Quasi-monotonic segmentation of state variable behavior for reactive control
Abstract
Real-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.
Reference
Will Fitzgerald, Daniel Lemire, and Martin Brooks, Quasi-monotonic segmentation of state variable behavior for reactive control, AAAI05, Pittsburgh, USA, July 2005.
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Software
Quasi-monotonic segmentation C++ and Python code.
BibTeX
@inproceedings{AAAI05,
author = {Will Fitzgerald and Daniel Lemire and Martin Brooks},
title = {Quasi-monotonic segmentation of state variable behavior for reactive control},
booktitle = {Proceedings of AAAI'05},
year = {2005},
url = {http://www.daniel-lemire.com/fr/documents/publications/episode_web.pdf}
}
Author
- Will Fitzgerald: william.a.fitzgerald at nasa.gov
- Daniel Lemire: lemire at acm.org
- Martin Brooks: martin.brooks@nrc.gc.ca
