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

本文研究了面向新手用户的非稳健智能决策支持系统的互动，介绍了一种新颖的基于子目标的解释类型，即补充传统智能决策支持系统输出的信息，以提高用户任务表现和系统的鲁棒性。研究表明，基于子目标的解释可实现更好的用户任务表现、提高用户区分优化和非优化智能决策支持推荐的能力、得到用户的喜爱，并在智能决策支持系统失灵的情况下实现更加鲁棒的用户表现。