  Human-autonomy teaming (HAT) scenarios feature humans and autonomous agents
collaborating to meet a shared goal. For effective collaboration, the agents
must be transparent and able to share important information about their
operation with human teammates. We address the challenge of transparency for
Belief-Desire-Intention agents defined in the Conceptual Agent Notation (CAN)
language. We extend the semantics to model agents that are observable (i.e. the
internal state of tasks is available), and attention-directing (i.e. specific
states can be flagged to users), and provide an executable semantics via an
encoding in Milner's bigraphs. Using an example of unmanned aerial vehicles,
the BigraphER tool, and PRISM, we show and verify how the extensions work in
practice.
