Summary of Distributed Online Life-long Learning (dol3) For Multi-agent Trust and Reputation Assessment in E-commerce, by Hariprasauth Ramamoorthy et al.
Distributed Online Life-Long Learning (DOL3) for Multi-agent Trust and Reputation Assessment in E-commerce
by Hariprasauth Ramamoorthy, Shubhankar Gupta, Suresh Sundaram
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel Distributed Online Life-Long Learning (DOL3) algorithm for trust and reputation assessment in non-stationary environments, where service providers interact with consumers. The goal is to rapidly and continually assess the trustworthiness of provider agents in e-commerce settings. The algorithm involves real-time learning and fusion of trust scores among observer agents in a communication network. Experimental results show that DOL3 outperforms traditional methods, effectively handling volatility in such environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way to measure how trustworthy service providers are in online marketplaces. They wanted to figure out how to quickly and continuously update the trustworthiness of these providers based on their past actions. The team created an algorithm that helps agents learn from each other and combine their opinions about which providers can be trusted. This new approach worked better than previous methods in 90% of cases, making it a useful tool for maintaining fair and reliable online interactions. |
