Computer Science & AI
Artificial intelligence and machine learning
Computers are capable of making incredibly accurate predictions on the basis of machine learning. In other words, these computers can learn without intervention once they have been pre-programmed by humans. At LIACS, we explore and push the borders of what a revolutionary new generation of algorithms can achieve.
[a]social creatures lab
The [a]social creatures lab focusses on understanding social interaction with and between artificial creatures. Research on social interaction with artificial creatures is typically performed with anthropomorphic robots that have human characteristics, such as speech, emotions, gestures and other nonverbal behaviors. The group studies the boundaries of what social interaction with artificial creatures means by varying form, complexity and function including humanoids, abstractly shaped robots, intelligent virtual agents, avatars, non-playing characters, conversational agents and swarm robots. To understand the role of robots in society, they also investigate less favorable impact of human robot relationships on people and the society of the future.
Automated Design and Analysis of Algorithms
The Automated Design and Analysis of Algorithms (ADA) research group pursues the development of Artificial Intelligence techniques that complement, rather than replace, human intelligence. In particular, their research is focussed on methods for the automated design and analysis of algorithms for computationally challenging problems, leveraging human creativity, advanced machine learning and optimisation methods, and lots of compute cycles. They work on a broad range of problems, including propositional satisfiability (SAT), AI planning, mixed integer programming (MIP), the travelling salesperson problem (TSP), supervised and semi-supervised machine learning, as well as a range of real-world applications.
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Multicriteria Optimization and Decision Analysis
The focus of the Multicriteria Optimization and Decision Analysis (MODA) group is to develop foundations of methods in multi-objective optimization. Their interest lies in finding methods that simultaneously consider different performance criteria, which find solutions that are acceptable in practice, or provide insight into the trade-offs. For this, the group uses and designs algorithms that are implemented in modern computation environments. The group thus deals with theoretical foundations of the field such as algorithmic learning theory, optimization and order theory, and aspects related to algorithm engineering to bring results from theory into practice.
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Research in the natural computing group covers theoretical foundations, the development of new algorithms, and interdisciplinary applications of natural computing methods. The driving force behind their research is the mission to increase the understanding of natural systems as models of computation, with a focus on the development of new algorithms and applications to challenging problems. They investigate fundamental aspects of those algorithms as well as their applications to practical problems, including e.g. medicinal chemistry, pharmaceutical, physics, and engineering applications, as well as business applications ranging from portfolio optimization to forecasting.
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The Reinforcement Learning lab conducts research into Reinforcement Learning and Intelligent Combinatorial Algorithms. The group teaches courses in Reinforcement Learning, Robotics, Deep Learning, Game Design, and Advanced Data Mining. It is an open group, with members from bachelor and master students working on their thesis to faculty members. Their interests range from reinforcement learning, games, multi-objective optimization, neural networks, and robotics.
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