Monitoring Piecewise Continuous Behaviors by Refining Trackers and their Models

Hybrid Systems and AI: Modeling Analysis and Control of Discrete Plus Continuous Systems

Volume

Papers from the 1999 AAAI Spring Symposium

Contents

We present a model-based monitoring method for dynamic systems that exhibit both discrete and continuous behaviors. MIMIC uses qualitative and semi-quantitative models to monitor dynamic systems even with incomplete knowledge. Recent advances have improved the quality of semi-quantitative behavior predictions, used observations to refine static envelopes around monotonic functions, and provided a semi-quantitative system identification method. Using these, we reformulate and extend MIMIC to handle discontinuous changes between models. Each hypothesis being monitored is embodied as a tracker, which uses the observation stream to refine its behavioral predictions, its underlying model, and the time uncertainty of any discontinuous transitions.