Jilani, Rabia, Crampton, Andrew, Kitchin, Diane E. and Vallati, Mauro (2015) ASCoL: a Tool for Improving Automatic Planning Domain Model Acquisition. In: AI*IA 2015 Advances in Artificial Intelligence : XIVth International Conference of the Italian Association for Artificial Intelligence. Lecture Notes in Computer Science, 9336 . Springer, pp. 438-451. ISBN 978-3-319-24308-5

Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. AI Planning requires domain models. Synthesising operator descriptions and domain specific constraints by hand for AI planning domain models is time intense, error-prone and challenging. To alleviate this, utomatic domain model acquisition techniques have been introduced. Amongst others, the LOCM and LOCM2 systems require as input some plan traces only, and are effectively able to automatically encode a large part of the domain knowledge. In particular, LOCM effectively determines the dynamic part of the domain model. On the other hand, 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 international planning competition domains, and has been shown to be effective.

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