The interest in explainability in artificial intelligence (AI) is growing vastly due to the near ubiquitous state of AI in our lives and the increasing complexity of AI systems. Answer-set Programming (ASP) is used in many areas, among them are industrial optimisation, knowledge management or life sciences, and thus of great interest in the context of explainability. To ensure the successful application of ASP as a problem-solving paradigm in the future, it is thus crucial to investigate explanations for ASP solutions. Such an explanation generally tries to give an answer to the question of why something is, respectively is not, part of the decision produced or solution to the formulated problem. Although several explanation approaches for ASP exist, almost all of them lack support for certain language features that are used in practice. Most notably, this encompasses the various ASP extensions that have been developed in the recent years to enable reasoning over theories, external computations, or neural networks. This project aims to fill some of these gaps and contribute to the state of the art in explainable ASP. We tackle this by extending the language support of existing approaches but also by the development of novel explanation formalisms, like contrastive explanations.

翻译：人工智能（AI）中可解释性的兴趣正急剧增长，这源于AI在我们生活中的近乎普遍存在以及AI系统日益增长的复杂性。答案集编程（ASP）被广泛应用于众多领域，包括工业优化、知识管理或生命科学，因此在可解释性背景下具有重大价值。为确保ASP作为问题解决范式在未来成功应用，研究ASP解决方案的解释至关重要。此类解释通常试图回答"为何某事物是或不是所产生决策或问题解决方案的一部分"这一问题。尽管已有多种ASP解释方法，但几乎所有方法都缺乏对实践中使用的特定语言特性的支持。最显著地，这包括近年来发展起来的、用于支持理论推理、外部计算或神经网络的各类ASP扩展。本项目旨在填补其中一些空白，并为可解释ASP的最新进展做出贡献。我们通过扩展现有方法的语言支持以及开发新颖的解释形式（如对比解释）来应对这一挑战。