title: Quasi-monotonic segmentation of state variable behavior for reactive control
creator: Fitzgerald, Will
creator: Lemire, Daniel
creator: Brooks, Martin
subject: Artificial Intelligence
description: 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.
date: 2005
type: Conference Paper
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/4496/1/episode_web.pdf
identifier:   Fitzgerald, Will and Lemire, Daniel and Brooks, Martin  (2005) Quasi-monotonic segmentation of state variable behavior for reactive control.  [Conference Paper]     
relation: http://cogprints.org/4496/