Abstract
We report a novel approach to visual analogical reasoning, one afforded expressly by fractal representations. We first describe the nature of visual analogies and fractal representations. Next, we exhibit the Fractal Ravens algorithm through a detailed example, describe its performance on all major variants of the Raven's Progressive Matrices tests, and discuss the implications and next steps. In addition, we illustrate the importance of considering the confidence of the answers, and show how ambiguity may be used as a guide for the automatic adjustment of the problem representation. To our knowledge, this is the first published account of a computational model's attempt at the entire Raven's test suite.
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