标题： 用于迭代犯罪数据分析和预测的自动生成驱动的多代理框架 显示英文标题

标题： AutoGen Driven Multi Agent Framework for Iterative Crime Data Analysis and Prediction

摘要： 本文介绍了LUCID-MA（通过多智能体对话学习和理解犯罪），这是一种创新的由人工智能驱动的框架，其中多个人工智能代理协同分析和理解犯罪数据。 我们的系统包含三个核心组件：一个分析助手，用于突出时空犯罪模式；一个反馈组件，用于审查和优化分析结果；以及一个预测组件，用于预测未来的犯罪趋势。 通过精心设计的提示和LLaMA-2-13B-Chat-GPTQ模型，该系统完全离线运行，并允许代理通过与人类交互较少的情况下进行100轮通信以实现自我提升。 引入了一个评分函数来评估代理的表现，提供可视化图表以跟踪学习进度。 这项工作展示了AutoGen风格代理在社会科学领域中进行自主、可扩展且迭代分析的潜力，同时通过离线执行维护数据隐私。 显示英文摘要

摘要： 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's 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.