Purpose of Review: To effectively synthesise and analyse multi-robot behaviour, we require formal task-level models which accurately capture multi-robot execution. In this paper, we review modelling formalisms for multi-robot systems under uncertainty, and discuss how they can be used for planning, reinforcement learning, model checking, and simulation. Recent Findings: Recent work has investigated models which more accurately capture multi-robot execution by considering different forms of uncertainty, such as temporal uncertainty and partial observability, and modelling the effects of robot interactions on action execution. Other strands of work have presented approaches for reducing the size of multi-robot models to admit more efficient solution methods. This can be achieved by decoupling the robots under independence assumptions, or reasoning over higher level macro actions. Summary: Existing multi-robot models demonstrate a trade off between accurately capturing robot dependencies and uncertainty, and being small enough to tractably solve real world problems. Therefore, future research should exploit realistic assumptions over multi-robot behaviour to develop smaller models which retain accurate representations of uncertainty and robot interactions; and exploit the structure of multi-robot problems, such as factored state spaces, to develop scalable solution methods.

翻译：综述目的：为了有效综合与分析多机器人行为，我们需要能准确捕捉多机器人执行过程的形式化任务级模型。本文综述了面向不确定性的多机器人系统形式化建模方法，并探讨了这些模型如何应用于规划、强化学习、模型检验与仿真领域。最新进展：近期研究通过考虑不同形式的不确定性（如时间不确定性和部分可观测性）以及建模机器人交互对动作执行的影响，提出了更能准确反映多机器人执行过程的模型。另一类研究工作提出了缩减多机器人模型规模的方法，以便采用更高效的求解策略——这可以通过在独立性假设下解耦机器人，或对高层宏动作进行推理来实现。总结：现有多机器人模型在准确捕捉机器人依赖性与不确定性的能力、与足够精简以可解实际问题的规模之间，呈现出权衡关系。因此，未来研究应基于对多机器人行为的合理假设，开发既能保持对不确定性与机器人交互的准确表征、又具有更小规模的模型；同时应利用多机器人问题中的结构化特性（如因子化状态空间），设计可扩展的求解方法。