Control of intelligent agent systems (robots) using extended soft computing

Recently, researches on group robot systems (multi-agent intelligent robots: MAIR) have been reported, where a number of robots behave in a group like birds or ants. It is generally known that each robot has a limited intellectual power, but the robots can behave more intellectually in a group because they can interact each other. It has been reported that the multi-agent robot systems which can do many kinds of tasks efficiently by training the rules between environments and actions using reinforcement learning. Intelligent agent systems are also applied to decision making, human interface, perception and others even in a robot system (intelligent function agent robot: IFAR). This paper first introduces extended soft computing (ESC) which is the fusion/combination of fuzzy, neuro, genetic and chaotic computings and immune network theory in order to explain, what they call, complex systems and cognitive and reactive AIs. Then, contemporary intelligent system concept is discussed while the ESC is promising to realize it. Finally, a decision making robot with multi-agents (antigens and antibodies of immune networks), fuzzy inference and reinforcement learning (genetic computing) is described, as an example. It is confirmed that the ESC plays an important role in constructing intelligent robots