• DE
  • Applications
    • Energy
      • Co-Simulation of Multi-Modal Energy Systems
      • Distributed Artificial Intelligence
      • Data Integration and Processing
      • Energy-efficient Smart Cities
      • Power Systems Intelligence
      • Resilient Monitoring and Control
      • Standardized Systems Engineering and Assessment
      • Smart Grid Testing
    • Society
      • Mixed Reality
      • Human-Centered AI
      • Personal Pervasive Computing
      • Social Computing
    • Health
      • Data Management and Analysis for Health Services Research
      • Automation and Integration Technology
      • Assistive Technologies for Care and Health Professionals
      • Biomedical Devices and Systems
    • Manufacturing
      • Smart Human Robot Collaboration
      • Manufacturing Operations Management
      • Distributed Computing and Communication
      • Sustainable Manufacturing Systems
    • Transportation: Info
    • Living Labs
      • Model Factory
      • DAVE
      • LIFE
      • IDEAAL
      • Pflegedienstzentrale
      • SESA
      • Fliegerhorst (Air Base) Smart City
  • Services
    • Contract research
    • Digitalization consulting
    • Technology consulting
    • Technology training
    • Contract development
  • Research
    • Applied Artificial Intelligence (AAI)
      • Adversarial Resilience Learning (E)
    • Architecture Frameworks (AF)
    • Cyber-Resilient Architectures and Security (CRAS)
    • Embedded System Design (ESD)
    • Human Machine Cooperation (HMC)
    • Multi-Scale Multi-Rate Simulation (MS²)
    • Sustainability
    • Safety Relevant Cyber Physical Systems (SRCPS)
  • OFFIS
    • News
      • Events
    • Blog
    • Career
      • Vacancies
    • Publishing and Tools
      • Roadmaps and Studies
      • Tools and Platforms
      • Datawork | OFFIS Journals
      • Annual Reports
    • Publications
    • Projects
    • Persons
    • About us
      • Mission statement, values and compliance
      • Organization
      • OFFIS-Memberships
      • Cooperation Partners
      • Society of Friends
      • Spin-offs
      • History
    • Contact
      • Directions
  • General
  • DE
  • Applications
    • Energy
      • Co-Simulation of Multi-Modal Energy Systems
      • Distributed Artificial Intelligence
        • Transparency und acceptance of self-x-systems
        • Models for agent-based flexibility management
        • Open Science – freely available and open source scientific results
      • Data Integration and Processing
      • Energy-efficient Smart Cities
      • Power Systems Intelligence
      • Resilient Monitoring and Control
      • Standardized Systems Engineering and Assessment
      • Smart Grid Testing
    • Society
      • Mixed Reality
      • Human-Centered AI
      • Personal Pervasive Computing
      • Social Computing
    • Health
      • Data Management and Analysis for Health Services Research
        • Health Services Research
        • Information Logistics
        • Analytical Applications
        • Data Protection & Data Security
      • Automation and Integration Technology
      • Assistive Technologies for Care and Health Professionals
      • Biomedical Devices and Systems
    • Manufacturing
      • Smart Human Robot Collaboration
      • Manufacturing Operations Management
      • Distributed Computing and Communication
      • Sustainable Manufacturing Systems
    • Transportation: Info
    • Living Labs
      • Model Factory
      • DAVE
      • LIFE
      • IDEAAL
      • Pflegedienstzentrale
      • SESA
      • Fliegerhorst (Air Base) Smart City
  • Services
    • Contract research
    • Digitalization consulting
    • Technology consulting
    • Technology training
    • Contract development
  • Research
    • Applied Artificial Intelligence (AAI)
      • Adversarial Resilience Learning (E)
    • Architecture Frameworks (AF)
    • Cyber-Resilient Architectures and Security (CRAS)
    • Embedded System Design (ESD)
    • Human Machine Cooperation (HMC)
    • Multi-Scale Multi-Rate Simulation (MS²)
    • Sustainability
    • Safety Relevant Cyber Physical Systems (SRCPS)
  • OFFIS
    • News
      • Events
    • Blog
    • Career
      • Vacancies
        • About Applications
    • Publishing and Tools
      • Roadmaps and Studies
      • Tools and Platforms
      • Datawork | OFFIS Journals
      • Annual Reports
        • Archive
    • Publications
    • Projects
    • Persons
    • About us
      • Mission statement, values and compliance
        • Principles and mission statement
        • Diversity and Equality
        • Whistleblower System
      • Organization
        • General Assembly
        • Scientific Advisory Council
        • Administrative Council
      • OFFIS-Memberships
      • Cooperation Partners
      • Society of Friends
      • Spin-offs
      • History
    • Contact
      • Directions
  1. Home
  2. Research
  3. Applied Artificial Intelligence (AAI)
[Translate to english:]

