迈向自主工业系统的意图抽象层
Towards an Intention Abstraction Layer for Autonomous Industrial Systems
AI总结：
针对自主工业系统中各子系统运行目标冲突难以及时发现的问题，提出意图抽象层（IAL），通过大型语言模型等将自然语言目标解析为结构化意图，检测并解释冲突，实现执行前意图级检查，保障协作自主系统行为。
AI中文摘要：
现代工业环境中同时运行着许多自主子系统，各子系统追求自身目标并共享物理资源。由于高层人类意图转化为底层控制逻辑后被丢弃，运行组件无法知晓是否按实际意图行事，目标冲突在导致错过目标或停机后才显现。我们提出意图抽象层（IAL），它是一种领域无关的中间件，将意图表示为一流的、持久的和可解释的运行时对象。大型语言模型基于正式的OWL本体将自然语言目标解析为结构化意图，一致性监视器在执行前检测冲突，透明模块用自然语言解释冲突。我们报告了一个概念验证，两个自主代理注册冲突的生产和能源意图，IAL在冲突到达执行层之前标记并解释冲突。结果是一种将协作自主系统的行为保证从事后故障分析转移到执行前意图级检查机制。
英文摘要：
Modern industrial environments increasingly run many autonomous subsystems at once - schedulers, energy managers, vehicle fleets - each pursuing its own goals while sharing the same physical resources. Because high-level human intentions are translated into low-level control logic and then discarded, no running component can tell whether it is still doing what was actually intended, and goal conflicts surface only after they have caused a missed target or a shutdown. We propose the Intention Abstraction Layer (IAL), a domainagnostic middleware that represents intentions as first-class, persistent, and explainable runtime objects: a large language model grounded in a formal OWL ontology parses naturallanguage goals into structured intentions, a consistency monitor detects conflicts at registration time, before execution, and a transparency module explains them in natural language. We report a first proof of concept in which two autonomous agents register conflicting production and energy intentions, and the IAL flags and explains the conflict before it reaches the execution layer. The result is a mechanism that shifts behavioral assurance for cooperating autonomous systems from post-hoc failure analysis to pre-execution, intention-level checking.
