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Publication - Professor Jonathan Lawry

    Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics

    Citation

    Lee, C, Lawry, J & Winfield, A, 2018, ‘Negative Updating Combined with Opinion Pooling in the Best-of-n Problem in Swarm Robotics’. in: Swarm Intelligence: 11th International Conference, ANTS 2018 Proceedings (Lecture Notes in Computer Science), October 29-31, 2018. Rome, Italy. Springer Nature, pp. 97-108

    Abstract

    There is a need for effective collective decision making in decentralised multi-agent and robotic systems. This paper introduces a novel approach to the best-of-n decision problem with large n. It utilises negative feedback obtained from direct pairwise comparison of options and evidence preserving opinion pooling. We present agent-based simulation experiments that explore the effects of pool size and the number of options on the speed of consensus. Robotic simulation experiments are then used to investigate the potential of the approach as a method for solving the best-of-n decision problem in swarm robotic applications. Overall, the results suggest that the proposed approach is highly scalable with regards to n.

    Full details in the University publications repository