构建可机器处理的人类行为解释本体，助力人机协作安全高效
OntoPret: An Ontology for the Interpretation of Human Behavior
- 基于认知科学与模块化方法构建本体框架
- 支持任务偏差与欺骗行为的分类识别
- 适用于制造与游戏场景，适合人机交互研究者
随着人机协同在工业5.0等范式中日益重要，机器需安全有效地理解复杂人类行为。当前技术主导的机器人框架常缺乏对人类行为的细腻建模，而描述性行为本体又不适用于实时协作解读。本文提出OntoPret，一种用于人类行为解释的本体。该本体基于认知科学与模块化工程方法，提供形式化、机器可处理的框架，用于分类行为，包括任务偏离和欺骗行为。我们在制造与游戏两个不同应用场景中验证其适应性，并建立高级意图推理所需的语义基础。
原文摘要 · Abstract (English)
As human machine teaming becomes central to paradigms like Industry 5.0, a critical need arises for machines to safely and effectively interpret complex human behaviors. A research gap currently exists between techno centric robotic frameworks, which often lack nuanced models of human behavior, and descriptive behavioral ontologies, which are not designed for real time, collaborative interpretation. This paper addresses this gap by presenting OntoPret, an ontology for the interpretation of human behavior. Grounded in cognitive science and a modular engineering methodology, OntoPret provides a formal, machine processable framework for classifying behaviors, including task deviations and deceptive actions. We demonstrate its adaptability across two distinct use cases manufacturing and gameplay and establish the semantic foundations necessary for advanced reasoning about human intentions.
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