We propose a new image-based method of reasoning, called ISR for Indeterminacy in Spatial Reasoning, that dynamically constructs and inspects multiple images to reason about spatial prepositional phrases. The consideration of more than one consistent image, as a model of the description, increases the accuracy of reasoning. However, since it is intractable to consider every possible consistent image, ISR utilizes several heuristics to generate only the most relevant images, admitting some inaccuracy. Thus ISR exploits the computational tradeoff between efficiency and accuracy, and we show empirically in a representative domain that this tradeoff is effective.
ISR demonstrates that, by modifying the procedures, imagery can be more accurate for reasoning about spatially indeterminate descriptions. However, as we observe in attempting to extend ISR to a scaled-up domain, the procedural encoding of knowledge hinders the maintenance of an effective computational tradeoff. We conclude that imagery is distinct from axiomatic/deductive approaches only in having heuristic knowledge about space for making approximations.