Plan and Goal Recognition as HTN Planning

Plan- and Goal Recognition (PGR) is the task of inferring the goals and plans of an agent based on its actions. A few years ago, an approach has been introduced that successfully exploits the performance of planning systems to solve it. That way, no specialized solvers are needed and PGR benefits from present and future research in planning. The approach uses classical planning systems and needs to plan (at least) once for every possible goal. However, models in PGR are often structured in a hierarchical way, similar to Hierarchical Task Networks (HTNs). These models are strictly more expressive than those in classical planning and can describe partially ordered sets of tasks or multiple goals with interleaving plans. We present the approach PGR as HTN Planning that enables the recognition of complex agent behavior by using unmodified, off-the-shelf HTN planners. Planning is thereby needed only once, regardless of how many possible goals there are. Our evaluation shows that current planning systems are able to handle large models with thousands of possible goals and that the approach results in high recognition rates.