Abstract
The lack of a diagnostic model often prohibits us
from deploying diagnostic reasoning for reasoning about the root causes of encountered issues.
For overcoming this obstacle, we discuss in this
manuscript how to exploit active automata learning for learning deterministic and stochastic models from black-box reactive systems for diagnostic purposes. On one hand, we can learn models
of faulty systems for being able to deploy model based reasoning. Furthermore, we will also show
how to exploit fault models in the learning process, such as to derive a behavioral model describing the entire corresponding diagnosis search
space. In terms of applications, we will discuss
several concrete diagnosis scenarios and how our
models can be exploited accordingly, as well as
report first experiments and corresponding results
from deploying diagnostic reasoning for reasoning about the root causes of encountered issues.
For overcoming this obstacle, we discuss in this
manuscript how to exploit active automata learning for learning deterministic and stochastic models from black-box reactive systems for diagnostic purposes. On one hand, we can learn models
of faulty systems for being able to deploy model based reasoning. Furthermore, we will also show
how to exploit fault models in the learning process, such as to derive a behavioral model describing the entire corresponding diagnosis search
space. In terms of applications, we will discuss
several concrete diagnosis scenarios and how our
models can be exploited accordingly, as well as
report first experiments and corresponding results
| Original language | English |
|---|---|
| Publication status | Published - 13 Sept 2021 |
| Event | 32nd International Workshop on Principle of Diagnosis (DX) - Hamburg, Germany Duration: 13 Sept 2021 → 15 Sept 2021 https://www.hsu-hh.de/imb/en/dx-2021 |
Workshop
| Workshop | 32nd International Workshop on Principle of Diagnosis (DX) |
|---|---|
| Period | 13/09/21 → 15/09/21 |
| Internet address |