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 division has around 60 employees and consists of five units (research laboratories):
For a presentation of each unit, please see below.
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.
AILAB includes three topic-focused research groups:
Research topics of current interest include the following:
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.
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.
Our main topics of interest include:
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:
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.
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