Beyond Logic Programming for Legal Reasoning
Beyond Logic Programming for Legal Reasoning
复制标题
超越逻辑编程的法律推理
DOI：
--
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ken Satoh
中科院分区：
文献类型：
--
作者：
Nguyen Ha Thanh;Francesca Toni;Kostas Stathis;Ken Satoh
长期以来，逻辑程序设计一直被倡导用于法律推理，并提出了几种依赖于逻辑程序设计术语中对法律的明确表示的方法。在这篇立场论文中，我们主要关注基于PROLEG逻辑编程的框架，用于形式化和使用日本预设的终极事实理论进行推理。具体地说，我们研究了利用深度学习技术来改进法律推理的挑战和机会，使用PROLEG确定了四个不同的选项，从使用深度学习增强事实提取到使用文本法律描述进行推理的端到端解决方案。我们评估每个方案的优点和局限性，考虑它们的技术可行性、可解释性以及与法律从业者和决策者的需求的一致性。我们相信，我们的分析可以为旨在为法律领域建立有效决策支持系统的开发人员提供指导，同时促进对法律应用中神经象征方法的挑战和潜在进步的更深入理解。
Logic programming has long being advocated for legal reasoning, and several approaches have been put forward relying upon explicit representation of the law in logic programming terms. In this position paper we focus on the PROLEG logic-programming-based framework for formalizing and reasoning with Japanese presupposed ultimate fact theory. Specifically, we examine challenges and opportunities in leveraging deep learning techniques for improving legal reasoning using PROLEG identifying four distinct options ranging from enhancing fact extraction using deep learning to end-to-end solutions for reasoning with textual legal descriptions. We assess advantages and limitations of each option, considering their technical feasibility, interpretability, and alignment with the needs of legal practitioners and decision-makers. We believe that our analysis can serve as a guideline for developers aiming to build effective decision-support systems for the legal domain, while fostering a deeper understanding of challenges and potential advancements by neuro-symbolic approaches in legal applications.