论文标题
我们在同一页面上吗?使用强化学习的人类机器人团队计划任务的层次解释生成
Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement Learning
论文作者
论文摘要
提供解释被认为是人类机器人团队框架中AI代理的必要能力。正确的解释提供了AI代理决策背后的理由。但是,为了维持人类队友的认知需求以理解所提供的解释,先前的工作重点是以特定顺序提供解释或通过计划执行来交织解释的生成。此外,这些方法没有考虑在提供的整个解释中共享所需的细节程度。在这项工作中,我们认为应将代理的解释,尤其是复杂的解释提取,以与人类队友希望保持接收者的认知负担的细节水平保持一致。因此,学习层次解释模型是一项艰巨的任务。此外,代理商需要遵循一致的高级政策,以将学习的队友偏好转移到新的情况下,而低级详细计划则不同。我们的评估证实了理解解释的过程,尤其是复杂而详细的解释,是分层的。反映这一方面的人类偏好与创造和采用抽象以在我们的认知过程中更深入的知识同化完全相对应。我们表明,层次解释在减少认知负载的同时,实现了更好的任务绩效和行为能力。这些结果阐明了设计利用跨各个领域的增强学习和计划的可解释的代理。
Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework. The right explanation provides the rationale behind an AI agent's decision-making. However, to maintain the human teammate's cognitive demand to comprehend the provided explanations, prior works have focused on providing explanations in a specific order or intertwining the explanation generation with plan execution. Moreover, these approaches do not consider the degree of details required to share throughout the provided explanations. In this work, we argue that the agent-generated explanations, especially the complex ones, should be abstracted to be aligned with the level of details the human teammate desires to maintain the recipient's cognitive load. Therefore, learning a hierarchical explanations model is a challenging task. Moreover, the agent needs to follow a consistent high-level policy to transfer the learned teammate preferences to a new scenario while lower-level detailed plans are different. Our evaluation confirmed the process of understanding an explanation, especially a complex and detailed explanation, is hierarchical. The human preference that reflected this aspect corresponded exactly to creating and employing abstraction for knowledge assimilation hidden deeper in our cognitive process. We showed that hierarchical explanations achieved better task performance and behavior interpretability while reduced cognitive load. These results shed light on designing explainable agents utilizing reinforcement learning and planning across various domains.