Learning from Human Decision-Making Behaviors — An Application to RoboCup Software Agents

Abstract

Programming of software agents is a difficult task. As a result, online learning techniques have been used in order to make software agents automatically learn to decide proper condition-action rules from their experiences. However, for complicated problems this approach requires a large amount of time and might not guarantee the optimality of rules. In this paper, we discuss our study to apply decision-making behaviors of humans to software agents, when both of them are present in the same environment. We aim at implementation of human instincts or sophisticated actions that can not be easily achieved by conventional multiagent learning techniques. We use RoboCup simulation as an experimenting environment and validate the effectiveness of our approach under this environment.