Search:
Computing and Library Services - delivering an inspiring information environment

Action Knowledge Acquisition with Opmaker2

McCluskey, T.L., Cresswell, S.N., Richardson, N.E. and West, Margaret M. (2010) Action Knowledge Acquisition with Opmaker2. In: Agents and Artificial Intelligence. Communications in Computer and Information Science, 67 . Springer, pp. 137-150. ISBN 978-3-642-11818-0

[img] PDF - Published Version
Restricted to Repository staff only

Download (133kB)

    Abstract

    AI planning engines require detailed specifications of dynamic knowledge of the domain in which they are to operate, before they can function. Further, they require domain-specific heuristics before they can function efficiently. The problem of formulating domain models containing dynamic knowledge regarding actions is a barrier to the widespread uptake of AI planning, because of the difficulty in acquiring and maintaining them. Here we postulate a method which inputs a partial domain model (one without knowledge of domain actions) and training solution sequences to planning tasks, and outputs the full domain model, including heuristics that can be used to make plan generation more efficient.

    To do this we extend GIPO’s Opmaker system [1] so that it can induce representations of actions from training sequences without intermediate state information and without requiring large numbers of examples. This method shows the potential for considerably reducing the burden of knowledge engineering, in that it would be possible to embed the method into an autonomous program (agent) which is required to do planning. We illustrate the algorithm as part of an overall method to acquire a planning domain model, and detail results that show the efficacy of the induced model.

    Item Type: Book Chapter
    Additional Information: International Conference, ICAART 2009, Porto, Portugal, January 19-21, 2009. Revised Selected Papers
    Uncontrolled Keywords: Planning and Scheduling; Machine Learning
    Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Informatics Research Group
    School of Computing and Engineering > Informatics Research Group > Knowledge Engineering and Intelligent Interfaces
    School of Computing and Engineering > Pedagogical Research Group
    Related URLs:
    Depositing User: Thomas Mccluskey
    Date Deposited: 19 May 2010 16:01
    Last Modified: 08 Nov 2013 15:59
    URI: http://eprints.hud.ac.uk/id/eprint/7716

    Document Downloads

    Downloader Countries

    More statistics for this item...

    Item control for Repository Staff only:

    View Item

    University of Huddersfield, Queensgate, Huddersfield, HD1 3DH Copyright and Disclaimer All rights reserved ©