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Detecting execution failures using learned action models

Fox, M. and Gough, J. and Long, D. (2007) Detecting execution failures using learned action models. In: Proceedings of AAAI 2007. Association for the Advancement of Artificial Intelligence. ISBN 978-1-57735-323-2

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    Abstract

    Planners reason with abstracted models of the behaviours they use to construct plans. When plans are turned into the instructions that drive an executive, the real behaviours interacting with the unpredictable uncertainties of the environment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a behaviour has diverged so far from the expected behaviour that it can be considered to be a failure. In this paper we present an approach by which a trace of the execution of a behaviour is monitored by tracking its most likely explanation through a learned model of how the behaviour is normally executed. In this way, possible failures are identified as deviations from common patterns of the execution of the behaviour. We perform an experiment in which we inject errors into the behaviour of a robot performing a particular task, and explore how well a learned model of the task can detect where these errors occur.

    Item type: Book Section
    ID code: 3424
    Keywords: execution failures, action models, errors, Electronic computers. Computer science
    Subjects: Science > Mathematics > Electronic computers. Computer science
    Department: Faculty of Science > Computer and Information Sciences
    Unknown Department
    Related URLs:
    Depositing user: Professor Maria Fox
    Date Deposited: 20 Jun 2007
    Last modified: 12 Mar 2012 15:30
    URI: http://strathprints.strath.ac.uk/id/eprint/3424

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