Research group’s website:

Group’s contact person: Peter van Beek

Group members


The Artificial Intelligence Group conducts research in many areas of artificial intelligence. The group conducts research on models of intelligent interaction, multi-agent systems, natural language understanding, constraint programming, computational vision, decision-theoretic planning and learning, and machine learning.

  • Intelligent user interfaces
    Integrating natural language processing models and user models to produce more effective human-computer interaction. This includes designing interfaces that allow for mixed-initiative interaction. Applications include interface agents, electronic commerce and recommender systems.
  • Multi-agent systems
    Studying how computational limitations influence strategic behaviour in multi-agent systems, as well as developing approaches to overcome computational issues that arise in practical applications of mechanism design and game theory. Designing systems of collaborative problem-solving agents, with an emphasis on issues of communication and co-ordination for applications of multi-agent systems to the design of effective electronic marketplaces and adjustable autonomy systems. Modelling trust, reputation and incentives in multi-agent systems, including the use of social networks.
  • Natural language processing
    The exploration of statistical and linguistic techniques to automate the analysis of natural text, the synthesis of clusters of documents, the retrieval of information from unstructured documents and the development of methods and software tools for computational rhetoric. Application domains include personalized mobile health and web analysis.
  • Constraint programming
    Investigating methodologies for solving difficult combinatorial problems by emphasizing modelling and the application of general purpose search algorithms that use constraint propagation. Current projects include instruction scheduling, constraint propagators for global constraints, and applying machine learning techniques to devise heuristics.
  • Computational vision
    Developing computational theories of perception, based on Bayesian inference, preference rules, and qualitative probabilities, and applying such methods to problems in object recognition, motion estimation, and learning. Other work includes computational perception of scene dynamics, with applications in event recognition, human computer interaction, and robotics, the analysis and categorization of image motion, particularly in densely cluttered scenes, and the recognition of human behaviours in natural environments with application to assistive technology.
  • Decision-theoretic planning and learning
    Design of algorithms to optimize a sequence of actions in an uncertain environment. The emphasis is on probabilistic and decision-theoretic techniques such as fully and partially observable Markov decision processes as well as reinforcement learning. Applications include assistive technology for persons with physical and cognitive disabilities and spoken-dialogue systems.
  • Machine learning
    Machine learning is a fast growing topic of both academic research and commercial applications. It addresses the issue of how computers can learn — that is, how they can processes drawing useful conclusions from massive data sets. Machine learning plays a central role in a wide range of important applications emerging from a need to process data sets whose sizes and complexities are beyond the ability of humans to handle.
  • Affective computing
    Studying how intelligent systems can be improved by reasoning about emotions. Investigating theories of culturally shared affective sentiments during human-machine interaction. Application areas include tutoring, sentiment analysis, assistive technologies and computational social science.
  • Human-in-the-loop intelligent systems
    Across many AI-related research areas, various models combine human and machine intelligence to solve computational problems, including human computation (e.g, crowdsourcing), learning by demonstration, mixed initiative systems, active learning from human teachers, interactive machine learning, etc. In these systems, humans are a critical part of the computational process — they serve as teachers and collaborators to the AI system, providing feedback and corrections, or performing computational tasks that are difficult for existing algorithms. This area of research is at the intersection of AI, Human-Computer Interaction (HCI) and EconCS, involving the design of interfaces, algorithms and incentive mechanisms to harness human processing power to tackle challenging computational problems.