Adaptive agent design offers a way to improve human-AI collaboration on time-sensitive tasks in rapidly changing environments. In such cases, to ensure the human maintains an accurate understanding of critical task elements, an assistive agent must not only identify the highest priority information but also estimate how and when this information can be communicated most effectively, given that human attention represents a zero-sum cognitive resource where focus on one message diminishes awareness of other or upcoming information. We introduce a theoretical framework for adaptive signalling which meets these challenges by using principles of rational communication, formalised as Bayesian reference resolution using the Rational Speech Act (RSA) modelling framework, to plan a sequence of messages which optimise timely alignment between user belief and a dynamic environment. The agent adapts message specificity and timing to the particulars of a user and scenario based on projections of how prior-guided interpretation of messages will influence attention to the interface and subsequent belief update, across several timesteps out to a fixed horizon. In a comparison to baseline methods, we show that this effectiveness depends crucially on combining multi-step planning with a realistic model of user awareness. As the first application of RSA for communication in a dynamic environment, and for human-AI interaction in general, we establish theoretical foundations for pragmatic communication in human-agent teams, highlighting how insights from cognitive science can be capitalised to inform the design of assistive agents.

翻译：自适应智能体设计为在快速变化环境中提升人机协作在时效性任务上的表现提供了途径。在此类场景中，为确保人类对关键任务要素保持准确理解，辅助智能体不仅需要识别最高优先级的信息，还必须评估如何以及何时能最有效地传递这些信息——鉴于人类注意力是一种零和认知资源，对某一信息的关注会降低对其他或即将到来信息的感知。我们提出了一种自适应信号传递的理论框架，该框架通过运用理性沟通原则（形式化为使用理性言语行为建模框架的贝叶斯指代消解）来应对这些挑战，从而规划出一系列能优化用户信念与动态环境之间及时对齐的消息序列。该智能体基于对先验引导的消息解释将如何影响界面注意力及后续信念更新的多步预测（直至固定时间范围），针对特定用户和场景调整消息的精确度与发送时机。通过与基线方法的比较，我们证明这种有效性关键依赖于将多步规划与真实的用户感知模型相结合。作为RSA在动态环境通信及人机交互领域的首次应用，本研究为人机团队中的语用沟通奠定了理论基础，并阐明了如何利用认知科学的洞见来指导辅助智能体的设计。