Commonsense Visuo-Spatial Reasoning: Theory and Applications


Mehul Bhatt, Faculty of Mathematics and Informatics, University of Bremen, DE

Carl Schultz, Institute for Geoinformatics, University of Münster, DE


Spatial thinking, conceptualisation, and the verbal and visual (e.g., gestural, iconic, diagrammatic) communication of commonsense as well as expert knowledge about the world –the space that we exist in— is one of the most important aspects of everyday human life. Philosophers, cognitive scientists, psychologists, linguists, psycholinguists, ontologists, information theorists, computer scientists, mathematicians, architects, and environmental psychologists have each investigated space through the perspective of the lenses afforded by their respective field of study. This tutorial will present computational visuo-spatial representation and reasoning from the viewpoint of the research areas of artificial intelligence, commonsense reasoning, and spatial cognition and computation. The key focus will be on declarative spatial reasoning: the ability to (declaratively) specify and solve real-world problems related to geometric (i.e., quantitative) and qualitative visuo-spatial representation and reasoning. The practical problems that we address and demonstrate in this context encompass both specialist and everyday commonsense reasoning instances identifiable in a range of cognitive technologies and spatial assistance systems where spatio-linguistic conceptualisation & background knowledge focussed visuo-spatial cognition and computation are central.

The tutorial will demonstrate CLP(QS), a declarative spatial reasoning system capable of modelling and reasoning about qualitative spatial relations pertaining to multiple spatial domains, i.e., one or more aspects of space such as topology, and intrinsic and extrinsic orientation, size, distance etc. With CLP(QS), users and application developers may freely mix object domains (i.e., points, line-segments, and regions) with the available spatial domains. We will especially highlight CLP(QS) capability to mix quantitative-qualitative spatial reasoning, and in its current form, basic quantification support offering the means to go back from qualitative relations to the domain of precise quantitative (geometric) information. The emphasis in CLP(QS) is on the seamless integration of declarative visuo-spatial (computational) problem-solving capabilities within large-scale hybrid AI systems, and cognitive (interaction) technologies.