多智能体协作分析犯罪数据，自动迭代提升预测能力。
AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction
- 三类智能体协同：分析、反馈、预测，通过对话不断优化
- 100轮通信后性能显著提升，评分函数量化改进效果
- 完全离线运行，保护隐私，适合社会科学研究者使用
本文提出LUCID-MA（Learning and Understanding Crime through Dialogue of Multiple Agents），一种基于AI的多智能体框架，通过多个智能体协作分析与理解犯罪数据。系统包含三个核心组件：分析助手用于识别时空犯罪模式；反馈组件对分析结果进行审查与优化；预测组件则用于预估未来犯罪趋势。基于精心设计的提示词和LLaMA-2-13B-Chat-GPTQ模型，系统可在完全离线环境下运行，并通过100轮智能体间通信实现自我迭代优化，极少依赖人工干预。引入评分函数评估智能体表现，并生成可视化图表追踪学习进展。本研究展示了AutoGen式智能体在社会科学领域实现自主、可扩展、迭代式分析的潜力，同时体现了离线执行下的数据隐私保障。系统展现出涌现智能特性——整体行为由智能体间的对话交互驱动，进而提升个体性能。
原文摘要 · Abstract (English)
This paper introduces LUCID-MA (Learning and Understanding Crime through Dialogue of Multiple Agents), an innovative AI powered framework where multiple AI agents collaboratively analyze and understand crime data. Our system that consists of three core components: an analysis assistant that highlights spatiotemporal crime patterns; a feedback component that reviews and refines analytical results; and a prediction component that forecasts future crime trends. With a well-designed prompt and the LLaMA-2-13B-Chat-GPTQ model, it runs completely offline and allows the agents undergo self-improvement through 100 rounds of communication with less human interaction. A scoring function is incorporated to evaluate agent performance, providing visual plots to track learning progress. This work demonstrates the potential of AutoGen-style agents for autonomous, scalable, and iterative analysis in social science domains, maintaining data privacy through offline execution. It also showcases a computational model with emergent intelligence, where the system's global behavior emerges from the interactions of its agents. This emergent behavior manifests as enhanced individual agent performance, driven by collaborative dialogue between the LLM-based agents.
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