Detecting execution failures using learned action models
Maria Fox, Jonathan Gough, Derek Long · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · 2007
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 in-teracting with the unpredictable uncertainties of the environ-ment can lead to failure. One of the challenges for intelligent autonomy is to recognise when the actual execution of a be-haviour 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 per-form an experiment in which we inject errors into the be-haviour of a robot performing a particular task, and explore how well a learned model of the task can detect where these errors occur. 1