Holger Hermanns — accepted AI-conference papers

2026

Probabilistic Safety Verification of Neural Policies via Predicate Abstraction

Marcel Vinzent, Holger Hermanns, Jörg Hoffmann

AAAI 2026technical

Neural networks are increasingly important to learn action policies. Policy predicate abstraction (PPA) verifies safety of such a neural policy pi by over-approximating the state space subgraph induced by pi and using counterexample-guided abstraction refinement (CEGAR) to iteratively refine the abs

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2026

SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability

Hanwei Zhang, Luo Cheng, Rui Wen, Yang Zhang, Lijun Zhang, Holger Hermanns

AAAI 2026technical

Explainable AI (XAI) is crucial for building transparent and trustworthy machine learning systems, especially in high-stakes domains. Concept Bottleneck Models (CBMs) have emerged as a promising ante-hoc approach that provides interpretable, concept-level explanations by explicitly modeling human-un

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