Multiagent learning using a variable learning rate
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Mobilized Ad-Hoc Networks: A Reinforcement Learning Approach
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Hierarchical multi-agent reinforcement learning
Autonomous Agents and Multi-Agent Systems
Learning the task allocation game
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
End-to-end routing behavior in the internet
ACM SIGCOMM Computer Communication Review
VizScript: visualizing complex interactions in multi-agent systems
Proceedings of the 12th international conference on Intelligent user interfaces
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Generalized multiagent learning with performance bound
Autonomous Agents and Multi-Agent Systems
Multiagent reinforcement learning and self-organization in a network of agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Weighted graphs and disconnected components: patterns and a generator
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Switching dynamics of multi-agent learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Tools for analyzing intelligent agent systems
Web Intelligence and Agent Systems
A multiagent reinforcement learning algorithm with non-linear dynamics
Journal of Artificial Intelligence Research
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Experimental analysis of networks of cooperative learning agents (to verify certain properties such as the system's stability) has been commonly used due to the complexity of theoretical analysis in such cases. Due to the large number of parameters to analyze, researchers used metrics that summarize the system in few parameters. Since in cooperative system the ultimate goal is to optimize some global metric, researchers typically analyzed the evolution of the global performance metric over time to verify system properties. For example, if the global metric improves and eventually stabilizes, it is considered a reasonable verification of the system's stability. The global performance metric, however, overlooks an important aspect of the system: the network structure. We show an experimental case study where the convergence of the global performance metric is deceiving, hiding an underlying instability in the system that later leads to a significant drop in performance. To expose such instability, we propose the use of the graph analysis methodology, where the network structure is summarized using some network measures. We develop a new network measure that summarizes an agent's interaction with its neighbors and takes the disparity of these interactions into account. The new measure is applied to our case study, clearly exposing the instability that was previously hidden by the global performance metric.