Framer: Planning Models from Natural Language Action Descriptions

Lindsay, A, Read, J, Ferreira, J, Hayton, T, Porteous, J and Gregory, P 2017, 'Framer: Planning Models from Natural Language Action Descriptions', in Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS 2017), Pittsburgh, United States, 18-23 June 2017, pp. 434-442.


Document type: Conference Paper
Collection: Conference Papers

Title Framer: Planning Models from Natural Language Action Descriptions
Author(s) Lindsay, A
Read, J
Ferreira, J
Hayton, T
Porteous, J
Gregory, P
Year 2017
Conference name ICAPS 2017
Conference location Pittsburgh, United States
Conference dates 18-23 June 2017
Proceedings title Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS 2017)
Publisher ICAPS
Place of publication USA
Start page 434
End page 442
Total pages 9
Abstract In this paper, we describe an approach for learning planning domain models directly from natural language (NL) descriptions of activity sequences. The modelling problem has been identified as a bottleneck for the widespread exploitation of various technologies in Artificial Intelligence, including automated planners. There have been great advances in modelling assisting and model generation tools, including a wide range of domain model acquisition tools. However, for modelling tools, there is the underlying assumption that the user can formulate the problem using some formal language. And even in the case of the domain model acquisition tools, there is still a requirement to specify input plans in an easily machine readable format. Providing this type of input is impractical for many potential users. This motivates us to generate planning domain models directly from NL descriptions, as this would provide an important step in extending the widespread adoption of planning techniques. We start from NL descriptions of actions and use NL analysis to construct structured representations, from which we construct formal representations of the action sequences. The generated action sequences provide the necessary structured input for inducing a PDDL domain, using domain model acquisition technology. In order to capture a concise planning model, we use an estimate of functional similarity, so sentences that describe similar behaviours are represented by the same planning operator. We validate our approach with a user study, where participants are tasked with describing the activities occurring in several videos. Then our system is used to learn planning domain models using the participants' NL input. We demonstrate that our approach is effective at learning models on these tasks.
Subjects Artificial Intelligence and Image Processing not elsewhere classified
Copyright notice © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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