Participatory Evolution of Artificial Life Systems via Semantic Feedback
Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, Yitong Sun
TL;DR
This paper introduces a semantic feedback framework that uses natural language to guide the evolution of artificial life systems, enabling user-driven customization and emergent behaviors in an interactive ecosystem.
Contribution
It presents a novel integration of prompt-to-parameter encoding, CMA-ES optimization, and CLIP evaluation for participatory evolution in artificial life systems.
Findings
Improved semantic alignment over manual tuning
Supports prompt refinement and multi-agent interaction
Demonstrates potential for participatory generative design
Abstract
We present a semantic feedback framework that enables natural language to guide the evolution of artificial life systems. Integrating a prompt-to-parameter encoder, a CMA-ES optimizer, and CLIP-based evaluation, the system allows user intent to modulate both visual outcomes and underlying behavioral rules. Implemented in an interactive ecosystem simulation, the framework supports prompt refinement, multi-agent interaction, and emergent rule synthesis. User studies show improved semantic alignment over manual tuning and demonstrate the system's potential as a platform for participatory generative design and open-ended evolution.
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