DKE research
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Research at DKE

Our research efforts focus on eight themes, with most researchers contributing to multiple different themes at the same time. Browse through DKE’s research themes via the menu on the right, or go directly to one of the dedicated theme pages:

DKE is also a core department of the Mathematics Centre Maastricht and our researchers are part of the Quantum Computing @ UM project.

DKE research
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Affective & Visual Computing Lab (AVCL)

The Affective & Visual Computing Lab builds techniques that allow machines to combine data from different sources and interpret human behavior as accurately as possible. The scope of the lab encompasses both fundamental research and research into a wide range of innovative applications.

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Algorithms, Complexity, and Optimization (ALGOPT)

Research within the scope of ALGOPT focuses on developing and analyzing algorithms with rigorous and verifiable performance guarantees. Performance guarantees typically relate to the quality of the output (optimal solutions or solutions that are at most a certain distance from optimality) and/or to the resources used by the algorithm (time/space complexity). Although much of the work has a theoretical flavour, this theory is used to drive the development of highly efficient algorithms in practise.

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Cognitive Robotics and Complex Self-Organising Systems (SwarmLab)

What do social networks, biological organisms, economic systems, and the human brain have in common? Their intelligence is in their network: these complex self-organizing systems are more than just the sum of their parts. At DKE SwarmLab we believe that understanding and exploiting the mechanisms that lead to self-organization in complex systems will lead to advanced artificial intelligent systems. One of our key research targets is the creation of cognitive robotic systems – human-friendly machines capable of perceiving, understanding, and interacting with their environment.

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Dynamic Game Theory (DGT)

Research within the DGT theme focuses on both theory and applications of dynamic game theory. Most nontrivial real-world problems are dynamic: their properties change over time. Dynamic games typically need different solution methodologies than static games do. The DGT theme unites research into various types of games (differential, stochastic, evolutionary, cooperative and spatial games) and builds and explores links between them. 

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Explainable and Reliable Artificial Intelligence (ERAI)

Research within the ERAI theme Investigates different ways to make intelligent systems better explainable and more reliable. Some of the research foci are:

  • Analyzing whether the input-output relations of a system can provide high-level correlation-based explanations of the black-box AI system
  • Logic-based systems can provide explanations and are able to reason with (legal) regulations that should be adhered. Integrating logic-based approaches with machine learning approaches is one possible way to realize explainable artificial intelligence, and is an important challenge for the near future
  • Learning explainable models instead of learning a mapping may improve explainability with minor or no decrease in the quality of predictions/decisions/recommendations
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Game AI & Search (GAIS)

The focus of the theme lies in the domain of AI that is able to play and design games. Besides performing research in abstract games in order to improve the playing strength (which ultimately results in finding the optimal strategy), research within GAIS focuses on four trends in the field:

  • Developing AI that can outplay (human) opponents in games
  • Video games as a test domain for AI research
  • The design of agents which can play a diverse number of games (General Game Playing)
  • Automatic game design/reconstruction or content generation for games
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Machine Learning (ML)

Many of the most visible advances in the field of Data Science all require some form of machine learning techniques. Machine learning is a central topic of interest to DKE, with applications spanning multiple research themes.

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Signals, Complex Systems and Images (SCSI)

Mathematically describing the situations that generate signals, helps to make the most of their data. Research within SCSI focusses on developing new techniques to analyze signals, images and systems, and develops ways to describe (signal-generating) systems mathematically.

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  • Research at DKE

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  • Affective & Visual Computing Lab (AVCL)

    Dit is er niet

  • Algorithms, Complexity, and Optimization (ALGOPT)

    Dit is er niet

  • Cognitive Robotics and Complex Self-Organising Systems (SwarmLab)

    Dit is er niet

  • Dynamic Game Theory (DGT)

    Dit is er niet

  • Explainable and Reliable Artificial Intelligence (ERAI)

    Dit is er niet

  • Game AI & Search (GAIS)

    Dit is er niet

  • Machine Learning (ML)

    Dit is er niet

  • Signals, Complex Systems and Images (SCSI)

    Dit is er niet