Name
Abstract | ||
|---|---|---|
Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., Learning-Enabled Components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain, and therefore cannot be assured as safe. Automated methods are needed for self-asses... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/MODELS50736.2021.00027 | 2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) |
Keywords | DocType | ISBN |
goal-based modeling,self-adaptive systems,artificial intelligence,machine learning,models at run time,cyber physical systems,behavior oracles,autonomous vehicles | Conference | 978-1-6654-3495-9 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Austin Langford | 1 | 3 | 0.71 |
| Kenneth H. Chan | 2 | 2 | 0.37 |
| Jonathon Emil Fleck | 3 | 2 | 0.37 |
| P. K. McKinley | 4 | 1397 | 121.87 |
| Betty H. C. Cheng | 5 | 3345 | 191.44 |