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.

本文回顾了多机器人系统模型的形式化方法，并探讨了如何将它们用于计划、加强学习、模型检验和仿真。最近的研究致力于改进方法，准确捕捉多机器人之间的相互影响与不确定性，以及减少模型规模以提高求解效率。然而，现有的多机器人模型存在着在捕捉机器人依赖和不确定性精度与规模之间的平衡，未来研究应从实际多机器人行为出发，开发精度更高、同时保留多机器人之间不确定性与相互作用的小规模模型，并利用多机器人问题的结构，如因子状态空间，来开发可扩展求解方法。

多机器人系统不确定性下的正式建模