With recent advancements in systems engineering and artificial intelligence, autonomous agents are increasingly being called upon to execute tasks that have normative relevance. These are tasks that directly—and potentially adversely—affect human well-being and demand of the agent a degree of normative-sensitivity and -compliance. Such norms and normative principles are typically of a social, legal, ethical, empathetic, or cultural (‘SLEEC’) nature. Whereas norms of this type are often framed in the abstract, or as high-level principles, addressing normative concerns in concrete applications of autonomous agents requires the refinement of normative principles into explicitly formulated practical rules. This paper develops a process for deriving specification rules from a set of high-level norms, thereby bridging the gap between normative principles and operational practice. This enables autonomous agents to select and execute the most normatively favourable action in the intended context premised on a range of underlying relevant normative principles. In the translation and reduction of normative principles to SLEEC rules, we present an iterative process that uncovers normative principles, addresses SLEEC concerns, identifies and resolves SLEEC conflicts, and generates both preliminary and complex normatively-relevant rules, thereby guiding the development of autonomous agents and better positioning them as normatively SLEEC-sensitive or SLEEC-compliant.

随着系统工程和人工智能的最新进展，越来越多的自主代理被要求执行具有规范相关性的任务。这些任务直接（并且可能产生不利影响）影响人类福祉，并要求代理人具有一定程度的规范敏感性和合规性。此类规范和规范原则通常具有社会、法律、道德、同理心或文化（“SLEEC”）性质。尽管这种类型的规范通常是抽象的，或者作为高级原则，但解决自治主体具体应用中的规范问题需要将规范原则细化为明确制定的实践规则。本文开发了一种从一组高级规范中导出规范规则的过程，从而弥合了规范原则与操作实践之间的差距。这使得自主主体能够在一系列潜在的相关规范原则的前提下，在预期背景下选择并执行最规范的有利行动。在将规范原则转化为 SLEEC 规则的过程中，我们提出了一个迭代过程，揭示规范原则，解决 SLEEC 问题，识别和解决 SLEEC 冲突，并生成初步和复杂的规范相关规则，从而指导自主代理的开发并更好地将它们定位为 SLEEC 规范敏感或符合 SLEEC。