Action Selection Based on Neural Networks in Multi Agent Environment

An agent must acquire and analyze various kinds of information from environment to achieve its goal. Moreover, in multi-agent environment, an action must be selected considering existence of other agents as a part of environment. The environmental information which includes existence of other agents has countless variations. It is required that an agent perceives these information and classifies them into some patterns which assigned suitable plan. In many fields of engineering, neural networks are one of the effective method for pattern processing. Soccer attracts many AI researchers' attention as the study model of multi-agent system that handles cooperative behavior of agents. Soccer is considered as multi-agent system because players must play cooperatively each other to win a game. In such reason, Soccer Server is provided as a framework to give a common test-bench to evaluate various kinds of multi-agent systems and cooperative algorithms. The goal of our work is to propose an architecture for cooperative autonomous agents. In this paper, we describe a training algorithm using neural network for selecting suitable plans by pattern learning and improvement of its performance