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.


The high-level semantic interpretation and qualitative analysis of dynamic visuo-spatial imagery requires the representational and inferential mediation of commonsense abstractions of space, time, action, change, interaction and their mutual interplay thereof. In this backdrop, deep visuo-spatial semantics denotes the existence of declaratively grounded models —e.g., pertaining to space, time, space-time, motion, actions & events, spatio-linguistic conceptual knowledge— and systematic formalisation supporting capabilities such as:

  • mixed quantitative-qualitative spatial inference and question answering (e.g., about consistency, qualification and quantification of relational knowledge);
  • non-monotonic spatial reasoning (e.g., for abductive explanation);
  • relational learning of spatio-temporally grounded concepts;
  • integrated inductive-abductive spatio-temporal inference;
  • probabilistic spatio-temporal inference;
  • embodied grounding and simulation from the viewpoint of cognitive linguistics (e.g., for knowledge acquisition and inference based on natural language).

Naturally, the above list is merely indicative of some research challenges that we have identified and are investigating.


Declarative spatial reasoning represents our practical manifestation of the research agenda underlying deep visuo-spatial semantics. At present, our interpretaion of declarative spatial representation and reasoning encompasses:

— the ability to (declaratively) specify and solve real-world problems related to (mixed) geometric and qualitative visuo-spatial representation and reasoning pertaining to temporal, spatial, and spatio-temporal things, be it abstract regions of space and time, geometric entities and physical objects, or spatial artefacts (e.g., shadows of objects, areas of visual attention) without any real physical manifestation.

— semantic question-answering with a rich spatio-temporal ontology where aspects pertaining to space, time, events, actions, change, interaction, conceptual knowledge may be handled as first-class objects within a systematic formal artificial intelligence / knowledge representation and reasoning framework.

PRACTICAL MANIFESTATION OF DECLARATIVE SPATIAL REASONING. With the above in mind, our current line of development of the declarative spatial reasoning method has focussed on rule-based inference with constraint logic programming, structured spatio-temporal relational learning with inductive logic programming, and non-monotonic spatial reasoning based on answer-set programming.