Parameterized synthesis offers a solution to the problem of constructing correct and verified controllers for parameterized systems. Such systems occur naturally in practice (e.g., in the form of distributed protocols where the amount of processes is often unknown at design time and the protocol must work regardless of the number of processes). In this paper, we present a novel learning based approach to the synthesis of reactive controllers for parameterized systems from safety specifications. We use the framework of regular model checking to model the synthesis problem as an infinite-duration two-player game and show how one can utilize Angluin's well-known L* algorithm to learn correct-by-design controllers. This approach results in a synthesis procedure that is conceptually simpler than existing synthesis methods with a completeness guarantee, whenever a winning strategy can be expressed by a regular set. We have implemented our algorithm in a tool called L*-PSynth and have demonstrated its performance on a range of benchmarks, including robotic motion planning and distributed protocols. Despite the simplicity of L*-PSynth it competes well against (and in many cases even outperforms) the state-of-the-art tools for synthesizing parameterized systems.

中文翻译：

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具有安全特性的参数化合成

参数化综合为为参数化系统构建正确且经过验证的控制器的问题提供了解决方案。这样的系统在实践中自然发生（例如，以分布式协议的形式，其中进程的数量在设计时通常是未知的，并且无论进程的数量如何，协议都必须工作）。在本文中，我们提出了一种新的基于学习的方法，用于根据安全规范合成参数化系统的反应控制器。我们使用常规模型检查框架将合成问题建模为无限持续时间的两人游戏，并展示如何利用 Angluin 著名的 L* 算法来学习正确设计控制器。这种方法产生的合成过程在概念上比现有的合成方法更简单，并且具有完整性保证，只要可以通过正则集表达获胜策略。我们在名为 L*-PSynth 的工具中实现了我们的算法，并在一系列基准测试中展示了其性能，包括机器人运动规划和分布式协议。尽管 L*-PSynth 很简单，但它与用于合成参数化系统的最先进工具竞争（并且在许多情况下甚至优于）最先进的工具。