Recent work on decentralized computational trust models for open Multi Agent Systems has resulted in the development of CA, a biologically inspired model which focuses on the trustee's perspective. This new model addresses a serious unresolved problem in existing trust and reputation models, namely the inability to handle constantly changing behaviors and agents' continuous entry and exit from the system. In previous work, we compared CA to FIRE, a well-known trust and reputation model, and found that CA is superior when the trustor population changes, whereas FIRE is more resilient to the trustee population changes. Thus, in this paper, we investigate how the trustors can detect the presence of several dynamic factors in their environment and then decide which trust model to employ in order to maximize utility. We frame this problem as a machine learning problem in a partially observable environment, where the presence of several dynamic factors is not known to the trustor and we describe how an adaptable trustor can rely on a few measurable features so as to assess the current state of the environment and then use Deep Q Learning (DQN), in a single-agent Reinforcement Learning setting, to learn how to adapt to a changing environment. We ran a series of simulation experiments to compare the performance of the adaptable trustor with the performance of trustors using only one model (FIRE or CA) and we show that an adaptable agent is indeed capable of learning when to use each model and, thus, perform consistently in dynamic environments.

翻译：针对开放多智能体系统中的去中心化计算信任模型，近期研究提出了CA这一受生物启发的模型，该模型从受托者视角出发，解决了现有信任与声誉模型中一个长期未解决的关键问题——无法应对不断变化的行为以及智能体持续进出系统的情况。在先前研究中，我们将CA与著名信任声誉模型FIRE进行对比，发现CA在委托者群体变化时表现更优，而FIRE对受托者群体变化具有更强的鲁棒性。因此，本文研究委托者如何感知环境中多种动态因素的存在，进而选择最优信任模型以最大化效用。我们将该问题建模为部分可观测环境下的机器学习问题——委托者无法获知环境中动态因素的具体情况，并阐述自适应委托者如何通过少数可测量特征评估当前环境状态，进而在单智能体强化学习框架下利用深度Q学习（DQN）适应动态环境。通过一系列仿真实验，我们对比了自适应委托者与仅使用单一模型（FIRE或CA）的委托者性能，结果表明自适应智能体能有效习得各模型的使用时机，从而在动态环境中保持稳定表现。