are invariant under orientation preserving affine transformations. The dipole calculi allow for a straightforward representation of prototypical reasoning tasks for spatial agents. As an example, we show how to generate survey knowledge from local observations in a street network. The example illustrates the fast constraint-based reasoning capabilities of dipole calculi. We integrate our results into

humans to acquire information. When asking a question, a robot needs to say or otherwise signal enough about its belief state and intentions, for the human to clearly understand what it is after. We survey existing work on the forms and meanings of questions in English, concentrating on the issue of how besides eliciting infor- mation from the hearer, a question can simultaneously offer a window into [...] into the speakers belief state. We propose a formalization based on a notion of common ground, set in a model of situated dialogue as part of collaborative activity. Generating questions starts from agent belief modeling, then forming the intention to request missing information or elicit feedback on uncertain information from a human, and planning and constructing the surface realization, including syntax

which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model