基于图时态逻辑描述的多Agent分布式策略合成
Distributed Policy Synthesis of Multi-Agent Systems With Graph Temporal Logic Specifications
摘要：
我们研究了分布式合成的政策多代理系统执行\emph{spatial-temporal}任务。我们正式的合成问题作为一个\emph{factored}马尔可夫决策过程受\emph{graph temporal logic}规范。每个代理的转换功能和任务是代理本身及其相邻代理的功能。在这项工作中，我们开发了另一种分布式合成方法，与我们之前的工作相比，该方法将可扩展性和运行时间提高了两个数量级。合成方法将问题分解为一组较小的问题，通过利用模型中的结构和规范，每个代理一个问题。我们表明，该方法的运行时间是线性的代理的数量。每个代理的问题的大小仅在相邻代理的数量上是指数的，这通常比代理的数量小得多。我们证明了疾病控制，城市安全，搜索和救援的案例研究中的方法的适用性。数值算例表明，该方法可扩展到数百个代理，每个代理有数百个状态，也可以处理比我们以前的工作显着更大的状态空间。
英文摘要：
We study the distributed synthesis of policies for multi-agent systems to perform \emph{spatial-temporal} tasks. We formalize the synthesis problem as a \emph{factored} Markov decision process subject to \emph{graph temporal logic} specifications. The transition function and task of each agent are functions of the agent itself and its neighboring agents. In this work, we develop another distributed synthesis method, which improves the scalability and runtime by two orders of magnitude compared to our prior work. The synthesis method decomposes the problem into a set of smaller problems, one for each agent by leveraging the structure in the model, and the specifications. We show that the running time of the method is linear in the number of agents. The size of the problem for each agent is exponential only in the number of neighboring agents, which is typically much smaller than the number of agents. We demonstrate the applicability of the method in case studies on disease control, urban security, and search and rescue. The numerical examples show that the method scales to hundreds of agents with hundreds of states per agent and can also handle significantly larger state spaces than our prior work.
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