用逻辑编程建模意大利刑法，自动推理解释判案并学习规则
XAI-LAW: A Logic Programming Tool for Modeling, Explaining, and Learning Legal Decisions
- 用答案集编程编码刑法条文，支持罪名与财产犯罪建模
- 基于历史判决自动提炼法律规则，准确生成新案件可能判决
- 提供可解释的推理过程，适合法律专家辅助审判决策
我们提出一种方法，使用答案集编程（ASP）建模意大利刑事法典（ICC）条文，并基于先前司法判决半自动生成法律规则。该工具旨在支持刑事审判阶段的法律专家，提供推理和可能的法律后果。方法包括分析并用ASP编码刑法中的“针对人身犯罪”和财产犯罪条文。构建的模型在一系列既往判决上进行验证，并根据需要进行优化。编码过程中可能出现矛盾，系统能妥善处理，并通过稳定模型的“支撑性”机制生成新案件的可能判决，同时提供解释。工具具备自动可解释性，有助于阐明司法决定背后的逻辑，提升决策透明度。此外，系统集成了用于ASP的归纳逻辑编程模块，用于从案例中泛化法律规则。
原文摘要 · Abstract (English)
We propose an approach to model articles of the Italian Criminal Code (ICC), using Answer Set Programming (ASP), and to semi-automatically learn legal rules from examples based on prior judicial decisions. The developed tool is intended to support legal experts during the criminal trial phase by providing reasoning and possible legal outcomes. The methodology involves analyzing and encoding articles of the ICC in ASP, including "crimes against the person" and property offenses. The resulting model is validated on a set of previous verdicts and refined as necessary. During the encoding process, contradictions may arise; these are properly handled by the system, which also generates possible decisions for new cases and provides explanations through a tool that leverages the "supportedness" of stable models. The automatic explainability offered by the tool can also be used to clarify the logic behind judicial decisions, making the decision-making process more interpretable. Furthermore, the tool integrates an inductive logic programming system for ASP, which is employed to generalize legal rules from case examples.
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