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Publication - Professor Jonathan Lawry

    Evidence Propagation and Consensus Formation in Noisy Environments

    Extended Abstract


    Crosscombe, M & Lawry, J, 2019, ‘Evidence Propagation and Consensus Formation in Noisy Environments: Extended Abstract’. in: the Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems. International Foundation for Autonomous Agents and MultiAgent Systems, pp. 1904-1906


    We study the effectiveness of consensus formation in multi-agent systems where belief updating is an iterative two-part process, consisting of both belief updating based on direct evidence and also belief combination between agents, within the context of a best-of-n problem. Agents’ beliefs are represented within Dempster-Shafer theory by mass functions and we investigate the macro-level properties of four well-known belief combination operators: Dempster’s rule, Yager’s rule, Dubois & Prade’s operator and the averaging operator. Simulation experiments are conducted for different evidence rates and noise levels. Broadly, Dubois & Prade’s operator results in better convergence to the best state, and is more robust to noisy evidence.

    Full details in the University publications repository