Distributed Online Life-Long Learning (DOL3) for Multi-agent Trust and Reputation Assessment in E-commerce
Hariprasauth Ramamoorthy, Shubhankar Gupta, Suresh Sundaram

TL;DR
This paper introduces DOL3, a distributed online lifelong learning algorithm designed for real-time trust and reputation assessment among agents in dynamic, non-stationary e-commerce environments, outperforming existing methods.
Contribution
The paper presents a novel distributed online lifelong learning algorithm for trust and reputation assessment that adapts rapidly to environment volatility and agent behavior changes.
Findings
DOL3 outperforms state-of-the-art methods in 90% of simulation cases.
The proposed method effectively handles environment volatility and non-stationarity.
Simulation results demonstrate improved trust assessment accuracy over existing approaches.
Abstract
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…
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Taxonomy
Methodstravel james
