Titlebook: Bridging the Gap Between AI and Reality; First International Bernhard Steffen Conference proceedings 2024 The Editor(s) (if applicable) an

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預感

跑過

https://doi.org/10.1007/978-3-8349-8649-8d combined parameterized boxes. This approach leverages the collective verdicts of monitors with various tau values to evaluate network predictions. We assign appropriate weights to these verdicts to ensure that no single verdict influences the decision-making process, thereby ensuring balance. Furt

Biofeedback

Managementmoden in der Verwaltunguracy guarantees on learned models are not strictly necessary. Through a combination of automata learning, testing, and statistics, we perform testing-based verification with statistical guarantees in the absence of guarantees on the learned automata. We showcase our approach by testing deep reinfor

exigent

Managementmoden in der Verwaltungory. Our tool operates on pre-trained, black-box classification DNNs, and generates input samples belonging to an output category of interest. It measures the DNN’s susceptibility to adversarial inputs around these inputs, and aggregates the results to infer the overall global categorial robustness

朦朧

https://doi.org/10.1007/978-3-642-87406-2imulation-based testing and the operation of LEAS is the monitoring and enforcement of safety, security and performance properties and the acquisition of information for the system’s operation in its environment. Finally, we show how the FOCETA approach has been applied to realistic continuous engin

Ventilator

傲慢物

羽毛長成

Shielded Reinforcement Learning for?Hybrid Systemstistical safety guarantees with a prototype implementation and .. Furthermore, we study the impact of the synthesized shield when applied as either a pre-shield (applied before learning a controller) or a post-shield (only applied after learning a controller). We experimentally demonstrate superiori

eucalyptus

DeepAbstraction++: Enhancing Test Prioritization Performance via Combined Parameterized Boxesd combined parameterized boxes. This approach leverages the collective verdicts of monitors with various tau values to evaluate network predictions. We assign appropriate weights to these verdicts to ensure that no single verdict influences the decision-making process, thereby ensuring balance. Furt