基于ACP和LLM犯罪学研究进展：综述与展望

刘佳茜; 王兴霞; 范丽丽; 黄峻; 张宁; 孟庆振

doi:10.11959/j.issn.2096-0271.202512

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基于ACP和LLM犯罪学研究进展：综述与展望

综述与展望 | 更新时间：2025-06-10

- 基于ACP和LLM犯罪学研究进展：综述与展望
- The ACP and LLM-based framework for criminology: a review and prospect
- 智能科学与技术学报 2025年7卷第1期页码：21-29
- 作者机构：
  
  1.英国曼彻斯特大学社会科学学院，曼彻斯特 M13 9PL
  2.中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
  3.中国科学院自动化研究所多模态人工智能系统全国重点实验室，北京 100190
  4.北京理工大学自动化学院，北京 100081
  5.澳门科技大学创新工程学院，澳门 999078
  6.公安部鉴定中心，北京 100038
- 作者简介：
  
  [ "刘佳茜（1998- ），女，英国曼彻斯特大学硕士生，主要研究方向为社会学社交网络分析及复杂系统在犯罪学研究中的应用、犯罪行为的网络结构、动态模式及其社会影响。" ]
  [ "王兴霞（1996- ），女，中国科学院自动化研究所多模态人工智能系统全国重点实验室博士生，主要研究方向为平行智能、工业大模型、故障诊断等。" ]
  [ "范丽丽（1991- ），女，博士，北京理工大学自动化学院助理教授，主要研究方向为机器人/自动驾驶复杂动态场景主动感知与理解。" ]
  [ "黄峻（1998- ），男，澳门科技大学创新工程学院博士生，主要研究方向为平行智能、自动驾驶轨迹预测规划、提示工程、大语言模型等。" ]
  张宁（1988- ），男，博士，公安部鉴定中心正高级职称，主要研究方向为法庭科学。zhangning@cifs.gov.cn
  孟庆振（1985- ），男，公安部鉴定中心副高级职称，主要研究方向为法庭科学。
- 基金信息：
  
  中央级公益性科研院所基本科研业务费专项资金项目(2024YJGG01)
- DOI：10.11959/j.issn.2096-0271.202512
  中图分类号： D917
- 收稿日期：2025-02-11，
  
  修回日期：2025-03-02，
  
  纸质出版日期：2025-03-15
- 稿件说明：
移动端阅览
刘佳茜,王兴霞,范丽丽等.基于ACP和LLM犯罪学研究进展：综述与展望[J].智能科学与技术学报,2025,07(01):21-29.

LIU Jiaxi,WANG Xingxia,FAN Lili,et al.The ACP and LLM-based framework for criminology: a review and prospect[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):21-29.
刘佳茜,王兴霞,范丽丽等.基于ACP和LLM犯罪学研究进展：综述与展望[J].智能科学与技术学报,2025,07(01):21-29. DOI： 10.11959/j.issn.2096-0271.202512.

LIU Jiaxi,WANG Xingxia,FAN Lili,et al.The ACP and LLM-based framework for criminology: a review and prospect[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):21-29. DOI： 10.11959/j.issn.2096-0271.202512.

摘要

为提供犯罪学研究的新视角和更有效的工具，围绕如何基于ACP（人工系统、计算实验、平行执行）方法和大语言模型（large language model，LLM）进行犯罪学研究展开。首先，深入剖析了传统犯罪学研究在系统刻画、动态推演及实证研究等方面面临的瓶颈，并详细阐述了ACP方法在解决上述挑战中的潜在优势。然后，提出了基于ACP和LLM的犯罪学研究框架，探讨了其理论意义与实践价值。最后，对未来基于LLM的数字助手如何赋能犯罪学研究进行了展望，同时指出该模式在实际应用中可能面临的挑战，并强调应多加强多学科协作与技术创新，以推动犯罪学理论与实践发展，提升犯罪防控整体效能。

Abstract

New perspectives and powerful tools for criminology research were provided by exploring how the ACP (artificial systems

computational experiments

and parallel execution) approach and large language model (LLM) could be integrated into criminological studies. Firstly

an in-depth analysis of the challenges faced by traditional criminology in system characterization

dynamic simulation

and empirical research was conducted. The potential advantages of the ACP approach in addressing these challenges were then elaborated. Subsequently

a criminology research framework based on ACP and LLM was proposed

and its theoretical significance and practical value were discussed. Finally

it was envisioned how LLM-based digital assistants could enhance criminological research in the future. Given the challenges in practical applications

interdisciplinary collaboration and technological innovation are necessary to further advance criminological studies and improve crime prevention effectiveness.

关键词

Keywords

references

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