Proposes Interaction-Centered Intelligence as a framework where intelligence emerges from interaction dynamics rather than internal agent computation.
Deep Learning for Identifying Potential Conceptual Shifts for Co-creative Drawing
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We present a system for identifying conceptual shifts between visual categories, which will form the basis for a co-creative drawing system to help users draw more creative sketches. The system recognizes human sketches and matches them to structurally similar sketches from categories to which they do not belong. This would allow a co-creative drawing system to produce an ambiguous sketch that blends features from both categories.
verdicts
UNVERDICTED 2representative citing papers
Deep learning vector novelty metric drives conceptual shifts in an AI-human sketching system; user study finds higher novelty correlates with more creative design outcomes.
citing papers explorer
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Interaction-Centered Intelligence: Toward an Interaction-Based Theory of Human-AI Co-Creation
Proposes Interaction-Centered Intelligence as a framework where intelligence emerges from interaction dynamics rather than internal agent computation.
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Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System
Deep learning vector novelty metric drives conceptual shifts in an AI-human sketching system; user study finds higher novelty correlates with more creative design outcomes.