Genetic Algorithms (GAs) to evolve multiple-agent cooperative systems

Abstract

In this paper, a microrobot soccer-playing game, such as that of MIROSOT (Microrobot World Cup Soccer Tournament), is adopted as a standard test bed for research on multiple-agent cooperative systems. It is considerably complex and requires expertise in several difficult research topics, such as mobile microrobot design, motor control, sensor technology, intelligent strategy planning, etc., to build up a complete system to play the game. In addition, because it is an antagonistic game, it appears ideal to test whether one method is better than other. To date there have been two different kinds of architecture for building such system. One is called vision-based or centralized architecture, and the other is known as robot-based or decentralized architecture. The main difference between them lies in whether there exists a host computer system which responds to data processing and strategy planning, and a global vision system which can view the whole playground and transfer the environment information to the host computer in real time. We believe that the decentralized approach is more advanced, but in the preliminary step of our study, we used the centralized approach because it can lighten any overload of the microrobot design. In this paper, a simplified layer model of the multiple-agent cooperative system is first proposed. Based on such a model, a system for a microrobot soccer-playing game is organized. At the same time a simple genetic algorithm (SGA) is used for the autonomous evolution of cooperative behavior among microrobots. Finally, a computer simulation system is introduced and some simulated results are explained.