How explainable plans can make planning faster

In recent years the ubiquity of artificial intelligence raised concerns among the uninitiated. The misunderstanding is further increased since most advances do not have explainable results. For automated planning, the research often targets speed, quality, or expressiv-ity. Most existing solutions focus on one criteria while not addressing the others. However, human-related applications require a complex combination of all those criteria at different levels. We present a new method to compromise on these aspects while staying explainable. We aim to leave the range of potential applications as wide as possible but our main targets are human intent recognition and assistive robotics. We propose the HEART planner, a real-time decompositional planner based on a hierarchical version of Partial Order Causal Link (POCL). It cyclically explores the plan space while making sure that intermediary high level plans are valid and will return them as approximate solutions when interrupted. These plans are proven to be a guarantee of solvability. This paper aims to evaluate that process and its results compared to classical approaches in terms of efficiency and quality.