Learning static constraints for domain modeling from training plans

Intelligent agents solving problems in the real-world require domain models containing widespread knowledge of the world. Synthesising operator descriptions and domain specific constraints by hand for AI planning domain models is time-intense, error-prone and challenging. To alleviate this, automatic domain model acquisition techniques have been introduced. For example, the LOCM system requires as input some plan traces only, and is effectively able to automatically encode the dynamic part of the domain model. However, the static part of the domain, i.e., the underlying structure of the domain that can not be dynamically changed, but that affects the way in which actions can be performed is usually missed, since it can hardly be derived by observing transitions only. In this paper we introduce ASCoL, a tool that exploits graph analysis for automatically identifying static relations, in order to enhance planning domain models. ASCoL has been evaluated on domain models generated by LOCM for the international planning competition, and has been shown to be effective.