Towards Detecting Group Identities in Complex Artificial Societies

Corrado Grappiolo, Georgios N. Yannakakis

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Abstrakt

This paper presents a framework for modelling group struc- tures and dynamics in both artificial societies and human-populated vir- tual environments such as computer games. The group modelling (GM) framework proposed focuses on the detection of existing, pre-defined group structures and is composed of a reinforcement learning method that infers collaboration values from the society’s local interactions and a clustering algorithm that detects group identities based on the learned collaboration values. An empirical evaluation of the framework in the social ultimatum bargain game shows that the GM method proposed is robust independently of the size of the society and the locality of the interactions.
OriginalsprogEngelsk
TitelFrom Animals to Animats 12 : 12th International Conference on Simulation of Adaptive Behavior, SAB 2012, Odense, Denmark, August 27-30, 2012. Proceedings
ForlagSpringer
Publikationsdato2012
Sider421-430
ISBN (Trykt)978-3-642-33092-6
StatusUdgivet - 2012
NavnLecture Notes in Computer Science
Vol/bind7426
ISSN0302-9743

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