38 citations · 56 across the 27 of their papers we have counts for
29 papers
Automatic Cause Determination in Road Scene Understanding Using Qualitative Reasoning and Four-Valued Logic
Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar +1
Road scene understanding in automated driving (AD) aims to build a comprehensive analysis of video sequences taken on the road by embedded or fixed cameras (e.g., mounted on vertic…
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…
Probabilistic Model Checking of Autoregressive Neural Sequence Models
Helge Spieker, Dennis Gross, Arnaud Gotlieb
Test-set accuracy is silent on two issues that matter when deploying autoregressive neural sequence models: how much probability mass the system under test (SUT) places on constrai…
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…
Gimitest: A Comprehensive Tool for Testing Reinforcement Learning Policies
Dennis Gross, Quentin Mazouni, Helge Spieker +1
Reinforcement learning (RL) policies can be unsafe and vulnerable to attacks. Ensuring their reliability is often a pain point as existing automated testing methods target only sel…
Metamorphic Testing with the Rashomon Set: Explanation Faithfulness in Machine Learning
Helge Spieker, Jørn Eirik Betten, Arnaud Gotlieb
Multiple machine learning models can achieve near-equivalent predictive performance on the same task, yet provide divergent feature-based explanations. This is called the Rashomon…
Semi-supervised CAPP Transformer Learning via Pseudo-labeling
Dennis Gross, Helge Spieker, Arnaud Gotlieb +3
High-level Computer-Aided Process Planning (CAPP) generates manufacturing process plans from part specifications. It suffers from limited dataset availability in industry, reducing…
Context-Aware Autoencoders for Anomaly Detection in Maritime Surveillance
Divya Acharya, Pierre Bernab'e, Antoine Chevrot +3
The detection of anomalies is crucial to ensuring the safety and security of maritime vessel traffic surveillance. Although autoencoders are popular for anomaly detection, their ef…
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…
Rashomon in the Streets: Explanation Ambiguity in Scene Understanding
Helge Spieker, Jørn Eirik Betten, Arnaud Gotlieb +2
Explainable AI (XAI) is essential for validating and trusting models in safety-critical applications like autonomous driving. However, the reliability of XAI is challenged by the R…
Efficiently Ranking Software Variants with Minimal Benchmarks
Théo Matricon, Mathieu Acher, Helge Spieker +1
Benchmarking is a common practice in software engineering to assess the qualities and performance of software variants, coming from multiple competing systems or from configuration…
Metamorphic Testing of Multimodal Human Trajectory Prediction
Helge Spieker, Nadjib Lazaar, Arnaud Gotlieb +1
Context: Predicting human trajectories is crucial for the safety and reliability of autonomous systems, such as automated vehicles and mobile robots. However, rigorously testing th…
Reusable Test Suites for Reinforcement Learning
Jørn Eirik Betten, Quentin Mazouni, Dennis Gross +2
Reinforcement learning (RL) agents show great promise in solving sequential decision-making tasks. However, validating the reliability and performance of the agent policies' behavi…
Prompting for Performance: Exploring LLMs for Configuring Software
Helge Spieker, Théo Matricon, Nassim Belmecheri +7
Software systems usually provide numerous configuration options that can affect performance metrics such as execution time, memory usage, binary size, or bitrate. On the one hand,…
Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks
Nassim Belmecheri, Arnaud Gotlieb, Nadjib Lazaar +1
This paper investigates the integration of graph neural networks (GNNs) with Qualitative Explainable Graphs (QXGs) for scene understanding in automated driving. Scene understanding…
Policy Testing with MDPFuzz (Replicability Study)
Quentin Mazouni, Helge Spieker, Arnaud Gotlieb +1
In recent years, following tremendous achievements in Reinforcement Learning, a great deal of interest has been devoted to ML models for sequential decision-making. Together with t…
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…
Safety-Oriented Pruning and Interpretation of Reinforcement Learning Policies
Dennis Gross, Helge Spieker
Pruning neural networks (NNs) can streamline them but risks removing vital parameters from safe reinforcement learning (RL) policies. We introduce an interpretable RL method called…
Efficient Milling Quality Prediction with Explainable Machine Learning
Dennis Gross, Helge Spieker, Arnaud Gotlieb +2
This paper presents an explainable machine learning (ML) approach for predicting surface roughness in milling. Utilizing a dataset from milling aluminum alloy 2017A, the study empl…
Enhancing RL Safety with Counterfactual LLM Reasoning
Dennis Gross, Helge Spieker
Reinforcement learning (RL) policies may exhibit unsafe behavior and are hard to explain. We use counterfactual large language model reasoning to enhance RL policy safety post-trai…
Evaluating Human Trajectory Prediction with Metamorphic Testing
Helge Spieker, Nassim Belmecheri, Arnaud Gotlieb +1
The prediction of human trajectories is important for planning in autonomous systems that act in the real world, e.g. automated driving or mobile robots. Human trajectory predictio…
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,…