Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Effort profiles in multi-agent resource allocation
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Resource-aware exploration of the emergent dynamics of simulated systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
How to calm hyperactive agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Mesoscopic modeling of emergent behavior – a self-organizing deliberative minority game
ESOA'05 Proceedings of the Third international conference on Engineering Self-Organising Systems
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Large systems of agents deployed in a real-world environment face threats to their problem solving performance that are independent of the complexity of the problem or the characteristics of their specific solution mechanism. One such threat is the degrading of the quality of agent coordination mechanisms when faced with delays in the flow of critical information among the agents introduced by communication latencies. In this paper we demonstrate in a simple model of locally interacting agents that the emerging system-level performance may degrade very suddenly as the rate of individual decision making increases against the availability of up-to-date information. We present results from extensive simulation experiments that lead us to select a locally accessible metric to adapt the agent's individual decision rate to values that are below this phase change. Given the generic nature of the coordination mechanism that is analyzed and the information-theoretic metric, the adaptation mechanism may increase the deployability of large-scale agent systems in real-world applications.