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Using learned action models in execution monitoring

Fox, M. and Gough, J. and Long, D. (2006) Using learned action models in execution monitoring. In: 25th Workshop of the UK Planning and Scheduling Special Interest Group, 2006-12-14 - 2006-12-15.

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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 further developments of the work described in (Fox et al. 2006), where models of behaviours were learned as Hidden Markov Models. Execution of behaviours is monitored by tracking the most likely trajectory through such a learned model, while possible failures in execution are identified as deviations from common patterns of trajectories within the learned models. We present results for our experiments with a model learned for a robot behaviour.

Item type: Conference or Workshop Item (Paper)
ID code: 3147
Keywords: planning, execution monitoring, learned action models, intelligent autonomy, Markov models, Electronic computers. Computer science, Computer software
Subjects: Science > Mathematics > Electronic computers. Computer science
Science > Mathematics > Computer software
Department: Faculty of Science > Computer and Information Sciences
Unknown Department
Depositing user: Professor Maria Fox
Date deposited: 27 Apr 2007
Last modified: 21 Sep 2017 15:31
URI: https://strathprints.strath.ac.uk/id/eprint/3147
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