27 citations · 64 across the 44 of their papers we have counts for
47 papers
Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility
Simon Kohaut, Felix Divo, Julius Hahnewald +4
Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential for safe autonomous navigation. Never…
Right in Time: Reactive Reasoning in Regulated Traffic Spaces
Simon Kohaut, Benedict Flade, Julian Eggert +2
Exact inference in probabilistic First-Order Logic offers a promising yet computationally costly approach for regulating the behavior of autonomous agents in shared traffic spaces.…
Reactive Knowledge Representation and Asynchronous Reasoning
Simon Kohaut, Benedict Flade, Julian Eggert +2
Exact inference in complex probabilistic models often incurs prohibitive computational costs. This challenge is particularly acute for autonomous agents in dynamic environments tha…
CycliST: A Video Language Model Benchmark for Reasoning on Cyclical State Transitions
Simon Kohaut, Daniel Ochs, Shun Zhang +4
We present CycliST, a novel benchmark dataset designed to evaluate Video Language Models (VLM) on their ability for textual reasoning over cyclical state transitions. CycliST captu…
QuAnTS: Question Answering on Time Series
Felix Divo, Maurice Kraus, Anh Q. Nguyen +5
Text offers intuitive access to information. This can, in particular, complement the density of numerical time series, thereby enabling improved interactions with time series model…
The Constitutional Controller: Doubt-Calibrated Steering of Compliant Agents
Simon Kohaut, Felix Divo, Navid Hamid +4
Ensuring reliable and rule-compliant behavior of autonomous agents in uncertain environments remains a fundamental challenge in modern robotics. Our work shows how neuro-symbolic s…
Tags for DAGs: Graph Refinement with Meta-Informed Relations
Florian Peter Busch, Moritz Willig, Florian Guldan +2
Causal discovery has shifted from data-centric methods to hybrid strategies that integrate semantic knowledge from experts or large language models (LLMs). Such external informatio…
Causal Explanations Over Time: Articulated Reasoning for Interactive Environments
Sebastian Rödling, Matej Zečević, Devendra Singh Dhami +1
Structural Causal Explanations (SCEs) can be used to automatically generate explanations in natural language to questions about given data that are grounded in a (possibly learned)…
METok: Multi-Stage Event-based Token Compression for Efficient Long Video Understanding
Mengyue Wang, Shuo Chen, Kristian Kersting +2
Recent advances in Video Large Language Models (VLLMs) have significantly enhanced their ability to understand video content. Nonetheless, processing long videos remains challengin…
Scaling Probabilistic Circuits via Data Partitioning
Jonas Seng, Florian Peter Busch, Pooja Prasad +3
Probabilistic circuits (PCs) enable us to learn joint distributions over a set of random variables and to perform various probabilistic queries in a tractable fashion. Though the t…
Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal Dependencies in Complex Data
Harsh Poonia, Felix Divo, Kristian Kersting +1
Causality in time series can be challenging to determine, especially in the presence of non-linear dependencies. Granger causality helps analyze potential relationships between var…
Probabilistic Mission Design for Neuro-Symbolic Unmanned Aircraft Systems
Simon Kohaut, Benedict Flade, Daniel Ochs +3
Advanced Air Mobility (AAM) is a growing field that demands accurate and trustworthy models of legal concepts and restrictions for navigating Unmanned Aircraft Systems (UAS). In ad…
StaR Maps: Unveiling Uncertainty in Geospatial Relations
Simon Kohaut, Benedict Flade, Julian Eggert +2
The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation.…
Answer Set Networks: Casting Answer Set Programming into Deep Learning
Arseny Skryagin, Daniel Ochs, Phillip Deibert +4
Although Answer Set Programming (ASP) allows constraining neural-symbolic (NeSy) systems, its employment is hindered by the prohibitive costs of computing stable models and the CPU…
Graph Neural Networks Need Cluster-Normalize-Activate Modules
Arseny Skryagin, Felix Divo, Mohammad Amin Ali +2
Graph Neural Networks (GNNs) are non-Euclidean deep learning models for graph-structured data. Despite their successful and diverse applications, oversmoothing prohibits deep archi…
Hybrid Many-Objective Optimization in Probabilistic Mission Design for Compliant and Effective UAV Routing
Simon Kohaut, Nikolas Hohmann, Sebastian Brulin +6
Advanced Aerial Mobility encompasses many outstanding applications that promise to revolutionize modern logistics and pave the way for various public services and industry uses. Ho…
The Constitutional Filter: Bayesian Estimation of Compliant Agents
Simon Kohaut, Felix Divo, Benedict Flade +3
Predicting agents impacted by legal policies, physical limitations, and operational preferences is inherently difficult. In recent years, neuro-symbolic methods have emerged, integ…
Forecasting Company Fundamentals
Felix Divo, Eric Endress, Kevin Endler +2
Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and economet…
xLSTM-Mixer: Multivariate Time Series Forecasting by Mixing via Scalar Memories
Maurice Kraus, Felix Divo, Devendra Singh Dhami +1
Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal a…
Systems with Switching Causal Relations: A Meta-Causal Perspective
Moritz Willig, Tim Nelson Tobiasch, Florian Peter Busch +3
Most work on causality in machine learning assumes that causal relationships are driven by a constant underlying process. However, the flexibility of agents' actions or tipping poi…
BlendRL: A Framework for Merging Symbolic and Neural Policy Learning
Hikaru Shindo, Quentin Delfosse, Devendra Singh Dhami +1
Humans can leverage both symbolic reasoning and intuitive reactions. In contrast, reinforcement learning policies are typically encoded in either opaque systems like neural network…
Towards Probabilistic Clearance, Explanation and Optimization
Simon Kohaut, Benedict Flade, Devendra Singh Dhami +2
Employing Unmanned Aircraft Systems (UAS) beyond visual line of sight (BVLOS) is an endearing and challenging task. While UAS have the potential to significantly enhance today's lo…
EXPIL: Explanatory Predicate Invention for Learning in Games
Jingyuan Sha, Hikaru Shindo, Quentin Delfosse +2
Reinforcement learning (RL) has proven to be a powerful tool for training agents that excel in various games. However, the black-box nature of neural network models often hinders o…
Mission Design for Unmanned Aerial Vehicles using Hybrid Probabilistic Logic Programs
Simon Kohaut, Benedict Flade, Devendra Singh Dhami +2
Advanced Air Mobility (AAM) is a growing field that demands a deep understanding of legal, spatial and temporal concepts in navigation. Hence, any implementation of AAM is forced t…