ABSTRACT
Analogy has always been considered a mechanism for interrelating distinct parts of the world, but it is perhaps just as important to consider how analogy might be used to break the world into comprehensible parts. The MAGI program uses the Structure-Mapping Engine (SME) to flexibly and reliably match a description against itself. The resulting mapping pulls out the two maximally consistent parts of the given description. MAGI then divides out the parts of the mapping and categorizes the mapping as symmetrical or regular. These parts may then be used as the basis for new comparisons. We theorize that MAGI models how people use symmetry and regularity to facilitate the encoding task. We demonstrate this with three sets of examples. First, we show how MAGI can augment traditional axis detection and reference frame adjustment in geometric figures. Next, we demonstrate how MAGI detects visual and functional symmetry in logic circuits, where symmetry of form aids encoding symmetry of function. Finally, to emphasize that regularity and symmetry detection is not simply visual, we show how MAGI models some aspects of expectation generation in story understanding. In general, MAGI shows symmetry and regularity to be not only pretty, but also cognitively valuable.
