RED.AI Id-Pattern: First Results of Stone Deterioration Patterns with Multi-Agent Systems

TL;DR

This paper presents the initial results of a multi-agent AI system designed to identify stone deterioration patterns, demonstrating significant improvements over previous models in analyzing complex visual evidence.

Contribution

Introduces a multi-agent AI system with specialized agents for diagnosing stone deterioration, enhancing accuracy and efficiency over traditional expert-based methods.

Findings

Significant performance improvements over the foundational model.

Effective collaboration among specialized AI agents.

Successful analysis of complex deterioration images.

Abstract

The Id-Pattern system within the RED.AI project (Reabilita\c{c}\~ao Estrutural Digital atrav\'es 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.