Modeling coordination in organizations and markets
Management Science
Technical Note: \cal Q-Learning
Machine Learning
Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Learning to coordinate without sharing information
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
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
On agent-based software engineering
Artificial Intelligence
Reasoning about commitments and penalties for coordination between autonomous agents
Proceedings of the fifth international conference on Autonomous agents
Designing Complex Organizations
Designing Complex Organizations
Learning to select a coordination mechanism
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Reflections on the Nature of Multi-Agent Coordination and Its Implications for an Agent Architecture
Autonomous Agents and Multi-Agent Systems
Learning Situation-Specific Coordination in Cooperative Multi-agent Systems
Autonomous Agents and Multi-Agent Systems
Sequential Optimality and Coordination in Multiagent Systems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
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)
The Dynamic Selection of Coordination Mechanisms
Autonomous Agents and Multi-Agent Systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Local strategy learning in networked multi-agent team formation
Autonomous Agents and Multi-Agent Systems
Investigating adaptive, confidence-based strategic negotiations in complex multiagent environments
Web Intelligence and Agent Systems
State space segmentation for acquisition of agent behavior
Web Intelligence and Agent Systems
Architecture of a discrete-event and agent-based crisis response simulation model
International Journal of Advanced Intelligence Paradigms
On-line coordination: Event interaction and state communication between cooperative agents
Web Intelligence and Agent Systems
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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 predictions about the other agents in the environment are 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.