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Strategies for Safety Goal Decomposition for Neural Networks
Schwalbe, Gesina; Schels, Martin (2020): Strategies for Safety Goal Decomposition for Neural Networks, in: Bamberg: Otto-Friedrich-Universität, doi: 10.20378/irb-47274.
Faculty/Chair:
Author:
Corporate Body:
ACM Computer Science in Cars Symposium (CSCS), 3, 2019, Kaiserslautern
Publisher Information:
Year of publication:
2020
Pages:
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
Year of first publication:
2019
Language:
English
DOI:
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:
RVK Classification:
Type:
Conferenceobject
Activation date:
July 3, 2020
Permalink
https://fis.uni-bamberg.de/handle/uniba/47274