Learning design patterns with Bayesian grammar induction

Jerry O. Talton, Lingfeng Yang, Ranjitha Kumar, Maxine Lim, Noah D. Goodman, Radomír Měch

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationUIST'12 - Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology
Pages63-73
Number of pages11
StatePublished - Nov 19 2012
Externally publishedYes
Event25th Annual ACM Symposium on User Interface Software and Technology, UIST 2012 - Cambridge, MA, United States
Duration: Oct 7 2012Oct 10 2012

Publication series

NameUIST'12 - Proceedings of the 25th Annual ACM Symposium on User Interface Software and Technology

Other

Other25th Annual ACM Symposium on User Interface Software and Technology, UIST 2012
CountryUnited States
CityCambridge, MA
Period10/7/1210/10/12

Keywords

  • Design patterns
  • Grammar induction

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

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