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.

翻译：我们提出一种方法，利用答案集编程（ASP）对意大利刑法典条款进行建模，并基于先前的司法判例半自动化地学习法律规则。所开发的工具旨在通过提供推理过程和可能的法律结果，为刑事审判阶段的法律专家提供支持。该方法涉及对意大利刑法典中“侵犯人身罪”与财产犯罪等条款进行ASP分析与编码。所得模型在一组历史判决案例上得到验证，并根据需要进行优化。在编码过程中可能出现的矛盾由系统妥善处理，该系统还能为新案件生成可能的判决结果，并借助利用稳定模型“可支持性”的工具提供解释。该工具提供的自动可解释性功能亦可用于阐明司法判决背后的逻辑，使决策过程更具可解释性。此外，该工具集成了面向ASP的归纳逻辑编程系统，用于从案例样本中归纳法律规则。