Proposes Interaction-Centered Intelligence as a framework where intelligence emerges from interaction dynamics rather than internal agent computation.
Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System
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abstract
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.
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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.