Behavior Learning System for Robot Soccer Using Neural Network

Abstract

With technological developments, the prospect of a human-robot symbiotic society has emerged. A soccer game has characteristics similar to those expected in such a society. Soccer is a multiagent game in which the strategy employed depends on each agent’s position and actions. This paper discusses the results of the development of a learning system that uses a self-organizing map to select behaviors depending on the scenario (two-dimensional absolute coordinates of the agent, other agents, and the ball). The system can reproduce the action-selection algorithms of all the players on a certain team, and the robot can instantly select the next cooperative action from information obtained during the game. Thus, common-sense rules can be shared to learn an action-selection algorithm for a set of both human and robot agents.