Proceedings of the Annual Meeting of the Cognitive Science Society

To date, the major focus of research in knowledge representations for artificial intelligence has been on sentential or linguistic formalisms involving logic and rulebased reasoning. There is a growing body of evidence suggesting, however, that much of human problem solving is achieved, not through the application of rules of inference, but rather through the manipulation of mental models. Such a model is represented by a system with a similar relational structure to the reality it represents. Moreover, spatial reasoning with models involves the inspection and transformation of representations in ways that are analogous to visually inspecting and physically transforming entities in the world. Since a crucial component of knowledge acquisition is to capture an expert's mental state and reasoning strategies, it is important to shift some of the attention of AI research to the study of representation techniques that correspond to the mental models used by humans. The paper begins with a cognitive perspective on model-based reasoning. A knowledge representation scheme for spatial reasoning with models is then presented. In this scheme, which has evolved from research in computational imagery, spatial models are represented as symbolic arrays where dimensions of the array correspond to transitive order relations among entities.