Bimodal Spatial Reasoning with Continuous Motion

Abstract

Symbolic AI systems typically have difficulty reasoning about motion in continuous environments, such as determining whether a cornering car will clear a close obstacle. Bimodal systems, integrating a qualitative symbolic system with a quantitative diagram-like spatial representation, are capable of solving this sort of problem, but questions remain of how and where knowledge about fine-grained motion processes is represented, and how it is applied to the problem. In this paper, we argue that forward simulation of motion is an appropriate method, and introduce continuous motion models to enable this simulation. These motion-specific models control behavior of objects at the spatial level, while general mechanisms in the higher qualitative level control and monitor them. This interaction of low- and high-level activity allows for problem solving that is both precise in individual problems and general across multiple problems. In addition, this approach allows perception and action mechanisms to be reused in reasoning about hypothetical motion problems and abstract non-motion problems, and points to how symbolic AI can become more grounded in the real world. We demonstrate implemented systems that solve problems in diverse domains, and connections to action control are discussed.