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Learning design patterns with Bayesian grammar induction (2012)
| Content Provider | CiteSeerX |
|---|---|
| Author | Talton, Jerry O. Lim, Maxine Yang, Lingfeng Goodman, Noah D. Kumar, Ranjitha Měch, Radomír |
| Description | 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. ACM Classification Keywords H.1.2. [Models and Principles]: User/Machine Systems – In UIST |
| File Format | |
| Language | English |
| Publisher Date | 2012-01-01 |
| Access Restriction | Open |
| Subject Keyword | User Machine System Reusable Guideline Web Page Learned Pattern Concept Learning Manual Process Natural Language Processing Bayesian Grammar Induction Acm Classification Keywords Many Creative Field Design Pattern Novel Artifact Geometric Model Algorithmic Method Hierarchical Design New Interaction Mechanism Generative Rule Probabilistic Formal Grammar Content Creator |
| Content Type | Text |
| Resource Type | Article |
