Ethical and legal concerns make it necessary for programs that may directly influence the life of people (via, e.g., legal or health counseling) to justify in human-understandable terms the advice given. Answer Set Programming has a rich semantics that makes it possible to very concisely express complex knowledge. However, justifying why an answer is a consequence from an ASP program may be non-trivial -- even more so when the user is an expert in a given domain, but not necessarily knowledgeable in ASP. Most ASP systems generate answers using SAT-solving procedures on ground rules that do not match how humans perceive reasoning. We propose using s(CASP), a query-driven, top-down execution model for predicate ASP with constraints to generate justification trees of (constrained) answer sets. The operational semantics of s(CASP) relies on backward chaining, which is intuitive to follow and lends itself to generating explanations that are easier to translate into natural language. We show how s(CASP) provides minimal justifications for, among others, relevant examples proposed in the literature, both as search trees but, more importantly, as explanations in natural language. We validate our design with real ASP applications and evaluate the cost of generating s(CASP) justification trees.

中文翻译：

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目标导向约束答案集编程的理由

出于道德和法律方面的考虑，可能直接影响人们生活的计划（通过例如法律或健康咨询）有必要以人类可以理解的方式证明所提供建议的合理性。答案集编程具有丰富的语义，可以非常简洁地表达复杂的知识。然而，证明为什么答案是来自 ASP 程序的结果可能并非易事——当用户是给定领域的专家时更是如此，但不一定精通 ASP。大多数 ASP 系统根据与人类感知推理方式不匹配的基本规则使用 SAT 求解程序生成答案。我们建议使用 s(CASP)，一种查询驱动的、自上而下的执行模型，用于带有约束的谓词 ASP，以生成（受约束的）答案集的证明树。s(CASP) 的操作语义依赖于反向链接，这很直观，并且有助于生成更容易翻译成自然语言的解释。我们展示了 s(CASP) 如何为文献中提出的相关示例等提供最小的理由，既作为搜索树，更重要的是作为自然语言的解释。我们用真实的 ASP 应用程序验证了我们的设计，并评估了生成 s(CASP) 理由树的成本。