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Evidence Propagation and Consensus Formation in Noisy Environments: Extended Abstract

Research output: Contribution to conferenceAbstract

Original languageEnglish
Pages1904
Number of pages1906
DatePublished - 13 May 2019

Abstract

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.

    Research areas

  • Consensus formation, evidence propagation, noisy decision-making, emergent behaviour, distributed problem solving

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  • ext_abstract_aamas_2019

    Accepted author manuscript, 726 KB, PDF-document

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    Licence: CC BY

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