Strategies for Safety Goal Decomposition for Neural Networks
Faculty/Professorship: | Cognitive Systems |
Author(s): | Schwalbe, Gesina ![]() |
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 |

originated at the
University of Bamberg
University of Bamberg