WEIN 2010 - The Fifth International Workshop on Emergent Intelligence on Networked Agents
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AAMAS 2010 Satellite Workshop

The Fifth International Workshop on Emergent Intelligence on Networked Agents (WEIN 2010)

Learning coordinated behavior in agent communities

Sandip Sen

We have studied mechanisms that enable agents to learn coordinated behavior through repeated interaction with other members in a semi-stable agent community. This talk will highlight relevant results on tag-based coordination and norm emergence in agent communities.
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. We show that carefully-designed tag-based interaction and learning mechanisms can be effective in enabling stable, socially optimal, and fair cooperative outcomes to emerge in agent communities where agent interactions are modeled as general-sum games. Effective tag mechanisms can facilitate coordination in multiagent systems by allowing agents to effectively reuse knowledge learned form interactions with one agent when interacting with other agents sharing the same features.
Effective norms, emerging from sustained individual interactions over time, can complement societal rules and significantly enhance performance of individual agents and agent societies. We have studied the emergence of social norms via learning from interaction experiences where each interaction is viewed as a stage game. In this social learning model, which is distinct from an agent learning from repeated interactions against the same player, an agent learns a policy to play the game from repeated interactions with multiple learning agents. We discuss the effects of agent heterogeneity, interaction topology, and initial biases on the success and rate of norm emergence in agent communities.


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