Trust and Reputation Assessment of service providers in citizen-focused environments like e-commerce is vital to maintain the integrity of the interactions among agents. The goals and objectives of both the service provider and service consumer agents are relevant to the goals of the respective citizens (end users). The provider agents often pursue selfish goals that can make the service quality highly volatile, contributing towards the non-stationary nature of the environment. The number of active service providers tends to change over time resulting in an open environment. This necessitates a rapid and continual assessment of the Trust and Reputation. A large number of service providers in the environment require a distributed multi-agent Trust and Reputation assessment. This paper addresses the problem of multi-agent Trust and Reputation Assessment in a non-stationary environment involving transactions between providers and consumers. In this setting, the observer agents carry out the assessment and communicate their assessed trust scores with each other over a network. We propose a novel Distributed Online Life-Long Learning (DOL3) algorithm that involves real-time rapid learning of trust and reputation scores of providers. Each observer carries out an adaptive learning and weighted fusion process combining their own assessment along with that of their neighbour in the communication network. Simulation studies reveal that the state-of-the-art methods, which usually involve training a model to assess an agent's trust and reputation, do not work well in such an environment. The simulation results show that the proposed DOL3 algorithm outperforms these methods and effectively handles the volatility in such environments. From the statistical evaluation, it is evident that DOL3 performs better compared to other models in 90% of the cases.

翻译：在电子商务等以公民为中心的环境中，对服务提供者进行信任与信誉评估对于维护智能体间交互的完整性至关重要。服务提供者智能体与服务消费者智能体的目标均与各自所代表的公民（最终用户）的目标相关。提供者智能体常追求自私目标，可能导致服务质量高度波动，从而加剧环境的非平稳性。活跃服务提供者的数量往往随时间变化，形成开放环境。这要求对信任与信誉进行快速且持续的评估。环境中大量服务提供者的存在需要分布式的多智能体信任与信誉评估机制。本文研究了在涉及提供者与消费者交易的非平稳环境中进行多智能体信任与信誉评估的问题。在此设定下，观察者智能体执行评估，并通过网络相互通信其评估得出的信任分数。我们提出了一种新颖的分布式在线终身学习（DOL3）算法，该算法能够实时快速学习提供者的信任与信誉分数。每个观察者执行自适应学习与加权融合过程，将其自身评估与通信网络中相邻节点的评估相结合。仿真研究表明，现有先进方法（通常涉及训练模型以评估智能体的信任与信誉）在此类环境中表现不佳。仿真结果显示，所提出的DOL3算法优于这些方法，并能有效应对此类环境中的波动性。统计评估表明，在90%的情况下，DOL3的性能明显优于其他模型。