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| Design patterns have proven useful in many creative ?elds,
providing content creators with archetypal, reusable guidelines to leverage in projects. Creating such patterns, however,
is a time-consuming, manual process, typically relegated to a
few experts in any given domain. In this paper, we describe an
algorithmic method for learning design patterns directly from
data using techniques from natural language processing and
structured concept learning. Given a set of labeled, hierarchical designs as input, we induce a probabilistic formal grammar over these exemplars. Once learned, this grammar encodes a set of generative rules for the class of designs, which
can be sampled to synthesize novel artifacts. We demonstrate
the method on geometric models and Web pages, and discuss
how the learned patterns can drive new interaction mechanisms for content creators. |
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