Artificial Intelligence and Integrated Computer Systems (AIICS)

The Division Artificial Intelligence and Integrated Computer Systems is part of the Department of Computer and Information Science. The division's main focus is research and teaching in artificial intelligence, its theoretical foundations and its applications. 

The robotdog Spot hands over a first aid kit to a kneeling man Photo credit Fredrik Streiffert

The division has around 60 employees and consists of five units (research laboratories):

  • Artificial Intelligence (AILAB)
  • Machine Reasoning (MR)
  • Natural Language Processing (NLP)
  • Reasoning and Learning (ReaL)
  • Theoretical Computer Science (TCSLAB)


For a presentation of each unit, please see below.

Unit Artificial Intelligence (AILAB)

Research in AILAB focuses on the theoretical and practical aspects associated with the representation of knowledge and the reasoning and inference techniques associated with the processing of knowledge as used by both physical and software artifacts.

Research groups

AILAB includes three topic-focused research groups:

  • Cognitive Robotics
  • Applied Logic
  • Planning and Diagnosis

Research topics

Research topics of current interest include the following:

  • Autonomous Intelligent Systems: From our research perspective, autonomous systems are man-made physical systems containing computational equipment and software that provide them with capabilities for receiving and comprehending sensory data, for reasoning, and for rational action in their environment, which is independent of human control. The degree of independence varies relative to task and purpose. Consequently, systems can be more or less autonomous. Our focus is on studying and developing hardware, software, and algorithms for autonomous intelligent systems that interact with other agents and human operators. The AILAB has more than two decades of experience with the development of air and ground autonomous systems used as demonstration platforms for the lab’s research results.
  • Multi-Agent Systems: Research with multi-agent systems involves studying and developing AI problem-solving and control paradigms for single and multi-agent systems where issues related to interaction, cooperation, autonomy, and distribution are paramount.
  • Cognitive Robotics: Research in cognitive robotics involves studying and developing higher-level cognitive functions that involve reasoning and empirically testing such functions on deployed robotic systems. Central to the endeavor is the efficient use and representation of models of the robot and its embedding environment and the grounding of these models in such environments through sensing and perception systems. Logic is often the modeling language of choice in this respect.
  • Applied Logic: Research in applied logic involves the study and use of logic as a representational mechanism for constructing models and a reasoning mechanism for using such models in intelligent artifacts such as software agents or robotic systems.
  • Planning and Diagnosis: Research with automated planning involves studying and developing algorithms that generate strategies or sequences of actions to achieve goals. Research with automated diagnosis involves studying and developing of algorithms that capitalise on cause-effect information in a system or system environment to troubleshoot and provide explanations and remedies for faulty system or cognitive behavior.

The AILAB, formerly known as the Knowledge Processing Laboratory (KPLAB), was established in 1996. Patrick Doherty, Professor, heads the lab. There are currently two professors, two research assistants, and three research engineers, of which one conducts his PhD studies.

Unit Machine Reasoning (MR)

Within our research laboratory, we develop machines that can reason and act in complex environments. Our primary research area is Automated Planning, which we complement with techniques from Machine Learning, Combinatorial Optimisation and Operations Research.

Research topics

Our main topics of interest include:

  • Theory of Planning: We contribute to the theoretical foundations of Automated Planning, studying the complexity of planning problems and algorithms.
  • Learning Planning Models: We develop algorithms that extract the dynamics of an observed environment to learn compact descriptions of planning tasks.
  • Efficient Planning Algorithms: We design and implement scalable planning algorithms, mainly based on heuristic state-space search.
  • Generalised Planning: We create methods for learning how to solve a whole class of tasks efficiently.
  • Planning and Reinforcement Learning: We combine the interpretability of planning with the flexibility of reinforcement learning.


In summary, we strive to create AI systems that efficiently solve intricate sequential decision-making problems, based on solid theoretical foundations and practical algorithms.

The unit is lead by Jendrik Seipp, Associate professor.

Unit Natural Language Processing (NLP)

We develop and analyse computational models of human language. Our work ranges from basic research on algorithms and machine learning to applied research in language technology and computational social science.

Our current focus is on analysing and enhancing neural language models. Specifically, we are working on methods for improving model efficiency, trustworthiness, and usefulness for lesser-resourced languages. We also have a long-standing interest in work on the intersection of natural language processing and theoretical computer science.

We are participating in several national and international research collaborations, including the Wallenberg AI, Autonomous Systems and Software Program (WASP), the EU-funded project TrustLLM – Democratize Trustworthy and Efficient Large Language Model Technology for Europe, and the Swedish Excellence Centre for Computational Social Science (SweCSS).

Our teaching portfolio comprises courses and degree projects in natural language processing and text mining at the basic, advanced, and doctoral levels.

The unit is lead by Marco Kuhlmann, Professor.


Unit Reasoning and Learning (ReaL)

The Reasoning and Learning (ReaL) AI Lab does fundamental AI research on algorithms, techniques and methods for machine reasoning, machine learning, and the integration of reasoning and learning. Our emphasis is on AI that is trustworthy, robust and transparent. Beyond theoretical contributions, the ReaL AI Lab addresses high-impact technical and societal challenges, producing practical AI advancements for real-world applications.

Research topics

Our research topics include:

  • Combinatorial Assignment
  • Generative AI for time-series
  • Large Language Models
  • Reasoning and Learning
  • Reinforcement Learning
  • Stream Reasoning and Learning
  • Synthetic Data Generation
  • Effective Autonomous Systems

ReaL leads many of the AI activities at Linköping University, including one of the four EU-funded networks of AI research excellence centers (TAILOR), the TrustLLM EU project developing trustworthy and factual large language models, and the Wallenberg AI and Transformative Technologies Education Development Program (WASP-ED).

Funding

The research is funded by Knut and Alice Wallenberg Foundation (KAW), Wallenberg AI, Autonomous Systems and Software Program (WASP), Marcus and Amalia Wallenberg Foundation (MAW), WASP Humanities and Society (WASP-HS), Vinnova, Horizon 2020, ELLIIT, Trafikverket, Graduate School in Computer Science (CUGS, LiU), and Zenith (LiU).

Collaboration

The ReaL AI Lab collaborates with and actively supports Swedish industry, the government and both the public and private sector. ReaL provides broad and deep AI expertise necessary to take full advantage of modern, trustworthy AI. Our focus is on AI solutions for decision support that are not only useful and reliable but also proven effective in real-world applications.

We make AI practical, reliable, and real. We make it ReaL.

The unit is lead by Fredrik Heintz, Professor.

Unit Theoretical Computer Science (TCSLAB) 

Contact us

News at AIICS

News

Shaping AI technology for the benefit of society globally

To explore similarities and challenges faced by Europe and Japan in the digital transformation era and to foster cross-border collaboration and knowledge exchange - researchers and industry professionals from Europe and Japan met at LiU.

Studenthuset

Collaboration between humans and AI - focus for conference

In November 2024, the Japanese-European EJEA conference will come to Sweden and, for the first time, to Linköping University. The conference's theme is future knowledge and competence for AI-driven innovation in Europe and Japan.

image cut from thesis cover

A new doctor in Computer Science: George Osipov

On the 3rd of June, George Osipov successfully defended his thesis in the intersection of theoretical computer science and artificial intelligence (AI), with a focus on parameterized complexity of constraint satisfaction problems (CSPs).

Research at AIICS

Latest publications

2024

Cyrille Berger, Patrick Doherty, Piotr Rudol, Mariusz Wzorek (2024) A Summary of the RGS: an RDF Graph Synchronization System for Collaborative Robotics
Cyrille Berger, Patrick Doherty, Piotr Rudol, Mariusz Wzorek (2024) Leveraging active queries in collaborative robotic mission planning INTELLIGENCE & ROBOTICS, Vol. 4, p. 87-106 (Article in journal) Continue to DOI
Emanuel Sanchez Aimar, Nathaniel Helgesen, Yonghao Xu, Marco Kuhlmann, Michael Felsberg (2024) Flexible Distribution Alignment: Towards Long-Tailed Semi-supervised Learning with Proper Calibration Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LIV, p. 307-327 (Conference paper) Continue to DOI
Md Fahim Sikder, Resmi Ramachandranpillai, Daniel de Leng, Fredrik Heintz (2024) FairX: A comprehensive benchmarking tool for model analysis using fairness, utility, and explainability Proceedings of the 2nd Workshop on Fairness and Bias in AI, co-located with 27th European Conference on Artificial Intelligence (ECAI 2024), Article 16 (Conference paper)
Paul Höft, David Speck, David Speck, Jendrik Seipp, Florian Pommerening (2024) Versatile Cost Partitioning with Exact Sensitivity Analysis Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2024), p. 276-280 (Conference paper)
Kristina Levina, Nikolaos Pappas, Athanasios Karapantelakis, Aneta Vulgarakis Feljan, Jendrik Seipp (2024) Numeric Reward Machines Workshop on Bridging the Gap Between AI Planning and Reinforcement Learning (Conference paper)
Jendrik Seipp (2024) Efficiently Computing Transitions in Cartesian Abstractions Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2024) (Conference paper) Continue to DOI
Augusto B. Corrêa, Jendrik Seipp (2024) Consolidating LAMA with Best-First Width Search ICAPS 2024 Workshop on Heuristics and Search for Domain-independent Planning (HSDIP) (Conference paper)
Clemens Büchner, Patrick Ferber, Jendrik Seipp, Malte Helmert (2024) Abstraction Heuristics for Factored Tasks Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2024), p. 40-49 (Conference paper) Continue to DOI
David Speck, Daniel Gnad (2024) Decoupled Search for the Masses: A Novel Task Transformation for Classical Planning Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling (Conference paper) Continue to DOI

More about AI at AIICS, IDA and LiU

About the department