An artificial immune system based multi-agent model and its application to robot cooperation problem

Artificial immune system (AIS) imitates the natural immune system that has sophisticated methodologies and capabilities to build computational algorithms that solves engineering problems efficiently. Based on this fact we have investigated three basic concepts: 1) The behavioral management of artificial intelligence (AI) namely the intelligent multi agent systems, 2) a geometric property of any object considered as an environment, and that define the average location of their weightrdquo for every object the weight always acts through the center of gravityrdquo. The motion of any object is described through space in terms of the translation of the center of gravity (G) of the object from one place to another and the rotation of the object about its center of gravity if it is free to rotate. 3) Artificial immune system that imitates the biological theory called the immune system, and the evolutionary computation called discrimination/detection-based B-Cell and evolved lymphocyte for environment idiosyncrasy. The outcome of this research is an artificial immune system based intelligent multi agent robotic that solves agent-based applications. This new method is applied to a robot cooperation problem where autonomous mobile robots emulate natural behaviors of our cells and molecules and realize their group behaviors. The cooperation problem focuses on the center of gravity of any object ldquouniform mass, common shapes, non-uniform mass and a general shaperdquo. The simulation experiments demonstrated that this novel evolutionary method solves the robot cooperation problems successfully.