تقرير
Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System
| العنوان: | Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System |
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| المؤلفون: | Karimi, Pegah, Maher, Mary Lou, Davis, Nicholas, Grace, Kazjon |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Human-Computer Interaction, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | This paper presents a computational model for conceptual shifts, based on a novelty metric applied to a vector representation generated through deep learning. This model is integrated into a co-creative design system, which enables a partnership between an AI agent and a human designer interacting through a sketching canvas. The AI agent responds to the human designer's sketch with a new sketch that is a conceptual shift: intentionally varying the visual and conceptual similarity with increasingly more novelty. The paper presents the results of a user study showing that increasing novelty in the AI contribution is associated with higher creative outcomes, whereas low novelty leads to less creative outcomes. Comment: 9 pages, 3 Figures, 1 Table, Accepted in ICCC 2019 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1906.10188 |
| رقم الأكسشن: | edsarx.1906.10188 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |