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).


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).


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


Image of diploma.

Best paper award in automated planning

David Speck and Daniel Gnad at the Department of Computer and Information Science have won the Best Paper Award at the Conference ICAPS 2024, one of the most important conferences for automated planning, a topic in artificial intelligence.

Two men at a stage shaking hand and smiling against the public, one of them holding a paper

International recognition for AI researcher at LiU

EurAI, a representative body of the European AI community, has awarded LiU researcher Mattias Tiger for his doctoral thesis. He may be the first Swedish researcher to receive such an award.

Demo of autonomous vehicle in Visionen.

ISY Day 2024 – AI in Society, Education, and Research

This year’s edition of ISY Day offered lectures, discussions, and demonstrations within this year's theme "AI in Society, Education, and Research". A theme that is both current and highly relevant at the Department of Electrical Engineering.

Research at AIICS

robot dog Spot, drone, small helicopter, operator

WASP at Department of Computer and Information Science (IDA)

WASP, Wallenberg AI autonomous systems and software program, is the largest individual research investment in Sweden in modern times. One of the WASP research environments at LiU is located at the Department of Computer and Information Science.

Proud partner of Digital4Business banner


Digital4Business is a four-year EU-funded project for digital upskilling to accelerate economic recovery and shape the digital transformation of Europe's society.

Latest publications


Emil Wiman, Ludvig Widén, Mattias Tiger, Fredrik Heintz (2024) Autonomous 3D Exploration in Large-Scale Environments with Dynamic Obstacles
Dominik Drexler, Jendrik Seipp, Hector Geffner (2024) Expressing and Exploiting Subgoal Structure in Classical Planning Using Sketches Journal of Artificial Intelligence Research, Vol. 80, p. 171-208 Continue to DOI
Daniel de Leng, Pieter Bonte (2024) Last Night in Sweden: A Vision for Resource-Intelligent Stream Reasoning Proceedings of the 18th ACM International Conference on Distributed and Event-based Systems (DEBS ’24)
Blai Bonet, Dominik Drexler, Hector Geffner (2024) On Policy Reuse: An Expressive Language for Representing and Executing General Policies that Call Other Policies Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling (ICAPS 2024)
Katarina Sperling, Carl-Johan Stenberg, Cormac Mcgrath, Anna Akerfeldt, Fredrik Heintz, Linnéa Stenliden (2024) In search of artificial intelligence (AI) literacy in teacher education: A scoping review COMPUTERS AND EDUCATION OPEN, Vol. 6, Article 100169 Continue to DOI
Jorke de Vlas (2024) On the Parameterized Complexity of the Perfect Phylogeny Problem SOFSEM 2024: THEORY AND PRACTICE OF COMPUTER SCIENCE, p. 169-182 Continue to DOI
Pieter Bonte, Jean-Paul Calbimonte, Daniel de Leng, Daniele Dell'Aglio, Emanuele Della Valle, Thomas Eiter, Federico Giannini, Fredrik Heintz, Konstantin Schekotihin, Danh Le-Phuoc, Alessandra Mileo, Patrik Schneider, Riccardo Tommasini, Jacopo Urbani, Giacomo Ziffer (2024) Grounding Stream Reasoning Research Transactions on Graph Data and Knowledge (TGDK), Vol. 2, p. 1-47, Article 2 Continue to DOI
Resmi Ramachandranpillai, Md Fahim Sikder, David Bergström, Fredrik Heintz (2024) Bt-GAN: Generating Fair Synthetic Healthdata via Bias-transforming Generative Adversarial Networks The journal of artificial intelligence research, Vol. 79, p. 1313-1341 Continue to DOI
George Osipov (2024) On Infinite-Domain CSPs Parameterized by Solution Cost
Fredrik Präntare (2024) Dividing the Indivisible: Algorithms, Empirical Advances, and Complexity Results for Value-Maximizing Combinatorial Assignment Problems

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