Applied Artificial Intelligence (AAI)

AI has long arrived in our everyday life. But how do self-learning systems influence our society?

It searches for the most beautiful snapshots from our image masses, takes over the evaluation of complex sensor data in vehicles, enables ever better forecasts for the feed-in from renewable energies and increases the efficiency in production processes. OFFIS researches and develops AI-based solutions for current and future challenges in a digitalized world of living and working, which depends on the smooth functioning of increasingly complex infrastructures in the application areas of energy, health, traffic and production. Under the collective term "artificial intelligence," we understand in particular methods and procedures from machine learning as well as distributed, autonomous, and learning systems.

The Competence Cluster AAI addresses all opportunities and risks in the areas of Deep Learning and Machine Learning and bundles the competences of OFFIS in a cross-divisional research strategy.

© AdobeStock / issaronow

The difficulty in the development of artificial intelligence is not so much that of transferring complex calculations to computers and machines that are difficult for humans to solve intellectually. The far greater challenge is to teach computers the experiential learning that characterizes humans. Tasks that are simple for humans can quickly push an AI system to its limits. Human abilities such as intuitive action, social and emotional intelligence, and the ability to create an overall picture from different sensory impressions cannot be described by formal mathematical rules.

Self-learning systems have a high potential for various fields of application. They can acquire knowledge, filter out relevant observations from large amounts of data, draw logical conclusions from them, and - as impressively demonstrated by the example of the millennia-old game of Go - even develop their own action strategies. Artificial intelligence is increasingly finding its way into safety-critical areas of application, such as autonomous driving, medical applications, or decentralized energy supply. The fulfillment of safety relevant properties is essential for a successful approach in these domains.

Deep Learning and Deep Reinforcement Learning

Deep Learning is the field of machine learning that focuses on so-called "deep" neural networks. For some years now, Deep Learning has been showing a rapid development with astonishing success: Starting with face and speech recognition, which has found its way into our cell phones, through prediction for a safe integration of renewable energies into the power grid, to predictive maintenance and sustainable infrastructure development. If deep artificial neural networks are used as a strategy generator for autonomous software agents, this is called Deep Reinforcement Learning. With the learning of complex strategies at AlphaGo Zero, deep reinforcement learning has not only become an important scientific discipline with impressive results, but also an important building block in applied research. OFFIS bundles its methodical competences in the CC Applied AI, from image recognition to sustainable infrastructure development to methods for explainable, secure artificial intelligence and offers internal as well as external trainings.

Distributed Artificial Intelligence

Distributed artificial intelligence is the term used to describe (partially) autonomous hardware and software systems that cooperate with each other to solve problems that could not be solved by individual components. These so-called agents usually possess individual intelligence for monitoring and controlling technical processes, can communicate with other agents and are able to form different organizational forms depending on the situation and flexibly. Under the heading of "self-organization" OFFIS is especially working on nature-inspired methods for heuristic optimization in complex systems such as energy supply. In the focus of the research work is the agent-based self-organization of cyber-resilient Smart Grids, which are able to stabilize themselves independently in case of operational disturbances and to rebuild the supply independently in case of a blackout. In addition, OFFIS has been investigating energy-economic aspects such as the self-organized aggregation and marketing of the flexibility of decentralized energy plants in the context of virtual power plants for many years.

