Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Distributed Systems: Principles and Paradigms
Distributed Systems: Principles and Paradigms
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Efficient meta-level control in bounded-rational agents
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Meta-Level Reasoning in Deliberative Agents
IAT '04 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
Method to balance the communication among multi-agents in real time traffic synchronization
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I
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In a complex environment where the messages exchange tensely among the agents, a difficulty task is to decide the best action for new arriving messages during on-line control. The Meta-Level Control model is modified and used to improve the performance of the communication among the agents in this research. During the control process, the decision is made from the experience acquired by the agents with reinforcement learning. The research proposed a Messages Meta Manager (MMM) model for Air Flow Management System (AFMS) with the combination of the Meta-Level Control approach and reinforcement learning algorithms. With the developed system, the cases of initial heuristic (IH), epsilon adaptative (EA) and performance heuristic (PH) were tested. The results from simulation and analyses show the satisfactory to the research purpose.