Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Communications of the ACM
Stereotypes in information filtering systems
Information Processing and Management: an International Journal
Bayesian learning in negotiation
International Journal of Human-Computer Studies - Evolution and learning in multiagent systems
Exploiting learning techniques for the acquisition of user stereotypes and communities
UM '99 Proceedings of the seventh international conference on User modeling
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
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
Introduction to recommender systems: Algorithms and Evaluation
ACM Transactions on Information Systems (TOIS)
Run the GAMUT: A Comprehensive Approach to Evaluating Game-Theoretic Algorithms
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
An empirical testing of user stereotypes of information retrieval systems
Information Processing and Management: an International Journal - Special issue: Cross-language information retrieval
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
An Evolutionary Dynamical Analysis of Multi-Agent Learning in Iterated Games
Autonomous Agents and Multi-Agent Systems
Digital Content Recommender on the Internet
IEEE Intelligent Systems
Accelerating reinforcement learning through implicit imitation
Journal of Artificial Intelligence Research
Satisficing and learning cooperation in the prisoner's dilemma
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Learning models of intelligent agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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
Applying a socially inspired technique (tags) to improve cooperation in P2P networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Tags and image scoring for robust cooperation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Effective tag mechanisms for evolving cooperation
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Changing neighbours: improving tag-based cooperation
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
The effects of evolved sociability in a commons dilemma
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Achieving Socially Optimal Outcomes in Multiagent Systems with Reinforcement Social Learning
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Tags or observable features shared by a group of similar agents are effectively used in real and artificial societies to signal intentions and can be used to infer unobservable properties and choose appropriate behaviors. Use of tags to select partners has been shown to produce stable cooperation in agent populations playing the Prisoner's Dilemma game. Existing tag mechanisms, however, can promote cooperation only if that requires identical actions from all group members. We propose a more general tag-based interaction scheme that facilitates and supports significantly richer coordination between agents. Our work is motivated by previous research that showed the ineffectiveness of current tag schemes for solving games requiring divergent actions. The mechanisms proposed here not only solves those problems but are effective for other general-sum games. We argue that these general-purpose tag mechanisms allow new application possibilities of multiagent learning algorithms as they allow an agent to reuse its learned knowledge about one agent when interacting with other agents sharing the same observable features.