Quasi-monotonic Segmentation of State Variable Behavior for Reactive Control

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ConferenceProceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), July 9 - 13, 2005., Pittsburg, Pennsylvania, USA
AbstractReal-world agents must react to changing conditions as they execute planned tasks. Conditions are typically monitored through time series representing state variables. While some predicates on these times series only consider one measure at a time, other predicates, sometimes called episodic predicates, consider sets of measures. We consider a special class of episodic predicates based on segmentation of the the measures into quasi-monotonic intervals where each interval is either quasi-increasing, quasi-decreasing, or quasi-flat. While being scale-based, this approach is also computational efficient and results can be computed exactly without need for approximation algorithms. Our approach is compared to linear spline and regression analysis.
Publication date
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number48223
NPARC number8913790
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Record identifier99a9e3a2-eb42-4eee-8af9-287e383e4402
Record created2009-04-22
Record modified2016-05-09
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