Observable and Attention-Directing BDI Agents for Human-Autonomy Teaming
This addresses transparency for human-autonomy teaming, but it is incremental as it builds on existing agent frameworks.
The paper tackled the challenge of transparency in human-autonomy teaming by extending Belief-Desire-Intention agents to be observable and attention-directing, demonstrating and verifying these extensions using unmanned aerial vehicles and tools like BigraphER and PRISM.
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.