Artificial Intelligence and Integrated Computer Systems (AIICS)

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.

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.

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.

The unit is lead by Peter Jonsson, Professor.