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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Differential Safety Testing of Deep RL Agents Enabled by Automata Learninguracy 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

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Deep Neural Networks, Explanations, and?Rationalityal “explanation” for a decision is a chronicle of the steps used to arrive at the decision. Herb Simon’s “bounded rationality” is the observation that the ability of a human brain to handle algorithmic complexity and data is limited. As a consequence, human decision-making in complex cases mixes som

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Shielded Reinforcement Learning for?Hybrid Systemss state, is known to be intricately hard. Reinforcement learning has been leveraged to construct near-optimal controllers, but their behavior is not guaranteed to be safe, even when it is encouraged by reward engineering. One way of imposing safety to a learned controller is to use a ., which is cor

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What, Indeed, is an?Achievable Provable Guarantee for?Learning-Enabled Safety-Critical Systemsnges. Among the challenges, it is known that a rigorous, yet practical, way of achieving safety guarantees is one of the most prominent. In this paper, we first discuss the engineering and research challenges associated with the design and verification of such systems. Then, based on the observation

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DeepAbstraction++: Enhancing Test Prioritization Performance via Combined Parameterized Boxess. Subsequently, the DeepAbstraction algorithm has recently become one of the leading techniques in this area. It employs a box-abstraction concept, the efficiency of which depends on the tau parameter, the clustering parameter, that influences the size of these boxes. The conclusion of the previous

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