Trustworthiness of artificially intelligent agents is vital for the acceptance of human-machine teaming in industrial manufacturing environments. Predictable behaviours and explainable (and understandable) rationale allow humans collaborating with (and building) these agents to understand their motivations and therefore validate decisions that are made. To that aim, we make use of G\"ardenfors's cognitively inspired Conceptual Space framework to represent the agent's knowledge using concepts as convex regions in a space spanned by inherently comprehensible quality dimensions. A simple typicality quantification model is built on top of it to determine fuzzy category membership and classify instances interpretably. We apply it on a use case from the manufacturing domain, using objects' physical properties obtained from cobots' onboard sensors and utilisation properties from crowdsourced commonsense knowledge available at public knowledge bases. Such flexible knowledge representation based on property decomposition allows for data-efficient representation learning of typically highly specialist or specific manufacturing artefacts. In such a setting, traditional data-driven (e.g., computer vision-based) classification approaches would struggle due to training data scarcity. This allows for comprehensibility of an AI agent's acquired knowledge by the human collaborator thus contributing to trustworthiness. We situate our approach within an existing explainability framework specifying explanation desiderata. We provide arguments for our system's applicability and appropriateness for different roles of human agents collaborating with the AI system throughout its design, validation, and operation.

翻译：人工智能代理的可信度对于工业制造环境中人机协同的接受度至关重要。可预测的行为与可解释（且可理解）的推理逻辑，使得与这些代理协作（或构建它们）的人类能够理解其动机，从而验证所做出的决策。为此，我们采用Gärdenfors提出的认知启发式概念空间框架，以本质可理解的质量维度所张成的空间中的凸区域来表示概念，并在此基础上构建简单的典型性量化模型以确定模糊类别隶属关系，从而实现可解释的实例分类。我们将该方法应用于制造领域的用例，利用从协作机器人机载传感器获取的物体物理属性，以及从公共知识库中众包常识知识获取的利用属性。这种基于属性分解的灵活知识表示方法，能够高效学习高度专业化或特定化的制造工件表示。在传统数据驱动方法（例如基于计算机视觉的分类方法）因训练数据匮乏而难以实施的场景中，该方法可赋予AI代理所获知识对人类协作者的可理解性，从而提升可信度。我们将该方法置于现有可解释性框架中，明确解释性需求，并论证其在不同角色人类协作者参与AI系统设计、验证和运行全过程中的适用性与适宜性。