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Emergence of social norms through collective learning in networked agent societies

Conference Paper


Abstract


  • Social norms play a pivotal role in sustaining social order by

    regulating individual behaviors in a society. In normative

    multiagent systems, social norms have been used as an efficient

    mechanism to govern virtual agent societies towards

    cooperation and coordination. In this paper, we study the

    emergence of social norms via learning from repeated local

    interactions in networked agent societies. We propose

    a collective learning framework, which imitates the opinion

    aggregation process in human decision making, to study the

    impact of agent local collective behaviors on norm emergence

    in different situations. In the framework, each agent

    interacts repeatedly with all of its neighbors. At each step,

    an agent first takes a best-response action towards each of

    its neighbors and then combines all of these actions into

    a final action using ensemble learning methods. We conduct

    extensive experiments to evaluate the framework with

    respect to different network topologies, learning strategies,

    numbers of actions, and so on. Experimental results reveal

    some significant insights into norm emergence in networked

    agent societies achieved through local collective behaviors.

Publication Date


  • 2013

Citation


  • Yu, C., Zhang, M., Ren, F. & Luo, X. (2013). Emergence of social norms through collective learning in networked agent societies. 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013) (pp. 475-482). United States: IEEE.

Scopus Eid


  • 2-s2.0-84890511053

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/937

Has Global Citation Frequency


Start Page


  • 475

End Page


  • 482

Place Of Publication


  • United States

Abstract


  • Social norms play a pivotal role in sustaining social order by

    regulating individual behaviors in a society. In normative

    multiagent systems, social norms have been used as an efficient

    mechanism to govern virtual agent societies towards

    cooperation and coordination. In this paper, we study the

    emergence of social norms via learning from repeated local

    interactions in networked agent societies. We propose

    a collective learning framework, which imitates the opinion

    aggregation process in human decision making, to study the

    impact of agent local collective behaviors on norm emergence

    in different situations. In the framework, each agent

    interacts repeatedly with all of its neighbors. At each step,

    an agent first takes a best-response action towards each of

    its neighbors and then combines all of these actions into

    a final action using ensemble learning methods. We conduct

    extensive experiments to evaluate the framework with

    respect to different network topologies, learning strategies,

    numbers of actions, and so on. Experimental results reveal

    some significant insights into norm emergence in networked

    agent societies achieved through local collective behaviors.

Publication Date


  • 2013

Citation


  • Yu, C., Zhang, M., Ren, F. & Luo, X. (2013). Emergence of social norms through collective learning in networked agent societies. 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013) (pp. 475-482). United States: IEEE.

Scopus Eid


  • 2-s2.0-84890511053

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/937

Has Global Citation Frequency


Start Page


  • 475

End Page


  • 482

Place Of Publication


  • United States