Participatory Evolution of Artificial Life Systems via Semantic Feedback

2025-07-04

AI Summary

This work addresses the challenge of simultaneously aligning visual outputs and underlying behavioral rules with user intent in artificial life systems. Methodologically, it introduces a natural language–guided evolutionary framework featuring a semantic feedback loop: a prompt-to-parameter encoder maps natural language prompts to evolvable parameters; these are optimized via the CMA-ES algorithm and evaluated using CLIP-based semantic scoring within an interactive multi-agent simulation environment. Its key contribution is the first use of natural language as an evolutionary control medium, enabling prompt-driven automatic synthesis of behavioral rules and collaborative generative design. User studies demonstrate that the system significantly improves semantic consistency of generated outputs compared to manual parameter tuning (p < 0.01), validating its effectiveness and practical potential for human–AI co-evolutionary design.

Technology Category

• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Guiding artificial life evolution via natural language feedback
• Modulating visual outcomes and behavioral rules with user intent
• Enhancing semantic alignment in generative design and evolution

Innovation

• Semantic feedback guides artificial life evolution
• Prompt-to-parameter encoder with CMA-ES optimizer
• CLIP-based evaluation modulates visual and behavioral rules