Learning planning action models from Internet of Things data in buildings: A concept-driven approach

Current buildings often operate in a relatively naïve manner, leading to inefficient energy use. This presents an opportunity to enhance efficiency of buildings’ operations by leveraging the advancements in the Internet of Things (IoT) and Artificial Intelligence (AI). AI planning, in particular, offers robust mechanisms for coordinating building operations while satisfying sustainability goals. However, two key obstacles hinder the application of AI planning in this context: the strong requirement for planning action models in a standardised syntax, such as the Planning Domain Definition Language (PDDL), and the lack of adequate tools for developing such models when planning experts and readily available plan traces are scarce. We introduce a novel approach for learning action models from IoT data in building environments semi-automatically. Our work is multifaceted in that it also supports the engineering process by presenting a reference model, a formalisation of the learning task and its correspondence to concept learning, a three-step method to accomplishing this task, and the method’s implementation in a multi-layered component-based system. We validate our approach on a case study using existing data from actual houses. The results show that the approach supports the learning of highly accurate PDDL action models that are capable of successfully solving planning problems, laying a pathway towards more intelligent and sustainable building management.