Algebraic Properties of Qualitative Spatio-Temporal Calculi

Qualitative spatial and temporal reasoning is based on so-called qualitative calculi. Algebraic properties of these calculi have several implications on reasoning algorithms. But what exactly is a qualitative calculus? And to which extent do the qualitative calculi proposed meet these demands? The literature provides various answers to the first question but only few facts about the second. In this paper we identify the minimal requirements to binary spatio-temporal calculi and we discuss the relevance of the according axioms for representation and reasoning. We also analyze existing qualitative calculi and provide a classification involving different notions of a relation algebra.Comment: COSIT 2013 paper including supplementary materia

Controlling Multiple Neural Nets with Semantic Networks

Moratz R, Posch S, Sagerer G. Controlling Multiple Neural Nets with Semantic Networks. In: Proceedings 16. DAGM-Symposium. 1994: 288-295

Integration von Bild- und Sprachverstehen in einer kognitiven Architektur

Hildebrandt B, Moratz R, Rickheit G, Sagerer G. Integration von Bild- und Sprachverstehen in einer kognitiven Architektur. Kognitionswissenschaft. 1995;4:118-128

Human-Robot Collaborative Scene Mapping from Relational Descriptions

Abstract. In this article we propose a method for cooperatively building a scene map between a human and a robot by using a spatial relational model employed by the robot to interpret human descriptions of the scene. The description will consist in a set of spatial relations between the objects in the scene. The scene map will contain the position of these objects. For this end we propose a model based on the generation of scalar elds of applicability for each of the available relations. The method can be summarized as follows. In rst place a person will come into the room and describe the scene to the robot, including in the description semantic information about the objects which the robot can't get from its sensors. From the description the robot will form the scene mental map. In second place the robot will sense the scene with a 2D range laser building the scene sensed map. The objects positions in the mental map will be used to guide the sensing process. In a third step the robot will fuse the two maps, linking the semantic information about the described objects to the corresponding sensed ones. The resulting map is called the scene enriched map.