Blair Archibald

Papers

Total Citations

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About

Blair Archibald’s research lies at the intersection of formal verification, multi-agent systems, and human-swarm interaction. His work addresses a critical challenge in robotics: how to pair the fault-tolerant redundancy of robot swarms with complex, mission-level decision-making—without overwhelming human operators. Archibald’s key contribution is the development of runtime formal modelling techniques that maintain operator situational awareness, allowing humans to supervise swarms effectively even in unpredictable environments. His most cited paper, “Successful Swarms: Operator Situational Awareness with Modelling and Verification at Runtime” (2023, 4 citations), introduces predictive formal modelling (PFM) as a means to keep operators informed of swarm state and potential failures. He further validated this approach through a user study (2025, 2 citations), demonstrating PFM’s practical benefits. Archibald also contributed the tool “CAN-verify: A Verification Tool For BDI Agents” (2023, 3 citations), extending formal verification to belief-desire-intention agent architectures. His work is notable for bridging the gap between theoretical verification and real-world human-robot teaming, with direct implications for defence, disaster response, and autonomous systems.