Existing automated domain acquisition approaches require large amounts of structured data in the form of plans or plan traces to converge. Further, automatically-generated domain models can be incomplete, error-prone, and hard to understand or modify. To mitigate these issues, we take advantage of readily-available natural language data: existing process manuals. We present a domain-authoring pipeline called NLtoPDDL, which takes as input a plan written in natural language and outputs a corresponding PDDL model. We employ a two-stage approach: stage one advances the state-of-the-art in action sequence extraction by utilizing transfer learning via pre-trained contextual language models (BERT and ELMo). Stage two employs an interactive modification of an object-centric algorithm which keeps human-in-the-loop to one-shot learn a PDDL model from the extracted plan. We show that NLtoPDDL is an effective and flexible domain-authoring tool by using it to learn five real-world planning domains of varying complexities and evaluating them for their completeness, soundness and quality.

The goals of Superhuman Sports games include to involve physical activity and enhance the skills and abilities of a human through technology. They are played for fun, competition or to improve the players’ health condition. To meet these goals, we designed and developed STAR: Superhuman Training in Augmented Reality, an augmented reality adventure shooter, and implemented it on the Microsoft Hololens. Our game promotes physical activity by making you avoid dangerous enemies and gather energy to deal with this threat while navigating a narrow path above lava. Social interaction is stimulated by its multiplayer mode, in which players have to work together to destroy an energy core. Player testing showed that we achieve our goal of physical exercise by making the player move at a pace slightly less than brisk walking and that the game is fun and immersive. These results show that STAR is a promising step in the right direction for the development of superhuman sports using augmented reality.