Knowledge Graph-Driven Retrieval-Augmented Multi-Agent Artificial Intelligence Framework for Evidence-Based Natural Resource Decision Support in Human Health

Abstract

The integration of Knowledge Graphs (KGs), Retrieval-Augmented Generation (RAG), and Multi-Agent AI systems presents a transformative opportunity for evidence-based decision support in natural resource management and human health. However, critical gaps persist, including multimodal hallucination, static retrieval pipelines, scalability limitations, lack of clinical validation, and inadequate causal reasoning integration. This research proposes a comprehensive framework combining GraphRAG with dynamic retrieval selection, multi-modal knowledge graphs, decentralized multi-agent collaboration, and causal reasoning to address these challenges. The methodology employs a Llama-3.3-70B agent for dynamic GraphRAG/VectorRAG selection, a multi-modal knowledge graph with 20,545 images for hallucination reduction, role-based multi-agent coordination with shared memory, and CogniRAG for causal hyperedge encoding. Experimental results demonstrate significant improvements: +46% in multi-hop reasoning accuracy, 94.2% faithfulness, 75.8% accuracy on PopQA, and 4.5× reduction in token consumption. The framework establishes a scalable, transparent foundation for translating AI innovation into actionable public health and environmental decision support, addressing the critical gap between computational methods and trustworthy recommendations.