Towards Intelligent Companion Systems in General Aviation using Hierarchical Plan and Goal Recognition

Modern ultralight aircraft in general aviation are equipped with an onboard Pilot Assistance System (PAS) as a companion system, meant to guide the pilot in decision-making, e.g. with plan suggestions, especially in critical situations. For more meaningful guidance, the PAS must possess a continuous understanding of the context, i.e. the pilot's intention, so that decision-making support is relevant. However, in realistic settings, the pilot's intention is not communicated manually, but can only be proactively monitored by the PAS. This paper explores the possibility of embedding domain expertise using Hierarchical Task Network (HTN) planning to track the pilot's intention, by recognising the pilot's current goal task judging from the pilot's actions. Furthermore, by leveraging probability theory for state estimation, we derive belief values to be associated with the recognised goal task, inferred from already executed actions which are in turn inferred from in-cockpit observable measurement data. Statistical evaluation using data collected from human-in-the-loop tests shows that our method for tracking the pilot's intention is reliable enough to provide the PAS with a contextual understanding in real time.