Participatory Evolution of Artificial Life Systems via Semantic Feedback

Abstract

We present a semantic-feedback framework that treats natural language as a regulatory signal for evolving artificial-life systems. Instead of using prompts to select finished images, text in our system shapes the dynamics of an interactive ecosystem, allowing audiences to cultivate behaviors over time. The framework couples a learned mapping from prompts to simulation parameters with evolutionary search and vision–language evaluation, so user intent modulates both visible outcomes and the underlying generative rules. It supports iterative prompt refinement, multi-agent interaction, and the synthesis of new collective rules from community input. In a user study, participants achieved higher semantic alignment and reported a greater sense of control than with manual tuning, while behaviors remained diverse across generations. As an art-led contribution, the work reframes authoring as participatory cultivation and advances open-ended evolution as a socially distributed, not solely algorithmic, process; as a tool contribution, it offers a practical platform for co-creative generative design.