Evidence Propagation and Consensus Formation in Noisy Environments
This addresses consensus formation in noisy multi-agent systems, presenting an incremental improvement by comparing existing belief combination operators.
The study investigated consensus formation in multi-agent systems using Dempster-Shafer theory, finding that combining direct evidence updating with belief combination between agents leads to better consensus to the best state than evidence updating alone, with Yager's rule performing best in terms of convergence, robustness to noise, and scalability.
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.