Name
Abstract | ||
|---|---|---|
Neural State Classification (NSC) is a recently proposed method for runtime predictive monitoring of Hybrid Automata (HA) using deep neural networks (DNNs). NSC predictors have very high accuracy, yet are prone to prediction errors that can negatively impact reliability. To overcome this limitation, we present Neural Predictive Monitoring (NPM), a technique that complements NSC predictions with estimates of the predictive uncertainty. These measures yield principled criteria for the rejection of predictions likely to be incorrect, without knowing the true reachability values. We also present an active learning method that significantly reduces the NSC predictor’s error rate and the percentage of rejected predictions. NPM is based on the use of Bayesian techniques, Bayesian Neural Networks and Gaussian Processes, to learn respectively the predictor and the rejection rule. Our approach is highly efficient, with computation times on the order of milliseconds, and effective, managing in our experimental evaluation to successfully reject almost all incorrect predictions. |
Year | Venue | DocType |
|---|---|---|
2020 | OVERLAY | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luca Bortolussi | 1 | 663 | 58.88 |
| Francesca Cairoli | 2 | 0 | 1.01 |
| Nicola Paoletti | 3 | 0 | 1.35 |
| Scott A. Smolka | 4 | 2959 | 249.22 |
| SD Stoller | 5 | 109 | 9.50 |