华东师范大学学报(自然科学版) ›› 2019, Vol. 2019 ›› Issue (5): 85-99.doi: 10.3969/j.issn.1000-5641.2019.05.007

• 新兴应用中的计算机智能 • 上一篇    下一篇

基于法计算学理论的人工智能辅助决策算法研究

陈亮1,2, 郭佳雯3, 武建功2, 王占全4, 史令4   

  1. 1. 同济大学 上海国际知识产权学院, 上海 200092;
    2. 华东理工大学 法学院, 上海 200237;
    3. 上海交通大学 中英国际低碳学院, 上海 201306;
    4. 华东理工大学 信息科学与工程学院, 上海 200237
  • 收稿日期:2019-07-28 出版日期:2019-09-25 发布日期:2019-10-11
  • 通讯作者: 王占全,男,博士,副教授,硕士生导师,研究方向为数据库、空间数据挖掘、教育大数据.E-mail:zhqwang@ecust.edu.cn. E-mail:zhqwang@ecust.edu.cn
  • 作者简介:陈亮,男,硕士研究生,主要研究方向为知识产权法、科技法学.E-mail:chenliang_1997@163.com.
  • 基金资助:
    2018年华为技术有限公司——教育部产学合作协同育人项目（201802001048）

Research on artificial intelligence assisted decision-making algorithms for lawyers based on legal-computing theory

CHEN Liang1,2, GUO Jia-wen3, WU Jian-gong2, WANG Zhan-quan4, SHI Ling4   

  1. 1. Shanghai International College of Intellectual Property, Tongji University, Shanghai 200092, China;
    2. School of Law, East China University of Science and Technology, Shanghai 200237, China;
    3. China-UK Low Carbon College, Shanghai Jiaotong University, Shanghai 201306, China;
    4. School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  • Received:2019-07-28 Online:2019-09-25 Published:2019-10-11

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摘要： 针对法学理论和法律实践中缺乏智能决策的问题，综合考虑该领域内的业务数据特征，采用多种数据分析模型进行智能决策算法的研究.法计算学理论以法律关系的数据化智能驱动为核心，在作为法律研究与应用本体的法律关系与计算机科学领域内的数据特征属性之间建立联系，提出了“涵摄分类”概念，并对决策树、朴素贝叶斯等算法进行法律场景下的改进，建立了法律关系坐标系，实现法律关系分析的空间几何转化，最后提出了智能化的辅助决策平台.实验结果表明，该辅助决策与真实律师的办案策略与结果高度吻合，具有辅助律师决策的可行性和有效性.

关键词: 法律人工智能, 法计算学, 朴素贝叶斯, C4.5决策树

Abstract: At present, there is a lack of intelligent decision-making tools applied to legal theory and practice. Given the characteristics of data in this field, we establish an intelligent decision-making algorithm using a variety of data analysis models. Legal-computing is focused on data-based mechanization of legal reasoning. It establishes a relationship between legal research and applications using the characteristics and data features of computer science. On this basis, the method of "implication classification" is formed, the decision tree and Naive Bayes algorithms are improved for application to the legal arena, and a coordinate system of legal relationships is established to transfer traditional legal relationship analysis into a spatial geometric system. Experimental results show that the algorithm is consistent with a lawyer's handling strategy and results, and has the feasibility of assisting lawyers more broadly in decision-making.

Key words: legal artificial intelligence, legal-computing, Naive Bayes, C4.5 decision tree

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