AI in Critical Infrastructures

Almost all our critical infrastructures nowadays are Cyber-Physical Systems (CPS). Here, IT system components and mechanical or electronic system components work together. Today, complex CPS can be found in practically every area of life: from vehicles with modern assistance systems to industrial process control and automation to digitalized energy systems, IT components are taking on increasingly important tasks in safety-relevant applications. The use of AI plays an important role here, since classical algorithms are no longer able to realize complex functions in these highly dynamic environments. However, the use of AI, especially in security-relevant CPSs, raises questions that have not yet been adequately answered: How can the correct functioning of an AI be guaranteed? How can decisions of AI-based systems be made transparent and comprehensible? Can AI also help to identify systemic weaknesses in security-relevant CPS? OFFIS pursues these research questions in the competence clusters Safety Relevant Cyber Physical Systems and Deep Learning and combines the proven expertise in the analysis and design of safety-critical systems with the well-founded methodological knowledge in machine learning.

Adversarial Resilience Learning

Critical infrastructures that support our civilization are becoming increasingly complex. They span domains that were never thought of before and face new threats: from volatile markets, a high proportion of supply-dependent energy sources to cyber attacks. Adversarial Resilience Learning is a new artificial intelligence methodology for the analysis and resilient operation of complex, critical cyber-physical systems.

Instead of considering artificial intelligence as a potential threat to the stability of our power supply, Adversarial Resilience Learning (ARL) turns the tables: two agents, attacker and defender, compete for control of a cyber-physical system. They have no explicit knowledge of the actions of the other side, but by observing the effects, the attacker explores the system and uncovers weaknesses, while the defender learns from the attacks to ensure resilient operation. By learning from each other, ARL agents help designers and decision makers to find weaknesses in the system and loopholes in market regulations, and operating teams to reliably manage the network even in complex, rapidly changing information situations.

Click on the image for more detailed information on Adversarial Resilience Learning

© AdobeStock / your123
  • Adversarial Resilience Learning (E)

Chairman of the Competence Cluster

Prof. Dr. techn. Susanne Boll
Prof. Dr. techn.
Susanne Boll

Prof. Dr. Ing. Astrid Nieße
Prof. Dr. Ing.
Astrid Nieße

Manager of the Competence Cluster

Dr.-Ing. Eric Veith
Dr.-Ing.
Eric Veith

Dr. rer. nat. Martin Tröschel
Dr. rer. nat.
Martin Tröschel

Persons

A

Dr. Ing. Larbi Abdenebaoui

E-Mail: larbi.abdenebaoui(at)offis.de, Phone: +49 441 9722-730, Room: E124

B

Stephan Balduin

E-Mail: stephan.balduin(at)offis.de, Phone: +49 441 9722-298, Room: E61

Aleksandr Berezin

E-Mail: aleksandr.berezin(at)offis.de, Phone: +49 441 9722-545, Room: E61

Prof. Dr. techn. Susanne Boll

E-Mail: susanne.boll(at)informatik.uni-oldenburg.de, Phone: +49 441 9722-213, Room: O 47

