With our research we connect basic research in Artificial Intelligence – in particular related to knowledge representation and reasoning – to technical systems in order to realize intelligent systems which interact with humans. To this end we often employ hybrid approaches to AI and we are particularly interested to investigate how different AI components can be integrated. 

Research highlights:

Qualitative Spatial and Temporal Reasoning (QSTR)

Aside regular contributions to field we develop the spatial reasoning toolbox SparQ or see our recent survey on existing formalisms. Using QSTR techniques in our BamBird agent we won the AI Birds competition in 2016.

Spatial Language and Situated Interaction

We are keen to connect spatial knowledge representation and reasoning to language in order to achieve a intuitive interactions with intelligent systems. Currently, we pursue the automatic interpretation of place descriptions in context of a DFG project within the priority program on volunteered geographic information (VGI).