On Agent-Mediated Electronic Commerce
IEEE Transactions on Knowledge and Data Engineering
Forecasting market prices in a supply chain game
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Agent Mertacor: A robust design for dealing with uncertainty and variation in SCM environments
Expert Systems with Applications: An International Journal
Learning and multiagent reasoning for autonomous agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
The supply chain trading agent competition
Electronic Commerce Research and Applications
Bidding for customer orders in TAC SCM
AAMAS'04 Proceedings of the 6th AAMAS international conference on Agent-Mediated Electronic Commerce: theories for and Engineering of Distributed Mechanisms and Systems
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In complex and dynamic environments where interdependencies cannot monotonously determine causality, data mining techniques may be employed in order to analyze the problem, extract key features and identify pivotal factors. Typical cases of such complexity and dynamicity are supply chain networks, where a number of involved stakeholders struggle towards their own benefit. These stakeholders may be agents with varying degrees of autonomy and intelligence, in a constant effort to establish beneficiary contracts and maximize own revenue. In this paper, we illustrate the benefits of data mining analysis on a well-established agent supply chain management network. We apply data mining techniques, both at a macro and micro level, analyze the results and discuss them in the context of agent performance improvement.