Technology Category
Natural Language Processing: Code Generation / Program Synthesis from Natural LanguageHumans and AI: Human-Aware Planning and Behavior PredictionMultiagent Systems: Agent-Based Simulation and Emergent Behavior
Participatory Evolution of Artificial Life Systems via Semantic Feedback
🤖 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
Application Category
📝 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
Research questions and friction points this paper is trying to address.
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
Methods, ideas, or system contributions that make the work stand out.
Semantic feedback guides artificial life evolution
Prompt-to-parameter encoder with CMA-ES optimizer
CLIP-based evaluation modulates visual and behavioral rules
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