Multiagent System (MAS) is a self-learning intelligent system formed by many single agents. Each agent in the MAS works independently of the other, and they have all the characteristics of an agent system. It can respond to changes and countermeasures based on its own external environmental conditions. When solving a complex problem, multiple agents can form a group to solve the problem together. In this paper, the agent’s evolutionary algorithm is integrated into the actual problem—multi-issue autonegotiation of law. According to this problem, the autonegotiation solution process and corresponding model are designed. In addition, a new type of solution is proposed for multiple legal issues. Compared with traditional solutions, the applicability has great advantages. Among them, the autonegotiation result of all the agent’s total utility can be quickly found. In the changing environment, this article focuses on the multiagent system negotiation problem. According to the distributed information sharing of multiple agents, even if the case reveals incomplete information, the multiagent can be generated while ignoring the incomplete information. Optimal solution is proposed. The experimental results show that the success rate of the system in analyzing multiple legal issues and autonegotiations reached 67.56% under the condition of incomplete information from the outside world.

中文翻译：

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基于人工智能和多智能体进化算法的法律多问题自动协商方法研究

多智能体系统（MAS）是由多个单智能体组成的自学习智能系统。MAS 中的每个代理彼此独立工作，并且它们具有代理系统的所有特征。它可以根据自身的外部环境条件来应对变化和对策。在解决复杂问题时，多个智能体可以组成一个小组，共同解决问题。本文将智能体的进化算法融入到实际问题——多问题的法律自动协商中。针对这个问题，设计了自协商求解过程和相应的模型。此外，针对多个法律问题提出了一种新型的解决方案。与传统解决方案相比，适用性具有很大优势。他们之中，可以快速找到所有代理总效用的自动协商结果。在不断变化的环境中，本文重点讨论多智能体系统协商问题。根据多个智能体的分布式信息共享，即使案件揭示了不完整的信息，也可以在忽略不完整信息的情况下生成多智能体。提出了最佳解决方案。实验结果表明，该系统在外界信息不完整的情况下，分析多个法律问题和自动协商的成功率达到了67.56%。可以在忽略不完整信息的情况下生成多代理。提出了最佳解决方案。实验结果表明，该系统在外界信息不完整的情况下，分析多个法律问题和自动协商的成功率达到了67.56%。可以在忽略不完整信息的情况下生成多代理。提出了最佳解决方案。实验结果表明，该系统在外界信息不完整的情况下，分析多个法律问题和自动协商的成功率达到了67.56%。