D

Maria Fernanda Davila Restrepo

E-Mail: maria.fernanda.davila.restrepo(at)offis.de, Phone: +49 441 9722-744

Lisa Dawel

E-Mail: lisa.dawel(at)offis.de, Phone: +49 441 9722-745

Viktor Dmitriyev

E-Mail: viktor.dmitriyev(at)offis.de, Phone: +49 441 9722-181, Room: I6-E02

E

Lars Elend

E-Mail: lars.elend(at)uni-oldenburg.de, Phone: +49 441 798-2863

F

Prof. Dr. Martin Fränzle

E-Mail: martin.fraenzle(at)offis.de, Phone: +49 441 9722-500, Room: D 119/120

Emilie Frost

E-Mail: Emilie.Frost(at)offis.de, Phone: +49 441 9722-582, Room: E62

Eike Furuno

E-Mail: eike.furuno(at)offis.de, Phone: +49 441 9722-571

H

Prof. Dr.-Ing. Axel Hahn

Lasse Hammer

E-Mail: lasse.hammer(at)offis.de, Phone: +49 441 9722-139, Room: U26

Stefanie Holly

E-Mail: stefanie.holly(at)offis.de, Phone: +49 441 9722-732, Room: E63

K

Christian Kowalski

E-Mail: christian.kowalski(at)offis.de, Phone: +49 441 9722-706, Room: E64

Carsten Krüger

E-Mail: carsten.krueger(at)offis.de, Phone: +49 441 9722-733, Room: E82

Rene Kuchenbuch

E-Mail: rene.kuchenbuch(at)offis.de, Phone: +49 441 9722-218, Room: I6-U05

L

Daniel Lange

E-Mail: daniel.lange(at)offis.de, Phone: +49 441 9722-188, Room: U26

Prof. Dr. rer. nat. Sebastian Lehnhoff

E-Mail: sebastian.lehnhoff(at)offis.de, Phone: +49 441 9722-240, Room: O50

M

Prof. Dr.-Ing. habil. Jorge Marx Gómez

E-Mail: jorge.marx-gomez(at)offis.de, Phone: +49 441 798 - 4470, Room: A4-3-315

Pranav Megarajan

E-Mail: pranav.megarajan(at)offis.de, Phone: +49 441 9722-584

N

Prof. Dr. Ing. Astrid Nieße

E-Mail: astrid.niesse(at)offis.de

O

Frauke Oest

E-Mail: frauke.oest(at)offis.de, Phone: +49 441 9722-137, Room: E88

Dr. rer. nat. Frank Oppenheimer

E-Mail: frank.oppenheimer(at)offis.de, Phone: +49 441 9722-285, Room: O43

P

Leonora Posega

E-Mail: leonora.posega(at)offis.de, Phone: +49 441 9722-286, Room: O80

Erika Puiutta

E-Mail: erika.puiutta(at)offis.de, Phone: +49 441 9722-504

R

Malin Radtke

E-Mail: malin.radtke(at)offis.de, Phone: +49 441 9722-125, Room: E128

Amin Raeiszadeh

E-Mail: amin.raeiszadeh(at)offis.de, Phone: +49 441 9722-156, Room: E83

S

Jens Sager

E-Mail: jens.sager(at)offis.de, Phone: +49 441 9722-561, Room: E62

Jürgen Sauer

E-Mail: juergen.sauer(at)offis.de, Phone: +49 441 9722-122, Room: O46

Sanja Stark

E-Mail: sanja.stark(at)offis.de, Phone: +49 441 9722-436, Room: E63

Dr. rer. nat. Tim Claudius Stratmann

E-Mail: Tim.Stratmann(at)offis.de, Phone: +49 441 9722-431, Room: O88

T

Dr. rer. nat. Martin Tröschel

E-Mail: martin.troeschel(at)offis.de, Phone: +49 441 9722-150, Room: E128

V

Dr.-Ing. Eric Veith

E-Mail: eric.veith(at)offis.de, Room: E68

W

Nils Wenninghoff

E-Mail: nils.wenninghoff(at)offis.de, Phone: +49 441 9722-124, Room: E61

Ani Withöft

E-Mail: ani.withoeft(at)offis.de, Phone: +49 441 9722-462, Room: E106

Torge Wolff

E-Mail: torge.wolff(at)offis.de, Phone: +49 441 9722-216, Room: E63

EN: Alle Personen aus dem Competence Cluster Applied Artificial Intelligence (AAI)

Projects

A

AgenC

Automatische Generierung von Modellen für Prädiktion, Testen und Monitoring cyber-physischer Systeme

