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Protecting buying agents in e-marketplaces by direct experience trust modelling

机译：通过直接体验信任建模保护电子市场中的采购代理

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摘要

In this paper, we describe a framework for modelling the trustworthiness of sellers in the context of an electronic marketplace where multiple selling agents may offer the same good with different qualities and selling agents may alter the quality of their goods. We consider that there may be dishonest sellers in the market (for example, agents who offer goods with high quality and later offer the same goods with very low quality). In our approach, buying agents use a combination of reinforcement learning and trust modelling to enhance their knowledge about selling agents and hence their opportunities to purchase high value goods in the marketplace. This paper focuses on presenting the theoretical results demonstrating how the modelling of trust can protect buying agents from dishonest selling agents. The results show that our proposed buying agents will not be harmed infinitely by dishonest selling agents and therefore will not incur infinite loss, if they are cautious in setting their penalty factor. We also discuss the value of our particular model for trust, in contrast with related work and conclude with directions for future research. Keywords Adaptive and learning agents - Trust modelling - Multi-agent scenarios - Electronic commerce applications
机译：在本文中，我们描述了一个在电子市场环境中建模卖方信任度的框架，在该市场中，多个销售代理商可能会提供具有不同质量的同一商品，而销售代理商可能会改变其商品质量。我们认为市场上可能存在不诚实的卖家（例如，代理商提供高质量的商品，后来又提供质量很低的相同商品）。在我们的方法中，购买代理商使用强化学习和信任模型的组合来增强他们对销售代理商的了解，从而增强他们在市场上购买高价值商品的机会。本文着重于提供理论结果，以证明信任建模如何保护购买者免受不诚实出售者的侵害。结果表明，如果我们谨慎地设定惩罚因子，那么我们建议的购买代理商将不会受到不诚实卖方的无限损害，因此不会招致无限损失。与相关工作相反，我们还讨论了特定模型对信任的价值，并为未来研究指明了方向。自适应学习代理-信任建模-多代理场景-电子商务应用

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