Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A scalable comparison-shopping agent for the World-Wide Web
AGENTS '97 Proceedings of the first international conference on Autonomous agents
The Michigan Internet AuctionBot: a configurable auction server for human and software agents
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence
Machine Learning
MAgNET: Mobile Agents for Networked Electronic Trading
IEEE Transactions on Knowledge and Data Engineering
A Social Mechanism of Reputation Management in Electronic Communities
CIA '00 Proceedings of the 4th International Workshop on Cooperative Information Agents IV, The Future of Information Agents in Cyberspace
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
Agent Architectures to Support Collaborative Processes
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 1 - Volume 1
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
RedAgent-2003: An Autonomous Market-Based Supply-Chain Management Agent
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
E-Marketplace Using Artificial Immune System as Matchmaker
CEC '04 Proceedings of the IEEE International Conference on E-Commerce Technology
Agent Based Simulation of Information Diffusion in a Virtual Market Place
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
TRUMMAR - A Trust Model for Mobile Agent Systems Based on Reputation
ICPS '04 Proceedings of the The IEEE/ACS International Conference on Pervasive Services
Reputation-oriented reinforcement learning strategies for economically-motivated agents in electronic market environments
A Distributed Reputation Management Scheme for Mobile Agent-Based E-Commerce Applications
EEE '05 Proceedings of the 2005 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE'05) on e-Technology, e-Commerce and e-Service
Addressing common vulnerabilities of reputation systems for electronic commerce
Journal of Theoretical and Applied Electronic Commerce Research
A dynamic reputation system with built-in attack resilience to safeguard buyers in e-market
ACM SIGSOFT Software Engineering Notes
A model for context aware mobile payment
Journal of Theoretical and Applied Electronic Commerce Research
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In this paper, we propose a market model which is based on reputation and reinforcement learning algorithms for buying and selling agents. Three important factors: quality, price and delivery-time are considered in the model. We take into account the fact that buying agents can have different priorities on quality, price and delivery-time of their goods and selling agents adjust their bids according to buying agents preferences. Also we have assumed that multiple selling agents may offer the same goods with different qualities, prices and delivery-times. In our model, selling agents learn to maximize their expected profits by using reinforcement learning to adjust product quality, price and delivery-time. Also each selling agent models the reputation of buying agents based on their profits for that seller and uses this reputation to consider discount for reputable buying agents. Buying agents learn to model the reputation of selling agents based on different features of goods: reputation on quality, reputation on price and reputation on delivery-time to avoid interaction with disreputable selling agents. The model has been implemented with Aglet and tested in a large-sized marketplace. The results show that selling/buying agents that model the reputation of buying/selling agents obtain more satisfaction rather than selling/buying agents who only use the reinforcement learning.