An autonomous spacecraft agent prototype
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
SIAM Journal on Scientific Computing
Hi-index | 0.00 |
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-fiat. 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.