Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
A polynomial time primal network simplex algorithm for minimum cost flows
Proceedings of the seventh annual ACM-SIAM symposium on Discrete algorithms
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms
Journal of Intelligent and Robotic Systems
Computational Intelligence: Principles, Techniques and Applications
Computational Intelligence: Principles, Techniques and Applications
Evolutinary Robotics: From Algorithms to Implementations (World Scientific Series in Robotics and Intelligent Systems)
A Neural Network Approach to Dynamic Task Assignment of Multirobots
IEEE Transactions on Neural Networks
An Evolutionary Solution for Cooperative and Competitive Mobile Agents
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
| Hi-index | 0.00 |
This paper deals with genetic algorithm based methods for finding optimal structure for a neural network (weights and biases) and for a fuzzy controller (rule set) to control a group of mobile autonomous robots. We have implemented a predator and prey pursuing environment as a test bed for our evolving agents. Using theirs sensorial information and an evolutionary based behaviour decision controller the robots are acting in order to minimize the distance between them and the targets locations. The proposed approach is capable of dealing with changing environments and its effectiveness and efficiency is demonstrated by simulation studies. The goal of the robots, namely catching the targets, could be fulfilled only trough an emergent social behaviour observed in our experimental results.