Evolving social rationality for MAS using "tags"
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Effective use of reputation in peer-to-peer environments
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Learning trust strategies in reputation exchange networks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Modeling reciprocal behavior in human bilateral negotiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Strategic Provider Selection in a Policy-Based Helping Scenario
CEC '10 Proceedings of the 12th IEEE International Conference on Commerce and Enterprise Computing
Evolving Cooperation through Reciprocity Using a Centrality-Based Reputation System
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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The Service Game is a model for reciprocity in multiagent systems. Here, agents interact repeatedly by requesting and providing services. In contrast to existing models where players are matched randomly, players of the Service Game may choose with whom they play. The rationale behind provider selection is to choose a provider that is likely to perform a task as desired. We develop a formal model for provider selection in the Service Game. An evolutionary process based on a genetic algorithm allows us to incorporate notions of bounded rationality, learning, and adaptation into the analysis of the game. We conduct a series of experiments to study the evolution of strategies and the emergence of cooperation. We show that cooperation is more expensive with provider selection than with random matching. Further, populations consisting of discriminators and defectors form a bistable community.