An Architecture for General Spatial Reasoning

Competence in interacting with the spatial world, the ability to move around an obstacle, or reach for a desired object, is one of the most immediate needs of any agent existing in such a world. For my thesis work, I am extending a largely-symbolic AI system, the Soar cognitive architecture (Laird, 2008), to better handle spatial problems. A key aspect in the design of Soar is a commitment to generality: the goal of the architecture is to be able to solve the same breadth problems humans are able to solve. In addition, Soar is a psychologically-inspired architecture: a second goal is to solve problems in a manner similar to humans. These goals are reflected in the design of the existing architecture, and must be reflected in the design of any extension to it. Systems for spatial reasoning exist, but they are typically defined for limited domains, and in isolation from a comprehensive intelligent system. My approach to the problem derives from work in diagrammatic reasoning and systems exploring mental imagery. The system augments symbolic working memory in Soar with short-term and long-term memories specialized for spatial information. Reasoning is then a process of manipulating both symbolic and lower-level perceptual data.