Duration: 2022 - 2025

D

DEER

Dezentraler Redispatch (DEER): Schnittstellen für die Flexibilitätsbereitstellung

Duration: 2022 - 2025

M

MeSSeR

Mensch-unterstützte Synthese von Simulationsdaten für die Robotik

Duration: 2021 - 2023

P

PLATON

Distributed computing platform for radar-based 3D environment sensing in safe autonomous driving

Duration: 2022 - 2025

R

ReHOpE

Reduktion körperlicher Belastungen von Handwerksberufen durch optimierte Exoskelette

Duration: 2022 - 2025

REMARK

Resilienz im digitalisierten Stromsystem: Toolbox zur Bewertung von Systemdienstleistungsmärkten

Duration: 2022 - 2024

RenovAIte

Boosting Renovation Industry with AI

Duration: 2022 - 2025

RESili8

Resilience for Cyber-Physical Energy Systems

Duration: 2022 - 2025

Publications

2022

AI-supported Natural Language Processing in project management -capabilities and research agenda

Helge F.R. Nuhn; Alfred Oswald; Agnetha Flore; Rüdiger Lang; IPMA 10th Research Conference 2022; Juni / 2022

BIB
Application of Recurrent Graph Convolutional Networks to the Neural State Estimation Problem

Alexander Berezin, Stephan Balduin, Thomas Oberließen, Eric Veith, Sebastian Peter, and Sebastian Lehnhoff; International Journal of Electrical and Electronic Engineering & Telecommunications; 2022

URL BIB
Choosing the right model for unified flexibility modeling

Brandt, Jonathan and Frost, Emilie and Ferenz, Stephan and Tiemann, Paul Hendrik and Bensmann, Astrid and Hanke-Rauschenbach, Richard and Nieße, Astrid; Energy Informatics; 2022

DOI BIB
How can machine learning improve waste-to-energy plant operation

Alexandra Pehlken, Henriette Garmatter, Lisa Dawel, Fabian Cyris, Hendrik Beck, Fenja Schwark, Roland Scharf, Astrid Nieße; ICE IEEE; 2022

BIB
ILMICA - Interactive Learning Model of Image Collage Assessment: A Transfer Learning Approach for Aesthetic Principles

Withöft, Ani and Abdenebaoui, Larbi and Boll, Susanne; MultiMedia Modeling; 2022

URL DOI BIB
Mit Künstlicher Intelligenz zu nachhaltigen Geschäftsmodellen – Nachhaltigkeit von, durch und mit KI

Boll, S., Schnell, M., Dowling, M., Faisst, W., Mordvinova, O, Pflaum, A., Rabe, M., Veith, E., Niesse, A., Gülpen, C., Terzidis, O., Riss, U., Eckerle, C., Manthey, S., Pehlken, A., Zielinski, O.; Februar / 2022

BIB
Sampling Strategies for Static Powergrid Models

Stephan Balduin. and Eric Veith. and Sebastian Lehnhoff.; Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH,; 2022

DOI BIB
The application of image recognition methods to improve the performance of waste-to-energy plants

Fenja Schwark, Henriette Garmatter, Maria Davila, Lisa Dawel, Alexandra Pehlken, Fabian Cyris, Roland Scharf; EnviroInfo 2022; 09 / 2022

BIB

2021

A Graph-Transformational Approach to Swarm Computation

Abdenebaoui, Larbi and Kreowski, Hans-Jörg and Kuske, Sabine; Entropy; 04 / 2021

URL DOI BIB
A User-Centered Approach for Recognizing Convenience Images in Personal Photo Collections

Maszuhn, Matthias and Abdenebaoui, Larbi and Boll, Susanne; 2021 International Conference on Content-Based Multimedia Indexing (CBMI); 06 / 2021

DOI BIB
EN: Alle Publikationen aus dem Competence Cluster Applied Artificial Intelligence (AAI)
PrivacyData TransparencyContactLegals