Strategies for Safety Goal Decomposition for Neural Networks
| Faculty/Professorship: | Cognitive Systems |
| Author(s): | Schwalbe, Gesina  ; Schels, Martin |
| Corporate Body: | ACM Computer Science in Cars Symposium (CSCS), 3, 2019, Kaiserslautern |
| Publisher Information: | Bamberg : Otto-Friedrich-Universität |
| Year of publication: | 2020 |
| Pages: | 3 |
| Source/Other editions: | 3rd ACM Computer Science in Cars Symposium – Future Challenges in Artificial Intelligence & Security for Autonomous Vehicles / German Chapter of the ACM e.V. : München, S. 3 |
| Language(s): | English |
| DOI: | 10.20378/irb-47274 |
| Licence: | Creative Commons - CC BY-NC-SA - Attribution - NonCommercial - ShareAlike 4.0 International |
| URL: | https://cscs19.cispa.saarland/files/cscs19_came... |
| URN: | urn:nbn:de:bvb:473-irb-472744 |
| Abstract: | Neural networks (NNs) have become a key technology for solving highly complex tasks, and require integration into future safety argumentations. New safety relevant aspects introduced by NN based algorithms are: representativity of test cases, robustness, inner representation and logic, and failure detection for NNs. In this paper, a general argumentation structure for safety cases respecting these four aspects is proposed together with possible sources of evidence. |
| GND Keywords: | Neuronales Netz; Beweisführung; Dekomposition; Sicherheit |
| Keywords: | neural network, safety argumentation, safety goal decomposition |
| DDC Classification: | 004 Computer science |
| RVK Classification: | ST 301 |
| Type: | Conferenceobject |
| URI: | https://fis.uni-bamberg.de/handle/uniba/47274 |
| Release Date: | 3. July 2020 |
| File | Description | Size | Format | |
|---|---|---|---|---|
| fisba47274.pdf | 124.89 kB | View/Open |
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University of Bamberg
University of Bamberg
