Planning in distributed artificial intelligence
Foundations of distributed artificial intelligence
Distributed rational decision making
Multiagent systems
Multi-Agent coordination based on tokens: reduction of the bullwhip effect in a forest supply chain
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
A key-based coordination algorithm for dynamic readiness and repair service coordination
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Self-Organizing Manufacturing Control: An Industrial Application of Agent Technology
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Multiagent Systems for Manufacturing Control
Multiagent Systems for Manufacturing Control
Proceedings of the 35th conference on Winter simulation: driving innovation
An integrated token-based algorithm for scalable coordination
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Coordination to avoid starvation of bottleneck agents in a large network system
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-agent coordination using local search
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Multiagent coordination for controlling complex and unstable manufacturing processes
Expert Systems with Applications: An International Journal
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We present a multiagent coordination technique to maintain throughput of a large-scale agent network system in the face of failures of agents. Failures do not just deteriorate throughput of the system but also create and change bottlenecks in the system. Since loss of bottleneck's capacity degrades the overall system performance, the system should identify bottlenecks dynamically and keep their utilization at a high level. In our system, CABS, information about an agent's urgency of jobs to fulfill demanded throughput and maintain its utilization is passed to upstream agents in the network. Upstream agents utilize this information to identify bottleneck agents and coordinate their actions to provide the bottlenecks with necessary and sufficient jobs for preventing their starvation and congestion. We empirically evaluate CABS using a benchmark problem of the semiconductor fabrication process, which is a good example of a large-scale network system, in comparison with a well-known traditional manufacturing control method.