38 citations · 56 across the 27 of their papers we have counts for
9 papers · 1 filter
Learning Symbolic Constraint Representations from Examples: A Neuro-Symbolic Approach
Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar +1
Learning user-defined concepts as constraint networks has been extensively studied in the constraint acquisition (CA) literature. However, existing approaches typically rely on int…
Automated Testing of LLM-Based Post Hoc Explainers Using Model Checking as an Oracle
Dennis Gross, Helge Spieker
Large language models (LLMs) are used as post hoc explainers of sequential decision-making policies, producing natural-language explanations of why an action was chosen. However, L…
Translating the Rashomon Effect to Sequential Decision-Making Tasks
Dennis Gross, Jørn Eirik Betten, Helge Spieker
The Rashomon effect describes the phenomenon where multiple models trained on the same data produce identical predictions while differing in which features they rely on internally.…
Verifying Memoryless Sequential Decision-making of Large Language Models
Dennis Gross, Helge Spieker, Arnaud Gotlieb
We introduce a tool for rigorous and automated verification of large language model (LLM)- based policies in memoryless sequential decision-making tasks. Given a Markov decision pr…
Bounded PCTL Model Checking of Large Language Model Outputs
Dennis Gross, Helge Spieker, Arnaud Gotlieb
In this paper, we introduce LLMCHECKER, a model-checking-based verification method to verify the probabilistic computation tree logic (PCTL) properties of an LLM text generation pr…
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning Policies
Dennis Gross, Helge Spieker
Deep reinforcement learning (RL) policies can demonstrate unsafe behaviors and are challenging to interpret. To address these challenges, we combine RL policy model checking--a tec…
Enhancing Manufacturing Quality Prediction Models through the Integration of Explainability Methods
Dennis Gross, Helge Spieker, Arnaud Gotlieb +1
This research presents a method that utilizes explainability techniques to amplify the performance of machine learning (ML) models in forecasting the quality of milling processes,…
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies
Dennis Gross, Helge Spieker
We introduce a method to verify stochastic reinforcement learning (RL) policies. This approach is compatible with any RL algorithm as long as the algorithm and its corresponding en…
Towards Trustworthy Automated Driving through Qualitative Scene Understanding and Explanations
Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar +1
Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the Qualitative Exp…