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Fanan, A; McCluskey, T.L.; Department of Informatics, School of Computing and Engineering, University of Hudders�eld (2012)
Languages: English
Types: Unknown
Subjects: QA75, QA76
In automated planning current research is focused\ud on developing domain-independent planning engines.\ud These require domain models, written in a standard\ud input language such as PDDL to supply knowledge of\ud the planning application and task, before they can be\ud used. The main component of a domain model is the\ud representation of actions in the form of lifted opera-\ud tor schema. The acquisition and engineering of these\ud schema is an important area of research, as this process\ud is recognised as being di�cult and laborious even for\ud planning experts.\ud A fruitful line of research is to investigate mechanisms\ud to automatically learn planning domain models. Re-\ud cent research has studied learning from structured or\ud re�ned inputs supplied by a training agent (Cress-\ud well, McCluskey, and West 2011; Zhuo et al. 2010;\ud Wu, Yang, and Jiang 2005; McCluskey et al. 2010). An\ud alternative method would be to allow planning agents\ud to learn and develop the domain models by observa-\ud tion. One freely available source for learning actions\ud is selected web text; here actions are represented as\ud verbs in natural language. This project aims to in-\ud vestigate the possibility of extracting formal structures\ud representing actions from free text. We intend to utilise\ud large text corpuses available on-line from which to ex-\ud tract such action knowledge, and learn operator schema\ud in a formal language that can be converted to PDDL.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Bartak, R.; Fratini, S.; and McCluskey, L. 2010. The third competition on knowledge engineering for planning and scheduling. AI Magazine, Spring 2010.
    • Chambers, N., and Jurafsky, D. 2009. Unsupervised learning of narrative schemas and their participants. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2, ACL '09, 602{610. Stroudsburg, PA, USA: Association for Computational Linguistics.
    • Cresswell, S.; McCluskey, T.; and West, M. M. 2011. Acquiring planning domain models using LOCM. Knowledge Engineering Review (To Appear).
    • H.H.Zhuo, Q.Yang, R.Pan and L.Li. 2011. Cross-Domain Action-Model Acquisition for Planning Via Web Search.
    • McCluskey, T. L.; Cresswell, S. N.; Richardson, N. E.; and West, M. M. 2010. Action Knowledge Acquisition with Opmaker2. In Agents and Arti cial Intelligence, volume 67 of Communications in Computer and Information Science.
    • Springer Berlin Heidelberg. 137{150.
    • Sil, A., and Yates, A. 2011. Extracting STRIPS representations of actions and events. In Recent Advances in Natural Language Learning (RANLP).
    • Sil, A.; Huang, F.; and Yates, E. 2010. Extracting action and event semantics from web text. AAAI Fall Symposium on Commonsense Knowledge.
    • 2007. Planning Domain De nition Using GIPO. The Knowledge Engineering Review 22(1).
    • 2009. From requirements and analysis to pddl in itsimple3.
    • 0. Arti cial Intelligence.
    • Wu, K.; Yang, Q.; and Jiang, Y. 2005. Arms: Actionrelation modelling system for learning acquisition models.
    • WU, K.; YANG, Q.; and JIANG, Y. 2007. Arms: an automatic knowledge engineering tool for learning action models for ai planning. Knowl. Eng. Rev. 22:135{152.
    • Zhuo, H. H.; Yang, Q.; Hu, D. H.; and Li, L. 2010. Learning complex action models with quanti ers and logical implications. Arti cial Intelligence 174(18):1540{1569.
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