Beyond Following: Mixing Active Initiative into Computational Creativity
Zhiyu Lin, Upol Ehsan, Rohan Agarwal, Samihan Dani, Vidushi Vashishth,, Mark Riedl

TL;DR
This paper explores an active, learning AI agent in mixed-initiative creative systems, demonstrating that proactive AI can enhance user satisfaction and collaboration in story co-creation.
Contribution
It introduces a reinforcement learning-based AI agent that actively adapts its creative role during human-AI co-creation, advancing beyond traditional passive following approaches.
Findings
Learning AI increases user satisfaction in MI-CC.
Proactive AI improves collaborative decision-making.
Participants show deeper understanding of AI's role.
Abstract
Generative Artificial Intelligence (AI) encounters limitations in efficiency and fairness within the realm of Procedural Content Generation (PCG) when human creators solely drive and bear responsibility for the generative process. Alternative setups, such as Mixed-Initiative Co-Creative (MI-CC) systems, exhibited their promise. Still, the potential of an active mixed initiative, where AI takes a role beyond following, is understudied. This work investigates the influence of the adaptive ability of an active and learning AI agent on creators' expectancy of creative responsibilities in an MI-CC setting. We built and studied a system that employs reinforcement learning (RL) methods to learn the creative responsibility preferences of a human user during online interactions. Situated in story co-creation, we develop a Multi-armed-bandit agent that learns from the human creator, updates its…
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Taxonomy
TopicsArtificial Intelligence in Games · Creativity in Education and Neuroscience · Scientific Computing and Data Management
