Beyond Following: Mixing Active Initiative into Computational Creativity

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 collaborative decision-making belief, and switches between its capabilities during an MI-CC experience. With 39 participants joining a human subject study, Our developed system's learning capabilities are well recognized compared to the non-learning ablation, corresponding to a significant increase in overall satisfaction with the MI-CC experience. These findings indicate a robust association between effective MI-CC collaborative interactions, particularly the implementation of proactive AI initiatives, and deepened understanding among all participants.