Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time. Real-world distributed networks such as online market places, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour. In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change.

中文翻译：

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通过动态行为改善信任和声誉评估

多代理系统 (MAS) 中代理之间的信任对于鼓励高水平的合作至关重要。评估信任和声誉的现有方法使用代理的直接和间接过去经验来估计其未来表现；然而，如果代理人随着时间的推移改变他们的行为，这些并不总是具有代表性。现实世界的分布式网络，如在线市场、P2P 网络、普适计算和智能电网可以被视为 MAS。这种 MAS 中的动态代理行为可能来自季节性变化、作弊者、供应链故障、网络流量和许多其他原因。然而，现有的信任和声誉模型使用有限的技术，例如遗忘因素和滑动窗口，来解释动态行为。在本文中，我们提出了信任和声誉中的反应和预测 (RaPTaR)，一种扩展现有信任和声誉模型的方法，使代理能够随着时间的推移监控与一组代理的交互输出，以识别任何可能的行为变化并做出相应的调整。此外，RaPTaR 可以提供先验当交互数据很少或没有交互数据时估计信任（因为代理是新的或因为检测到的行为变化表明最近的过去经验不再具有代表性）。我们的结果表明，当动态行为导致与之交互的最佳代理的排名发生变化时，与现有的信任和声誉方法相比，RaPTaR 的性能有所提高。