基于诉讼风险分析的智能推理应用探究
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国家科技部重点研发计划资助项目:面向多方证据关联分析的诉讼风险智能分析和结果预测技术研究(2018YFC0830202)


On the Application of Intelligent Reasoning in the Analysis of Litigation Risk
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    摘要:

    人工智能技术的快速发展为诉讼规则知识库构建奠定了坚实基础。当前在诉讼风险分析中,智能推理存在司法风险规则知识库匮乏、诉讼风险分析技术较低等弊端,以致无法从根本上化解证据、诉讼时效和行为规范等方面的风险。是以,需要就案件类型化后随机抽取,并收集案例的起诉状、证据、案情和裁判文书,在对多方证据关联分析模型进行解析的基础上,设计开发诉讼时效性规则知识库、当事人行为规范性规则知识库以及证据有效性规则知识库,结合多方证据关联模型,并与法律法规知识库和诉讼风险规则知识库融合,运用决策树算法,关系网络推理技术列举分析可能存在的诉讼风险,最终达至对诉讼风险点的识别、裁判结果的精准预测和合理分流不必要的诉讼,以期为民众提供全面的诉讼决策指引。

    Abstract:

    The strong development of artificial intelligence technology has laid a solid foundation for the construction of litigation rules knowledge base. Currently, in the process of litigation risk analysis, intelligent reasoning has some disadvantages, such as lack of knowledge base of judicial risk rules, low technology of litigation risk analysis, etc., which cannot fundamentally resolve the risks in evidence, limitation of action and code of conduct. Therefore, it is necessary to select cases randomly after they are typed, and collect the indictment, evidence, facts and judgment documents of cases. On the basis of analyzing the multi-party evidence association analysis model, according to the legal risk points, we design and develop the knowledge base of litigation prescription rules, the knowledge base of Party behavior normative rules and the knowledge base of evidence validity rules, combined with multi-party evidence relationship. The joint model, which is integrated with the knowledge base of laws and regulations and the knowledge base of litigation risk rules, uses decision tree algorithm and relational network reasoning technology to enumerate and analyze the possible litigation risks, and finally achieves the identification of litigation risk points, so as to provide comprehensive litigation decision-making guidance, accurate prediction of judgment results and reasonable diversion of unnecessary litigation for the public.

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朱福勇,龙依雯,王凯.基于诉讼风险分析的智能推理应用探究[J].重庆邮电大学学报(社会科学版),2021,33(1):73-83

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  • 收稿日期:2020-03-20
  • 最后修改日期:2020-07-05
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  • 在线发布日期: 2021-01-25

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