Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Machine Learning
Rational Communication in Multi-Agent Environments
Autonomous Agents and Multi-Agent Systems
Predicting the Expected Behavior of Agents that Learn About Agents: The CLRI Framework
Autonomous Agents and Multi-Agent Systems
Congregation Formation in Multiagent Systems
Autonomous Agents and Multi-Agent Systems
The Moving Target Function Problem in Multi-Agent Learning
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
An evidential and genetic method for cooperative learning systems
Multiagent and Grid Systems
An evidential cooperative multi-agent system
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
We introduce the topic of learning in multiagent systems. We first provide a quick introduction to the field of game theory, focusing on the equilibrium concepts of iterated dominance, and Nash equilibrium. We show some of the most relevant findings in the theory of learning in games, including theorems on fictitious play, replicator dynamics, and evolutionary stable strategies. The CLRI theory and n-level learning agents are introduced as attempts to apply some of these findings to the problem of engineering multiagent systems with learning agents. Finally, we summarize some of the remaining challenges in the field of learning in multiagent systems.