The Dynamic and Distributed Information Systems Group at the University of Zurich investigates the foundations and technical means for building systems that dynamically adapt to changes in the environment or in their goals and/or require the collaboration of many human or computational actors to achieve their goals. The implicit underlying hypothesis of these investigations are that dynamic and distributed systems need some means to reason about their tasks and to learn from their actions. 
In order to reason systems need to be able to (i) collect some kind of information and (ii) draw conclusions from them. Whilst we make no assumptions about the nature of the information collected we assume that they usually come in either a typed graph or in some kind of probabilistic/statistcial format. We operationalize the former usually in the form of Semantic Web knowledge bases and the latter as probabilistic statements. These formats allow for the integration of widely distributed information sources such as the web. To learn from their action systems must contain some kind of machine learning component. 

As a consequence most of our research projects either have a Semantic Web or a machine learning component or both. 

Currently active projects

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