Using learned action models in execution monitoring

M. Fox, J. Gough, D. Long, R. Qu (Editor)

Research output: Contribution to conferencePaperpeer-review

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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.
Original languageEnglish
Number of pages8
Publication statusPublished - 2006
Event25th Workshop of the UK Planning and Scheduling Special Interest Group - Nottingham, United Kingdom
Duration: 14 Dec 200615 Dec 2006


Conference25th Workshop of the UK Planning and Scheduling Special Interest Group
Abbreviated titlePlanSIG 2006
Country/TerritoryUnited Kingdom


  • planning
  • execution monitoring
  • learned action models
  • intelligent autonomy
  • Markov models


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