  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.
