KQML as an agent communication language
Software agents
Agents for process coherence in virtual enterprises
Communications of the ACM
Computational intelligence for decision support
Computational intelligence for decision support
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Data mining: concepts and techniques
Data mining: concepts and techniques
The Future of Emarkets: Multi-Dimensional Market Mechanisms
The Future of Emarkets: Multi-Dimensional Market Mechanisms
The Design of Intelligent Agents: A Layered Approach
The Design of Intelligent Agents: A Layered Approach
Design of Roles and Protocols for Electronic Negotiations
Electronic Commerce Research
Autonomous Agents and Multi-Agent Systems
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
Technology Trends and Drivers and a Vision of the Future of e-Business
EDOC '00 Proceedings of the 4th International conference on Enterprise Distributed Object Computing
Agent-mediated electronic commerce: a survey
The Knowledge Engineering Review
Electronic commerce: from economic and game-theoretic models to working protocols
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Trading Paper Clips --An Analysis of “Trading Up” in Artificial Societies without Altruists
Proceedings of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence
| Hi-index | 0.00 |
A market is in equilibrium if there is no opportunity for arbitrage, ie: risk-free, or low-risk, profit. The majority of real markets are not in equilibrium. A project is investigating the market evolutionary process in a particular electronic market that has been constructed in an on-going collaborative research project between a university and a software house. The way in which actors (buyers, sellers and others) use the market will be influenced by the information available to them. In this experiment, data mining and filtering techniques are used to distil both individual signals drawn from the markets and signals from the Internet into meaningful advice for the actors. The goal of this experiment is first to learn how actors will use the advice available to them to identify arbitrage opportunities, and second how the market will evolve through entrepreneurial intervention. In this electronic market a multiagent process management system is used to manage all market transactions including those that drive the market evolutionary process.