estimate of the interaction of agents represented as rigid cubic or tetrahedral structures with variable electrostatic charges on the faces and vertices. Local events cause the agentsý charges to change according to user-defined rules or rules generated by genetic algorithms. The system is used as an experimental environment for theoretical and practical study of autonomous agent self-assembly. In particular, the system is used to further develop and test self-assembly properties of meso-blocks. The software system will be applied to the analysis, prediction and design of self-assembly behavior of agents from atomic- to macro-scales. In particular, it will be a platform for developing design techniques that can be implemented in real nano-scale systems to achieve useful structures.