Towards Automatic Code Generation for Robotic Soccer Behavior Simulation

Abstract

Multi-Agent Systems (MAS) are an Artificial Intelligence (AI) branch where agents handle distributed nature tasks in a cooperative system. MAS is widely used in robotic systems in scenarios where multiple robots must cooperate. In this direction, the robot soccer domain has been used as a test bed to stimulate research in this area, as it reproduces some important features of these systems, such as coordination. Each soccer team member is an agent whose behavior must be coordinated with the other team members cooperating to win the game. Simulation tools are frequently used in this context to create rehearsed plays, called setplays, during team training. However, these tools generally have a limited set of behaviors, e.g., kicking, available to use in setplays, and new behaviors must be manually implemented. This implementation requires knowledge of specific source codes and a significant programming effort, in addition to leaving the behavior coupled and dependent on the tool. This work proposes the Robot Soccer Behavior Generator (RoboSocBG), a solution to develop new behaviors in the context of simulated soccer robots. It uses Model-Driven Development (MDD), an approach that enables the specification of behavior platform-independent models and code generation in specific tools. The solution was tested in our laboratory and validated in a case study. The results evidenced its feasibility to generate code in different platforms.