Jonathan Lawry
Papers
12
Total Citations
118
H-Index
7
About
Jonathan Lawry is a leading researcher in swarm robotics and collective decision-making, with a particular focus on how robot swarms can make robust, distributed choices in uncertain environments. His major contributions center on the best-of-n problem, where he has pioneered the use of three-valued logic and intermediate belief states (such as "uncertain" or "indifferent") to improve decision accuracy and resilience. Lawry's work on negative updating—a mechanism that allows robots to downgrade confidence in poor options—has been especially influential, with his foundational 2017 paper on robust distributed decision-making accumulating 27 citations and his subsequent studies on noisy environments and collective preference learning each garnering over 10 citations. Beyond swarm algorithms, he has also contributed to formal specification and verification of autonomous systems, addressing how robots can operate under partial compliance with safety requirements. His research consistently bridges theoretical rigor with practical deployment challenges, as seen in his work on multi-robot patrolling and anomaly perception. With over 100 total citations across his most-cited papers, Lawry's work is essential reading for anyone interested in decentralized intelligence, swarm robotics, and the mathematical foundations of collective behavior.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3Negative updating applied to the best-of-n problem with noisy qualities15 citations · 2021
- 4Towards Autonomous Robotic Systems (TAROS)15 citations · 2007
- 5Collective preference learning in the best-of-n problem10 citations · 2021
- 6
- 7Epistemic Sets Applied to Best-of-n Problems8 citations · 2019
- 8
- 9
- 10