Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
The Michigan Internet AuctionBot: a configurable auction server for human and software agents
AGENTS '98 Proceedings of the second international conference on Autonomous agents
Communications of the ACM
eMediator: a next generation electronic commerce server
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Effect of bargaining in electronic commerce
International Journal of Electronic Commerce - Special issue: Intelligent agents for electronic commerce
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Designing intelligent sales-agent for online selling
ICEC '05 Proceedings of the 7th international conference on Electronic commerce
An Architecture for Flexible Web Service QoS Negotiation
EDOC '05 Proceedings of the Ninth IEEE International EDOC Enterprise Computing Conference
Understanding Agent-Based On-Line Persuasion and Bargaining Strategies: An Empirical Study
International Journal of Electronic Commerce
The design and evaluation of an intelligent sales agent for online persuasion and negotiation
Electronic Commerce Research and Applications
Using temporal-difference learning for multi-agent bargaining
Electronic Commerce Research and Applications
A multi-agent framework for distributed theorem proving
Expert Systems with Applications: An International Journal
Negotiation support for web service selection
TES'04 Proceedings of the 5th international conference on Technologies for E-Services
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The authors propose a multiagent framework, called the OLDyB system, to facilitate an online automated bargaining process for electronic trading. They designed a dynamic price-issuing agent based on the utility theory to offer prices and determine when to close a deal. A pattern-generalization agent logs and then processes the bargaining steps to generalize bargaining patterns. During the bargaining process, the pattern-matching agent tries to match the bargaining steps with the discovered bargaining patterns to return a price to the buyer using the pattern-matching algorithm. When failing to match patterns, the matching agent will invoke the dynamic price-issuing agent to offer prices. The authors conducted a field experiment to evaluate the proposed framework in different sellers' risk perspectives and compared the performance with existing bargaining methods. The results show that the proposed methods obtain encouraging performance. This research initiates the efforts on developing data mining algorithms to support the price bargaining process for electronic commerce.