Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Evolving social rationality for MAS using "tags"
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
Effective tag mechanisms for evolving coordination
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
The success and failure of tag-mediated evolution of cooperation
LAMAS'05 Proceedings of the First international conference on Learning and Adaption in Multi-Agent Systems
Changing neighbours: improving tag-based cooperation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Achieving Socially Optimal Outcomes in Multiagent Systems with Reinforcement Social Learning
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Certain observable features (tags), shared by a group of similar agents, can be used to signal intentions and can be effectively used to infer unobservable properties. Such inference will enable the formulation of appropriate behaviors for interaction with those agents. Tags have been previously shown to be successful in social dilemma situations such as the prisoner's dilemma, and more recently have been shown to be applicable to other games by augmenting the standard tag mechanisms. We examine these more general tag mechanisms, and explain previously reported results by more thoroughly examining their fundamental designs. We show that these new tag mechanisms, along with some adjustments and augmentations, can be effective in enabling stable, socially optimal, and fair cooperative outcomes to emerge in general sum games. We focus, in particular, on general-sum conflicted games, where socially optimal outcomes do not necessarily yield the best results for individual agents. We argue that the improvements and understanding of these mechanisms expands the usability of tag mechanisms for facilitating coordination in multiagent systems. We argue that they allow agents to effectively reuse knowledge learned form interactions with one agent when interacting with other agents sharing the same features.