RED.AI Id-Pattern: First Results of Stone Deterioration Patterns with Multi-Agent Systems
This addresses the costly and time-consuming process of expert-based stone deterioration analysis, though it appears incremental as it builds on existing AI methods for a specific domain.
The paper tackled the problem of identifying stone deterioration patterns by developing a multi-agent AI system that automates diagnosis from visual evidence, achieving a significant boost in all metrics compared to the foundational model on 28 difficult images.
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.