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：为有效综合与分析多机器人行为，我们需要能够精确捕捉多机器人执行过程的正式任务级模型。本文综述了不确定条件下多机器人系统的形式化建模方法，并探讨了这些模型在规划、强化学习、模型检验及仿真中的应用。最新发现：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，提出了更精确刻画多机器人执行过程的模型。另一类工作提出了缩减多机器人模型规模的方法，从而支持更高效的求解策略：这可通过在独立性假设下解耦机器人，或基于高层宏动作进行推理来实现。总结：现有多机器人模型在精确捕捉机器人依赖关系与不确定性，以及保持足够小规模以可解实际世界问题之间存在权衡。因此，未来研究应利用多机器人行为的现实假设，开发既能保留不确定性及机器人交互的精确表征、又能缩小规模的模型；同时利用多机器人问题的结构特性（如分解式状态空间），发展可扩展的求解方法。