噪声环境中的证据传播与共识形成
Evidence Propagation and Consensus Formation in Noisy Environments
摘要：
我们研究了多智能体系统中的共识形成的有效性，其中既有基于直接证据的信念更新，也有智能体之间的信念组合。特别是，我们考虑的情况下，人口的代理合作的最好的n的问题，其目的是达成共识，这是最好的（或者，真正的）状态之间的一组状态，每个具有不同的质量值（或证据水平）。代理人的信念表示在Dempster-Shafer理论的质量函数，我们调查的宏观层面的属性四个著名的信念组合运营商的多智能体共识形成问题：Dempster的规则，Yager的规则，Dubois和普拉德的运营商和平均运营商。考虑了算子的收敛性，并针对不同的证据率和噪声水平进行了仿真实验。结果表明，直接证据的更新和信念的组合代理之间的结果更好的共识，以最佳状态比单独的证据更新。我们还发现，在这个框架中的运营商是强大的噪音。广泛地说，Yager的规则被证明是更好的运营商在各种参数值，即收敛到最佳状态，鲁棒性噪声，和可扩展性。
英文摘要：
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.
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