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Title: | Towards detecting group identities in complex artificial societies |
Authors: | Grappiolo, Corrado Yannakakis, Georgios N. |
Keywords: | Computer games -- Group identity Group games Virtual reality |
Issue Date: | 2012 |
Publisher: | Springer |
Citation: | Grappiolo, C., & Yannakakis G. N. (2012) Towards detecting group identities in complex artificial societies. In T. Ziemke, C. Balkenius, & J. Hallam (Eds.), From Animals to Animats 12. SAB 2012. Lecture Notes in Computer Science, vol. 7426, (pp. 421-430). Springer, Berlin, Heidelberg. |
Abstract: | This paper presents a framework for modelling group structures and dynamics in both artificial societies and human-populated virtual 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. |
URI: | https://www.um.edu.mt/library/oar//handle/123456789/29277 |
ISBN: | 9783642330933 |
Appears in Collections: | Scholarly Works - InsDG |
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File | Description | Size | Format | |
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Towards_detecting_group_identities_in_complex_artificial_societies_2012.pdf Restricted Access | 1.51 MB | Adobe PDF | View/Open Request a copy |
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