Although moral responsibility is not circumscribed by causality, they are both closely intermixed. Furthermore, rationally understanding the evolution of the physical world is inherently linked with the idea of causality. Thus, the decision-making applications based on automated planning inevitably have to deal with causality, especially if they consider imputability aspects or integrate references to ethical norms. The many debates around causation in the last decades have shown how complex this notion is and thus, how difficult is its integration with planning. As a result, much of the work in computational ethics relegates causality to the background, despite the considerations stated above. This paper's contribution is to provide a complete and sound translation into logic programming from an actual causation definition suitable for action languages, this definition is a formalisation of Wright's NESS test. The obtained logic program allows to deal with complex causal relations. In addition to enabling agents to reason about causality, this contribution specifically enables the computational ethics domain to handle situations that were previously out of reach. In a context where ethical considerations in decision-making are increasingly important, advances in computational ethics can greatly benefit the entire AI community.

翻译：尽管道德责任并不完全由因果关系界定，但两者紧密交织。此外，理性理解物理世界的演化本质上与因果概念密不可分。因此，基于自动规划的决策应用不可避免地需要处理因果关系，尤其是当其考虑归责问题或融入伦理规范时。过去几十年围绕因果关系的大量争议揭示了这一概念的复杂性，进而凸显了其与规划整合的难度。结果，尽管存在前述考量，计算伦理学领域的许多工作仍将因果关系置于次要地位。本文的贡献在于，将一种适用于动作语言的实际因果定义——即Wright的NESS测试的形式化表述——完整且可靠地转化为逻辑编程方案。所获得的逻辑程序能够处理复杂的因果关联。这一成果不仅使智能体能够推理因果关系，更具体地推动了计算伦理学领域处理此前无法触及的情境。在决策中伦理考量日益重要的背景下，计算伦理学的进展将使整个AI社区受益匪浅。