基于子目标的不可靠智能决策支持系统推理
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems
摘要：
智能决策支持（IDS）系统利用人工智能技术来生成建议，这些建议引导人类用户完成任务的决策阶段。然而，一个关键的挑战是，IDS系统并不完美，在复杂的现实世界中，可能会产生不正确的输出或完全无法工作。可解释人工智能规划（XAIP）领域一直在寻求开发技术，使顺序决策人工智能系统的决策对最终用户更具解释性。至关重要的是，在将XAIP技术应用于IDS系统之前的工作已经假设规划者提出的计划总是最优的，因此作为决策支持向用户推荐的行动或计划总是正确的。在这项工作中，我们研究了新手用户与非鲁棒IDS系统的交互-偶尔会建议错误的操作，并且在用户习惯了它的指导后可能会变得不可用。我们引入了一种新的解释类型，基于子目标的解释，基于规划的IDS系统，补充传统的IDS输出的子目标的信息，建议的行动将有助于。我们证明了基于子目标的解释可以提高用户任务性能，提高用户区分最佳和次优IDS建议的能力，受到用户的青睐，并在IDS失败的情况下实现更强大的用户性能
英文摘要：
Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task. However, a key challenge is that IDS systems are not perfect, and in complex real-world scenarios may produce incorrect output or fail to work altogether. The field of explainable AI planning (XAIP) has sought to develop techniques that make the decision making of sequential decision making AI systems more explainable to end-users. Critically, prior work in applying XAIP techniques to IDS systems has assumed that the plan being proposed by the planner is always optimal, and therefore the action or plan being recommended as decision support to the user is always correct. In this work, we examine novice user interactions with a non-robust IDS system -- one that occasionally recommends the wrong action, and one that may become unavailable after users have become accustomed to its guidance. We introduce a novel explanation type, subgoal-based explanations, for planning-based IDS systems, that supplements traditional IDS output with information about the subgoal toward which the recommended action would contribute. We demonstrate that subgoal-based explanations lead to improved user task performance, improve user ability to distinguish optimal and suboptimal IDS recommendations, are preferred by users, and enable more robust user performance in the case of IDS failure
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
