Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Information-driven phase changes in multi-agent coordination
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Universality in Multi-Agent Systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Stigmergic Learning for Self-Organizing Mobile Ad-Hoc Networks (MANET's)
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments
Proceedings of the 2005 conference on Self-Organization and Autonomic Informatics (I)
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Analyzing stigmergic learning for self-organizing mobile ad-hoc networks (MANET's)
Engineering Self-Organising Systems
Managing dynamic flows in production chains through self-organization
Engineering Self-Organising Systems
Applying distributed adaptive optimization to digital car body development
Engineering Self-Organising Systems
A new protocol to share critical resources by self-organized coordination
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
Information-Driven phase changes in multi-agent coordination
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
A study of system nervousness in multi-agent manufacturing control system
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
Self-organization and multiagent systems: II. Applications and the development technology
Journal of Computer and Systems Sciences International
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System performance in multi-agent resource allocation systems can often improve if individual agents reduce their activity. Agents need a way to modulate their individual behavior in light of the system's state, preferably without centralized control. We illustrate the problem of hyperactive agents in two domains related to resource allocation. A simple, decentralized scheme, inspired by insect pheromones, enables individual agents to adjust their activity as the system operates, and suggests a general approach to dealing with approaching deadlines.