用五个专家型AI协作，自动识别石材病害模式。
RED.AI Id-Pattern: First Results of Stone Deterioration Patterns with Multi-Agent Systems
- 构建五类专家AI协同诊断系统，模拟人工判断流程。
- 在28张复杂图像上，性能显著优于基础模型。
- 适合文化遗产保护与智能检测领域研究者参考。
RED.AI项目中的Id-Pattern系统是一个用于辅助识别石材劣化模式的智能体系统。传统方法依赖专家团队实地观察，虽准确但耗时耗力。本文提出并评估了一种多智能体人工智能系统，模拟专家间协作，实现基于视觉证据的石材病理自动诊断。该系统采用认知架构，由五类专业智能体组成：岩矿学家、病理学家、环境专家、修复保护师及诊断协调员。为验证系统效果，选取28张包含多种劣化模式的难题图像进行测试。初步结果表明，系统各项指标均显著优于基础模型。
原文摘要 · Abstract (English)
The Id-Pattern system within the RED.AI project (Reabilitação Estrutural Digital através da AI) consists of an agentic system designed to assist in the identification of stone deterioration patterns. Traditional methodologies, based on direct observation by expert teams, are accurate but costly in terms of time and resources. The system developed here introduces and evaluates a multi-agent artificial intelligence (AI) system, designed to simulate collaboration between experts and automate the diagnosis of stone pathologies from visual evidence. The approach is based on a cognitive architecture that orchestrates a team of specialized AI agents which, in this specific case, are limited to five: a lithologist, a pathologist, an environmental expert, a conservator-restorer, and a diagnostic coordinator. To evaluate the system we selected 28 difficult images involving multiple deterioration patterns. Our first results showed a huge boost on all metrics of our system compared to the foundational model.
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