A novel AI framework uses multi-agent systems with decentralised consensus and weighted influence to achieve self-evolution. Agents develop stable collaboration, forming meta-agents with enhanced strategic capabilities. The system integrates global rewards with local credit assignment, using Shapley values to assess individual agent contributions.
This approach allows AI to adapt and improve autonomously, potentially revolutionising industries like healthcare and science. The framework's ability to learn and evolve without explicit programming could lead to more efficient and effective AI solutions. Meta-agents can handle complex tasks, driving innovation and discovery in various fields.
By combining decentralised coordination with individualised performance metrics, the framework fosters a dynamic and adaptive AI ecosystem. This self-evolving AI promises to accelerate progress and address challenges across multiple sectors, marking a significant step forward in AI development.
