Learning design patterns with bayesian grammar induction

Authors:
Jerry Talton;Lingfeng Yang;Ranjitha Kumar;Maxine Lim;Noah Goodman;Radomír Měch
Affiliations:
Intel Corporation, Hillsboro, Oregon, USA;Stanford University, Stanford, California, USA;Stanford University, Stanford, California, USA;Stanford University, Stanford, California, USA;Stanford University, Stanford, California, USA;Adobe Systems, San Jose, California, USA
Venue:
Proceedings of the 25th annual ACM symposium on User interface software and technology
Year:
2012

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Abstract

Design patterns have proven useful in many creative fields, 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.