Creative Wand: A System to Study Effects of Communications in Co-creative Settings
Keywords:Procedural Content Generation, Mixed-initiative, Co-creativity, Communication, Collaboration
AbstractRecent neural generation systems have demonstrated the potential for procedurally generating game content, images, stories, and more. However, most neural generation algorithms are “uncontrolled” in the sense that the user has little say in creative decisions beyond the initial prompt specification. Co-creative, mixed-initiative systems require user-centric means of influencing the algorithm, especially when users are unlikely to have machine learning expertise. The key to co-creative systems is the ability to communicate ideas and intent from the user to the agent, as well as from the agent to the user. Key questions in co-creative AI include: How can users express their creative intentions? How can creative AI systems communicate their beliefs, explain their moves, or instruct users to act on their behalf? When should creative AI systems take initiative? The answer to such questions and more will enable us to develop better co-creative systems that make humans more capable of expressing their creative intents. We introduce CREATIVE-WAND, a customizable framework for investigating co-creative mixed-initiative generation. CREATIVE-WAND enables plug-and-play injection of generative models and human-agent communication channels into a chat-based interface. It provides a number of dimensions along which an AI generator and humans can communicate during the co-creative process. We illustrate the CREATIVE-WAND framework by using it to study one dimension of co-creative communication—global versus local creative intent specification by the user—in the context of storytelling.
How to Cite
Lin, Z., Agarwal, R., & Riedl, M. (2022). Creative Wand: A System to Study Effects of Communications in Co-creative Settings. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 18(1), 45-52. https://doi.org/10.1609/aiide.v18i1.21946
Research Track Papers