activity
20222026
most citedSystematic Comparison of Software Agents and Digital Twins: Differences, Similarities, and Synergies in Industrial Production

25 citations · 84 across the 35 of their papers we have counts for

collaborators

35 papers

cs.AI2026

Towards an Intention Abstraction Layer for Autonomous Industrial Systems

Artan Markaj, Raphael Höfer, Felix Gehlhoff

Modern industrial environments increasingly run many autonomous subsystems at once - schedulers, energy managers, vehicle fleets - each pursuing its own goals while sharing the sam…

cs.MA2026

World-Model-Aware Responsibility Allocation in Heterogeneous Logistics Systems

Artan Markaj, Niklas Jobs, Felix Gehlhoff

Logistics systems increasingly mix \emph{autonomous logistic equipment} (ALE) with non-autonomous machinery under a central control system (CS), where the best decision-maker depen…

cs.SE2026

An AutomationML Domain Library for the Formalized Process Description

Hamied Nabizada, Rainer Drath, Felix Gehlhoff +1

The Formalized Process Description (FPD) according to VDI/VDE 3682 provides a standardized graphical notation for describing processes across engineering domains but lacks a standa…

eess.SY2026

A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents

Javal Vyas, Milapji Singh Gill, Artan Markaj +2

Fault recovery in process plants still relies heavily on plant operators, especially when faults fall outside predefined supervisory logic. Operators interpret alarms, procedures,…

eess.SY2026

From Detection to Action: Using LLM Agents for Fault-Tolerant Control

Javal Vyas, Milapji Singh Gill, Artan Markaj +2

We propose an agentic Large Language Model (LLM) framework for active Fault-Tolerant Control (FTC) that transforms fault detection outputs into constraint-aware recovery actions gr…

cs.GT2026

Market Strategy Evaluation for Prosumers in Local Electricity Markets

Lukas Peter Wagner, Raoul Bisson, Felix Gehlhoff

Prosumers equipped with distributed generation and flexible loads form autonomous cyber-physical energy systems that control local resources and participate in local energy markets…

cs.AI2026

From Capability Models to Automated Planning: An AAS-Native Approach for Automatic PDDL Generation

Hamied Nabizada, Thomas Wirt, Luis Miguel Vieira da Silva +2

Engineers designing production systems need to verify that a given layout supports all required production sequences. Automated planning techniques can answer such questions, but f…

cs.AI2026

An LLM-Based Assistance System for Intuitive and Flexible Capability-Based Planning

Luis Miguel Vieira da Silva, Nicolas König, Felix Gehlhoff

In modern industry, dynamic environments and the complexity of modular and reconfigurable resources require automated planning of process sequences. Capability-based planning appro…

cs.AI2026

Foundation-Model-Based Agents in Industrial Automation: Purposes, Capabilities, and Open Challenges

Vincent Henkel, Felix Gehlhoff, David Kube +13

Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision support, process monitoring, and e…

eess.SY2026

Coupling Scenario-Based Grid Simulations with State Estimation: Measurement Requirements for Low-Voltage Networks under the German Energy Transition Pathway

Nane Zimmermann, Lukas P. Wagner, Luca von Rönn +3

Increasing penetration of electric vehicles, heat pumps, and rooftop photovoltaics is creating thermal and voltage stress in low-voltage distribution grids. This work links the Ger…

cs.AI2025

Benchmark for Planning and Control with Large Language Model Agents: Blocksworld with Model Context Protocol

Niklas Jobs, Luis Miguel Vieira da Silva, Jayanth Somashekaraiah +3

Industrial automation increasingly requires flexible control strategies that can adapt to changing tasks and environments. Agents based on Large Language Models (LLMs) offer potent…

cs.AI2025

Bridging Engineering and AI Planning through Model-Based Knowledge Transformation for the Validation of Automated Production System Variants

Hamied Nabizada, Lasse Beers, Alain Chahine +3

Engineering models created in Model-Based Systems Engineering (MBSE) environments contain detailed information about system structure and behavior. However, they typically lack sym…

cs.AI2025

Consistency Verification in Ontology-Based Process Models with Parameter Interdependencies

Tom Jeleniewski, Hamied Nabizada, Jonathan Reif +2

The formalization of process knowledge using ontologies enables consistent modeling of parameter interdependencies in manufacturing. These interdependencies are typically represent…

cs.SE2025

An Expert Survey on Models and Digital Twins

Jonathan Reif, Daniel Dittler, Milapji Singh Gill +5

Digital Twins (DTs) are becoming increasingly vital for future industrial applications, enhancing monitoring, control, and optimization of physical assets. This enhancement is made…

cs.AI2025

Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL

Tom Westermann, Aljosha Köcher, Felix Gehlhoff

AutomationML (AML) enables standardized data exchange in engineering, yet existing recommendations for proper AML modeling are typically formulated as informal and textual constrai…

cs.AI2025

Integrating AI Planning Semantics into SysML System Models for Automated PDDL File Generation

Hamied Nabizada, Tom Jeleniewski, Lasse Beers +3

This paper presents a SysML profile that enables the direct integration of planning semantics based on the Planning Domain Definition Language (PDDL) into system models. Reusable s…

cs.AI2025

Representing Time-Continuous Behavior of Cyber-Physical Systems in Knowledge Graphs

Milapji Singh Gill, Tom Jeleniewski, Felix Gehlhoff +1

Time-continuous dynamic models are essential for various Cyber-Physical System (CPS) applications. To ensure effective usability in different lifecycle phases, such behavioral info…

cs.SE2025★ 3 cited

Beyond Formal Semantics for Capabilities and Skills: Model Context Protocol in Manufacturing

Luis Miguel Vieira da Silva, Aljosha Köcher, Felix Gehlhoff

Explicit modeling of capabilities and skills -- whether based on ontologies, Asset Administration Shells, or other technologies -- requires considerable manual effort and often res…

cs.AI2025★ 7 cited

Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants

Milapji Singh Gill, Javal Vyas, Artan Markaj +2

Advances in Automation and Artificial Intelligence continue to enhance the autonomy of process plants in handling various operational scenarios. However, certain tasks, such as fau…

cs.AI2025★ 3 cited

Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces

Luis Miguel Vieira da Silva, Aljosha Köcher, Nicolas König +2

Modern automation systems increasingly rely on modular architectures, with capabilities and skills as one solution approach. Capabilities define the functions of resources in a mac…

cs.AI2025

Automatic Mapping of AutomationML Files to Ontologies for Graph Queries and Validation

Tom Westermann, Malte Ramonat, Johannes Hujer +2

AutomationML has seen widespread adoption as an open data exchange format in the automation domain. It is an open and vendor neutral standard based on the extensible markup languag…

cs.SE2024★ 1 cited

Exploring LLMs for Verifying Technical System Specifications Against Requirements

Lasse M. Reinpold, Marvin Schieseck, Lukas P. Wagner +2

Requirements engineering is a knowledge intensive process and crucial for the success of engineering projects. The field of knowledge-based requirements engineering (KBRE) aims to…

eess.SY2024

Semantic model for the description of energy data in the Module Type Package

Leif-Thore Reiche, Felix Gehlhoff, Alexander Fay

Modular production systems that employ the Module Type Package (MTP) to describe module interfaces can, at present, only communicate energy data through proprietary solutions. Due…

cs.AI2024★ 1 cited

Model-based Workflow for the Automated Generation of PDDL Descriptions

Hamied Nabizada, Tom Jeleniewski, Felix Gehlhoff +1

Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already emb…