Spatial problem solving in spatial structures

Abstract

The ability to solve spatial tasks is crucial for everyday life and therefore of great importance for cognitive agents. In artificial intelligence (AI) we model this ability by representing spatial configurations and spatial tasks in the form of knowledge about space and time. Augmented by appropriate algorithms, such representations enable the generation of knowledge-based solutions to spatial problems. In comparison, natural embodied and situated cognitive agents often solve spatial tasks without detailed knowledge about underlying geometric and mechanical laws and relationships. They directly relate actions and their effects through physical affordances inherent in their bodies and their environments. Examples are found in everyday reasoning and also in descriptive geometry. In an ongoing research effort we investigate how spatial and temporal structures in the body and the environment can support or even replace reasoning effort in computational processes. We call the direct use of spatial structure Strong Spatial Cognition. Our contribution describes cognitive principles of an extended paradigm of cognitive processing. The work aims (i) to understand the effectiveness and efficiency of natural problem solving approaches; (ii) to overcome the need for detailed representations required in the knowledge-based approach; and (iii) to build computational cognitive systems that make use of these principles.