In our design of OPTIMIST we distill social science domain background knowledge about processes and data dependences to pre-set OPTMIST for our customers. So citizen data scientists with no statistical knowledge and only little training can quickly learn how to use their insights gained about their data with the analytics module to access and activate the automatic prediction module. This hybrid architecture uses both semantic technologies and machine learning and is called Aided Neural Networks liberating Citizen Data Scientist Actions (ANCA).

Originating from Allen's Interval Algebra, composition-based reasoning has been widely acknowledged as the most popular reasoning technique in qualitative spatial and temporal reasoning. Given a qualitative calculus (i.e. a relation model), the first thing we should do is to establish its composition table (CT). In the past three decades, such work is usually done manually. This is undesirable and error-prone, given that the calculus may contain tens or hundreds of basic relations. Computing the correct CT has been identified by Tony Cohn as a challenge for computer scientists in 1995. This paper addresses this problem and introduces a semi-automatic method to compute the CT by randomly generating triples of elements. For several important qualitative calculi, our method can establish the correct CT in a reasonable short time. This is illustrated by applications to the Interval Algebra, the Region Connection Calculus RCC-8, the INDU calculus, and the Oriented Point Relation Algebras. Our method can also be used to generate CTs for customised qualitative calculi defined on restricted domains.

A qualitative representation of space and/or time provides mechanisms which characterize the essential properties of objects or configurations. The advantages over quantitative representations can be: (1) a better match with human concepts related to natural language, and (2) better efficiency for reasoning. The two main trends in qualitative spatial constraint reasoning are topological reasoning about regions and reasoning about directions between points and straight lines and orientations of straight lines or configurations derived from points. In this work, we apply universal algebraic tools to binary qualitative calculi and their relations.

Qualitative spatial descriptions characterize essential properties of spatial objects or configurations by relying on relative comparisons rather than measuring. Typically, in qualitative approaches only relatively coarse distinctions between configurations are made. Qualitative spatial knowledge can be used to represent incomplete and underdetermined knowledge in a systematic way. This is especially useful if the task is to describe features of classes of configurations rather than individual configurations. Although reasoning with them is generally NP-hard, relative directions are important because they play a key role in human spatial descriptions and there are several approaches how to represent them using qualitative methods. In these approaches directions between spatial locations can be expressed as constraints over infinite domains, e.g. the Euclidean plane. The theory of relation algebras has been successfully applied to this field. Viewing relation algebras as universal algebras and applying and modifying standard tools from universal algebra in this work, we (re)define notions of qualitative constraint calculus, of homomorphism between calculi, and of quotient of calculi. Based on this method we derive important properties for spatial calculi from corresponding properties of related calculi. From a conceptual point of view these formal mappings between calculi are a means to translate between different granularities.

We propose an approach that propagates distance and orientation intervals to integrate imprecise local knowledge into survey knowledge. In the context of mobile robot exploration this propagation scheme can be used to reason about the positions of landmarks or other salient positions and thus can be utilized to generate hypotheses for cycles in the environment. We describe a path-based and mainly topological representation and navigation approach based on the generalized Voronoi graph and demonstrate how the propagation method can be applied in this context.

The question addressed in this paper is which types of spatial reference human users employ in their interaction with a robot and how a cognitively adequate model of these strategies can be implemented. Experiments in human-robot interaction were carried out which show how human users approach an artificial communication partner which was designed on the basis of empirical findings regarding spatial references among humans. The results are considerable differences in the strategies which speakers employ to achieve spatial reference in human-robot interaction and in natural communication.