Technical Note: \cal Q-Learning
Machine Learning
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Online learning about other agents in a dynamic multiagent system
AGENTS '98 Proceedings of the second international conference on Autonomous agents
The dynamics of reinforcement learning in cooperative multiagent systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Learning in multiagent systems
Multiagent systems
Reasoning about commitments and penalties for coordination between autonomous agents
Proceedings of the fifth international conference on Autonomous agents
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Learning Situation-Specific Coordination in Cooperative Multi-agent Systems
Autonomous Agents and Multi-Agent Systems
Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent's Internal Model
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Performance models for large scale multiagent systems: using distributed POMDP building blocks
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The Dynamic Selection of Coordination Mechanisms
Autonomous Agents and Multi-Agent Systems
Learning when and how to coordinate
Web Intelligence and Agent Systems
European research and development of intelligent information agents: the agentlink perspective
Intelligent information agents
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This paper examines the potential and the impact of introducing learning capabilities into autonomous agents that make decisions at run-time about which mechanism to exploit in order to coordinate their activities. Specifically, the efficacy of learning is evaluated for making the decisions that are involved in determining when and how to coordinate. Our motivating hypothesis is that to deal with dynamic and unpredictable environments it is important to have agents that can learn the right situations in which to attempt to coordinate and the right method to use in those situations. This hypothesis is evaluated empirically, using reinforcement based algorithms, in a grid-world scenario in which a) an agent's prediction about the other agents in the environment is approximately correct and b) an agent can not correctly predict the others' behaviour. The results presented show when, where and why learning is effective when it comes to making a decision about selecting a coordination mechanism.