Vidal's libraryTitle: | Improving User Satisfaction in Agent-Based Electronic Marketplaces by Reputation Modelling and Adjustable Product Quality |
Author: | Thomas Tran and Robin Cohen |
Book Tittle: | Proceedings of the Third International Joint Conference on Autonomous Agents and MultiAgent Systems |
Pages: | 828--835 |
Publisher: | ACM |
Year: | 2004 |
Abstract: | In this paper, we propose a market model and learning algorithms for buying and selling agents in electronic marketplaces. We take into account the fact that multiple selling agents may offer the same good with different qualities, and that selling agents may alter the quality of their goods. We also consider the possible existence of dishonest selling agents in the market. In our approach, buying agents learn to maximize their expected value of goods using reinforcement learning. In addition, they model and exploit the reputation of selling agents to avoid interaction with the disreputable ones, and therefore to reduce the risk of purchasing low value goods. Our selling agents learn to maximize their expected profits by using reinforcement learning to adjust product prices, and also by altering product quality to provide more customized value to their goods. This paper focuses on presenting results from experiments investigating the behaviours of buying and selling agents in large-sized electronic marketplaces. Our results confirm that buying and selling agents following the proposed algorithms obtain greater satisfaction than buying and selling agents who only use reinforcement learning, with the buying agents not modelling sellers reputation and the selling agents not adjusting product quality. |
Cited by 4 - Google Scholar
@InProceedings{tran04a,
author = {Thomas Tran and Robin Cohen},
title = {Improving User Satisfaction in Agent-Based
Electronic Marketplaces by Reputation Modelling and
Adjustable Product Quality},
booktitle = {Proceedings of the Third International Joint
Conference on Autonomous Agents and MultiAgent
Systems},
pages = {828--835},
year = 2004,
publisher = {{ACM}},
abstract = {In this paper, we propose a market model and
learning algorithms for buying and selling agents in
electronic marketplaces. We take into account the
fact that multiple selling agents may offer the same
good with different qualities, and that selling
agents may alter the quality of their goods. We also
consider the possible existence of dishonest selling
agents in the market. In our approach, buying agents
learn to maximize their expected value of goods
using reinforcement learning. In addition, they
model and exploit the reputation of selling agents
to avoid interaction with the disreputable ones, and
therefore to reduce the risk of purchasing low value
goods. Our selling agents learn to maximize their
expected profits by using reinforcement learning to
adjust product prices, and also by altering product
quality to provide more customized value to their
goods. This paper focuses on presenting results from
experiments investigating the behaviours of buying
and selling agents in large-sized electronic
marketplaces. Our results confirm that buying and
selling agents following the proposed algorithms
obtain greater satisfaction than buying and selling
agents who only use reinforcement learning, with the
buying agents not modelling sellers reputation and
the selling agents not adjusting product quality.},
keywords = {modeling trust learning ecommerce},
url = {http://jmvidal.cse.sc.edu/library/tran04a.pdf},
comment = {masrg},
googleid = {v1JdKI00JqgJ:scholar.google.com/},
cluster = {12116429628359135935}
}
Last modified: Wed Mar 9 10:16:14 EST 2011