Advances in ad hoc teamwork have the potential to create agents that
collaborate robustly in real-world applications. Agents deployed in the real
world, however, are vulnerable to adversaries with the intent to subvert them.
There has been little research in ad hoc teamwork that assumes the presence of
adversaries. We explain the importance of extending ad hoc teamwork to include
the presence of adversaries and clarify why this problem is difficult. We then
propose some directions for new research opportunities in ad hoc teamwork that
leads to more robust multi-agent cyber-physical infrastructure systems.

本文介绍了将对手纳入 ad hoc teamwork 的重要性，并探讨了该问题的难度，提出了新的研究方向，旨在实现更加强大的多智能体网络物理基础设施系统。

存在对手的即兴团队合作

Ad Hoc Teamwork in the Presence of Adversaries

Recently, it has been widely accepted by the research community that
interactions between humans and cyber-physical infrastructures have played a
significant role in determining the performance of the latter. The existing
paradigm for designing cyber-physical systems for optimal performance focuses
on developing models based on historical data. The impacts of context factors
driving human system interaction are challenging and are difficult to capture
and replicate in existing design models. As a result, many existing models do
not or only partially address those context factors of a new design owing to
the lack of capabilities to capture the context factors. This limitation in
many existing models often causes performance gaps between predicted and
measured results. We envision a new design environment, a cyber-physical human
system (CPHS) where decision-making processes for physical infrastructures
under design are intelligently connected to distributed resources over
cyberinfrastructure such as experiments on design features and empirical
evidence from operations of existing instances. The framework combines existing
design models with context-aware design-specific data involving
human-infrastructure interactions in new designs, using a machine learning
approach to create augmented design models with improved predictive powers.

介绍了一个新的设计环境，称作 CPHS，它将现有设计模型与涉及人机基础设施交互的上下文感知设计数据相结合，使用机器学习方法创建增强的设计模型来实现性能预测力的提高。