  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
