Proactive Decision Support aims at improving the decision making experience of human decision-makers by enhancing the quality of the decisions and the ease of making them. Given that AI techniques are efficient in searching over a potentially large solution space (of decision) and finding good solutions, it can be used for human-in-the-loop scenarios such as disaster response that demand naturalistic decision making. A human decision-maker, in such scenarios, may experience high-cognitive overload leading to a loss of situational awareness. In this paper, we propose the use of automated task-planning techniques coupled with design principles laid out in the Human-Computer Interaction (HCI) community for developing a proactive decision support system. To this extent, we highlight the capabilities of such a system RADAR and briefly, describe how automated planning techniques help us in providing the varying degrees of assistance. To evaluate the effectiveness of the different capabilities, we conduct ablation studies with human subjects on a synthetic environment for making an interactive plan of study. We found that planning techniques like plan validation and suggestions help to reduce planning time (objective metrics) and improves user satisfaction (subjective metrics) compared to expert human planners without any support.

Designing agents capable of explaining complex sequential decisions remains a significant open problem in human-AI interaction. Recently, there has been a lot of interest in developing approaches for generating such explanations for various decision-making paradigms. One such approach has been the idea of explanation as model-reconciliation. The framework hypothesizes that one of the common reasons for a user's confusion could be the mismatch between the user's model of the agent's task model and the model used by the agent to generate the decisions. While this is a general framework, most works that have been explicitly built on this explanatory philosophy have focused on classical planning settings where the model of user's knowledge is available in a declarative form. Our goal in this paper is to adapt the model reconciliation approach to a more general planning paradigm and discuss how such methods could be used when user models are no longer explicitly available. Specifically, we present a simple and easy to learn labeling model that can help an explainer decide what information could help achieve model reconciliation between the user and the agent with in the context of planning with MDPs.