有限可观察性下基于声誉的自适应惩罚优化

Samhar Mahmoud, Daniel Villatoro, Jeroen Keppens, Michael Luck
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引用次数: 9

摘要

事实证明,在没有中央权威的分散系统的自治中,使用社会规范是有效的。Axelrod在自利个体群体中建立规范的开创性模型提供了一些通过使用元规范来支持这一点所需的机制,但不能直接适用于现实世界的场景,例如在线点对点社区。特别是,它不能反映相互作用的不同拓扑安排。虽然最近的一些努力试图解决这些限制,但它们也受到限制,因为它们没有考虑真实系统中出现的代理之间的点对点交互,而只是考虑整个邻居可见的交互。本文的目标是双重的:首先,将这些现实适应纳入原始模型;其次,为智能体提供基于声誉的机制,使它们能够动态优化惩罚强度,确保在这些有限的观察条件下建立规范。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimised Reputation-Based Adaptive Punishment for Limited Observability
The use of social norms has proven to be effective in the self-governance of decentralised systems in which there is no central authority. Axelrod's seminal model of norm establishment in populations of self-interested individuals provides some insight into the mechanisms needed to support this through the use of metanorms, but is not directly applicable to real world scenarios such as online peer-to-peer communities, for example. In particular, it does not reflect different topological arrangements of interactions. While some recent efforts have sought to address these limitations, they are also limited in not considering the point-to-point interactions between agents that arise in real systems, but only interactions that are visible to an entire neighbourhood. The objective of this paper is twofold: firstly to incorporate these realistic adaptations to the original model, and secondly, to provide agents with reputation based mechanisms that allow them to dynamically optimise the intensity of punishment ensuring norm establishment in exactly these limited observation conditions.
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