AI 中文总结
本文提出ARF五域本体，将权威关系定义为跨域原语，提供机器可解释的权威表示，支持AI智能体在执行行动前确定权威的来源、范围与有效性，属于AI治理交叉领域的知识表示与推理研究。
AI 中文摘要
随着AI系统的自主行动能力不断增强，仅判断智能体在技术上是否有能力执行某一行动是不够的：系统还必须确定该行动在特定情境下是否被授权。本文提出权威解析框架（Authority Resolution Framework, ARF），这是一个五域本体，用于表示和解析组织角色与非正式影响力、业务概念、成文流程、机器可读权限及可执行系统、外部现实世界情境中的权威关系。ARF将权威关系（Authority Relation, AR）定义为跨域原语，绑定了行动者、行动、对象、有限情境、证明链以及名为DNA系数的校准度量，该度量用于捕获成文权威结构与实际行使的权威之间的差异。该框架提供了权威来源与范围的机器可解释表示，包含用于权威解析的JSON-LD表示和知识图谱查询模式。ARF旨在支持AI智能体在执行重要行动前确定权威的来源、范围和情境有效性。该框架将权威解析定位为本体工程、语义AI、智能体AI与AI治理交叉领域的知识表示与推理问题。
英文摘要
As AI systems become increasingly capable of autonomous action, determining whether an agent is technically capable of performing an action is insufficient: the system must also determine whether the action is authorised in its context. This paper introduces the Authority Resolution Framework (ARF), a five-domain ontology for representing and resolving authority across organisational roles and informal influence, business concepts, codified processes, machine-readable permissions and executable systems, and external real-world context. ARF defines the Authority Relation (AR) as a cross-domain primitive binding an actor, action, object, bounded context, justification chain, and a calibration measure termed the DNA-Coefficient, which captures divergence between documented authority structures and authority as practiced. The framework provides a machine-interpretable representation of authority provenance and scope, with JSON-LD representations and knowledge-graph query patterns for authority resolution. ARF is designed to support AI agents in determining the provenance, scope and contextual validity of authority before executing consequential actions. The framework positions authority resolution as a knowledge-representation and reasoning problem at the intersection of ontology engineering, semantic AI, agentic AI and AI governance.
Comments27 Pages, 2 Tables, 1 Script, 1 Query
