Observable and Attention-Directing BDI Agents for Human-Autonomy Teaming

Blair Archibald
Muffy Calder
Michele Sevegnani
Mengwei Xu

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.

In Marie Farrell and Matt Luckcuck: Proceedings Third Workshop on Formal Methods for Autonomous Systems (FMAS 2021), Virtual, 21st-22nd of October 2021, Electronic Proceedings in Theoretical Computer Science 348, pp. 167–175.
Published: 21st October 2021.

ArXived at: http://dx.doi.org/10.4204/EPTCS.348.12 bibtex PDF
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