Reputation Management in Multi-Agent Systems Using Permissioned Blockchain Technology

D. Calvaresi, V. Mattioli, Alevtina Dubovitskaya, A. Dragoni, M. Schumacher
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引用次数: 21

Abstract

The multi-agent framework is a well-known approach to realize distributed intelligent systems. Multi-agent systems (MAS) are increasingly employed in safety-and information-critical domains (e.g., eHealth, cyber-physical systems, financial services, and energy market). Therefore, these systems need to be equipped with mechanisms to ensure transparency and the trustworthiness of the behaviors of their components. Trust can be achieved by employing reputation-based mechanisms. Nevertheless, the existing methods are still unable to fully guarantee the desired accountability and transparency. Aligned with the recent trends, advocating the distribution of trust to avoid the risks of having a single point of failure of the system, this work extends existing efforts on combining blockchain technologies (BCT) and MAS. To attain a trusted environment, we provide the architecture and implementation of a system that allows the agents to interact with each other and enables tracking how their reputation changes after every interaction. Agents reputations are computed transparently using smart contracts. Immutable distributed ledger stores reputation values, as well as services and their evaluations to ensure trustworthy interactions between the agents. We also developed a graphical interface to test different scenarios of interactions between the agents. Finally, we summarize and discuss the experience gained and explain the strategic choices when binding MAS and BCT.
使用许可区块链技术的多代理系统中的声誉管理
多智能体框架是实现分布式智能系统的一种众所周知的方法。多主体系统(MAS)越来越多地应用于安全和信息关键领域(如电子健康、网络物理系统、金融服务和能源市场)。因此,这些系统需要配备机制,以确保其组件的行为的透明度和可信度。信任可以通过采用基于声誉的机制来实现。然而,现有的方法仍然不能充分保证所期望的责任制和透明度。与最近的趋势一致,倡导信任分布以避免系统单点故障的风险,这项工作扩展了将区块链技术(BCT)和MAS结合起来的现有努力。为了获得一个可信的环境,我们提供了一个系统的架构和实现,该系统允许代理相互交互,并能够跟踪每次交互后它们的声誉如何变化。代理人的声誉使用智能合约透明地计算。不可变分布式账本存储信誉值,以及服务及其评估,以确保代理之间的可信交互。我们还开发了一个图形界面来测试代理之间交互的不同场景。最后,我们总结和讨论了所获得的经验,并解释了结合MAS和BCT的策略选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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