Communications of the ACM
Neural network and fuzzy logic applications in C/C++
Neural network and fuzzy logic applications in C/C++
Brokering and matchmaking for coordination of agent societies: a survey
Coordination of Internet agents
Software Engineering Risk Management: Finding Your Path through the Jungle
Software Engineering Risk Management: Finding Your Path through the Jungle
An improvement to matchmaking algorithms for middle agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Intelligent Software Agents: Foundations and Applications
Intelligent Software Agents: Foundations and Applications
Heterogeneous Agent Systems
Autonomous Agents and Multi-Agent Systems
Impact: A Platform for Collaborating Agents
IEEE Intelligent Systems
Matchmaking to Support Intelligent Agents for Portfolio Management
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A Taxonomy of Middle-Agents for the Internet
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Agent-Based Hybrid Intelligent Systems
Agent-Based Hybrid Intelligent Systems
Grid Computing
The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver
IEEE Transactions on Computers
Matchmaking for information agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Middle agents play a very important role in multiagent systems used in open and dynamic environments. The performance of middle agents relies heavily on the matchmaking algorithms used. The actual performance of provider agents in accomplishing delegated tasks has a significant impact on the matchmaking outcomes of middle agents. With those observations in mind, this paper discusses the incorporation of the track records of agents in accomplishing delegated tasks into the matchmaking process. How to provide initial values for track records in the algorithm are detailed. A prototype is also built to verify the algorithm. Based on the improved algorithm, the matchmaking outcomes are more accurate and reasonable.