This paper presents a model tackling a variant of the Raven's Matrices family of human intelligence tests along with computational experiments. Raven's Matrices are thought to challenge human subjects' ability to generalize knowledge and deal with novel situations. We investigate how a generic ability to quickly and accurately generalize knowledge can be succinctly captured by a computational system. This work is distinct from other prominent attempts to deal with the task in terms of adopting a generalized similarity-based approach. Raven's Matrices appear to primarily require similarity-based or analogical reasoning over a set of varied visual stimuli. The similarity-based approach eliminates the need for structure mapping as emphasized in many existing analogical reasoning systems. Instead, it relies on feature-based processing with both relational and non-relational features. Preliminary experimental results suggest that our approach performs comparably to existing symbolic analogy-based models.
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