Strategies for Safety Goal Decomposition for Neural Networks
Faculty/Professorship: | Cognitive Systems |
Author(s): | Schwalbe, Gesina ![]() |
Conference: | 3rd ACM Computer Science in Cars Symposium – Future Challenges in Artificial Intelligence & Security for Autonomous Vehicles, Kaiserslautern |
Publisher Information: | München : German Chapter of the ACM e.V. |
Year of publication: | 2019 |
Pages: | 3 |
Language(s): | English |
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/53985 |
Release Date: | 10. May 2022 |

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University of Bamberg