Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling discretionality to act and other vague concepts such as ambiguity cannot be expressed in top-down execution models based on Prolog, and in bottom-up execution models based on ASP the justifications are incomplete and/or not scalable. We propose to use s(CASP), a top-down execution model for predicate ASP, to model vague concepts following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) a representative use case, the criteria for the admission of students in the "Comunidad de Madrid".

翻译：自动法律推理及其在智能合约和自动化决策中的应用日益受到关注。在此背景下，伦理和法律问题要求自动推理者能够以人类可理解的术语解释其给出的建议。逻辑编程，特别是答案集编程，具有丰富的语义，已被用于非常简洁地表达复杂知识。然而，基于Prolog的自上而下执行模型无法表达行动自由裁量权及其他如歧义等模糊概念，而基于ASP的自下而上执行模型中，解释不完整或不可扩展。我们提出使用s(CASP)——一种用于谓词ASP的自上而下执行模型——按照一组模式来建模模糊概念。我们实现了一个名为s(LAW)的框架，用于建模、推理并解释适用法律，并通过翻译（及基准测试）一个代表性用例——马德里自治区学生录取标准——来验证该框架。