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Evidence Propagation and Consensus Formation in Noisy Environments
arXiv - CS - Artificial Intelligence Pub Date : 2019-05-13 , DOI: arxiv-1905.04840 Michael Crosscombe, Jonathan Lawry, Palina Bartashevich
arXiv - CS - Artificial Intelligence Pub Date : 2019-05-13 , DOI: arxiv-1905.04840 Michael Crosscombe, Jonathan Lawry, Palina Bartashevich
We study the effectiveness of consensus formation in multi-agent systems
where there is both belief updating based on direct evidence and also belief
combination between agents. In particular, we consider the scenario in which a
population of agents collaborate on the best-of-n problem where the aim is to
reach a consensus about which is the best (alternatively, true) state from
amongst a set of states, each with a different quality value (or level of
evidence). 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 for this multi-agent consensus formation problem:
Dempster's rule, Yager's rule, Dubois & Prade's operator and the averaging
operator. The convergence properties of the operators are considered and
simulation experiments are conducted for different evidence rates and noise
levels. Results show that a combination of updating on direct evidence and
belief combination between agents results in better consensus to the best state
than does evidence updating alone. We also find that in this framework the
operators are robust to noise. Broadly, Yager's rule is shown to be the better
operator under various parameter values, i.e. convergence to the best state,
robustness to noise, and scalability.
更新日期：2020-01-22
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