The department of Artificial intelligence consists of several research groups:
- Agent Systems headed by prof. dr. Jan Treur
- Computational Intelligence headed by prof. dr. Guszti Eiben
- Knowledge Representation & Reasoning headed by prof. dr. Frank van Harmelen
The Agent Systems group investigates methods and techniques for modelling and analysis of agent systems in the area of human-oriented Ambient Intelligence (or Human Ambience). In this area agent systems are considered from two perspectives. On the one hand, to obtain distributed software/hardware systems, the agent systems perspective provides concepts used in design and implementation of practically applied software/hardware agent systems. On the other hand, agent systems provide a conceptual framework to model, analyse and simulate parts of reality studied in the context of other scientific disciplines such as Biology, Neurology, Psychology, and Social Sciences. Both fundamental and application-directed aspects (and their interaction) of agent systems are investigated.
Computational Intelligence is a fairly new name covering a fairly new field. There is no consensus (yet) on what computational intelligence exactly is, but there is a widely accepted view on which areas belong to it: evolutionary computing, fuzzy computing and neurocomputing. The World Congress on Computational Intelligence held every four years (1994 Orlando, 1998 Anchorage, 2002 Honolulu) consists of three tracks, the IEEE International Conference on Evolutionary Computing, Fuzzy Computing, and Neurocomputing.
Enclosed in the name computational intelligence is a `message', according to scientific folklore it is chosen to indicate the link to and the difference with artificial intelligence. While some techniques within computational intelligence are often counted as artificial intelligence techniques (e.g. genetic algorithms, or neural networks) there is a clear difference between these techniques and traditional, logic based artificial intelligence techniques. In general, typical artificial intelligence techniques are top-to-bottom where, i.e., the structure of models, solutions, etc. is imposed from above. Computational intelligence techniques are generally bottom-up, where order and structure emerges from an unstructured beginning.
The areas covered by the term computational intelligence are also known under the name soft computing. Again, according to scientific folklore, this name was chosen to indicate the difference between soft computing and operations research, also known as hard computing. The two areas are connected by the problem domains they are applied in, but while operations research algorithms usually come with crisp (and strict) conditions on the scope of applicability and proven guarantees for a solution (or even an optimal solution), soft computing puts no conditions on the problem but also provides no guarantees for success, a deficiency which is compensated by the robustness of the methods.
The Knowledge Representation & Reasoning group investigates modelling and representation of different forms of knowledge and reasoning, as found in a large variety of AI systems. We have an interest in both applications and theory. We study theoretical properties of knowledge representation and reasoning formalisms, but are also involved in developing practical knowledge-based systems. Recently, we have been very active in developments around the Semantic Web.