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.

中文翻译：

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实现机器人足球行为模拟的自动代码生成

摘要

多代理系统 (MAS) 是人工智能 (AI) 的一个分支，代理在协作系统中处理分布式自然任务。MAS广泛应用于机器人系统中多个机器人必须协作的场景。在这个方向上，机器人足球领域已被用作刺激该领域研究的测试平台，因为它再现了这些系统的一些重要特征，例如协调性。每个足球队成员都是一个代理人，其行为必须与其他队员协调一致才能赢得比赛。在这种情况下，模拟工具经常用于在团队训练期间创建排练的比赛，称为“固定战术”。然而，这些工具通常具有一组有限的可在比赛中使用的行为，例如踢球，并且必须手动实现新的行为。除了使行为耦合并依赖于工具之外，这种实现还需要了解特定的源代码和大量的编程工作。这项工作提出了机器人足球行为生成器（RoboSocBG），这是一种在模拟足球机器人背景下开发新行为的解决方案。它使用模型驱动开发（MDD），这种方法可以在特定工具中指定独立于平台的行为模型和代码生成。该解决方案在我们的实验室进行了测试，并在案例研究中进行了验证。结果证明了其在不同平台上生成代码的可行性。