Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies

Corrado Grappiolo; Julian Togelius; Georgios N. Yannakakis

doi:10.1109/ALIFE.2013.6602441

Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies

Corrado Grappiolo, Julian Togelius, Georgios N. Yannakakis

Center for Digital Play

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in- group and out-group, (2) reinforcement learning for deriving the existing levels of collaboration within the society, and (3) an evolutionary algorithm for the detection of group structures and the assignment of group identities to the agents. Under a case study of static societies — i.e. the agents do not evolve their social preferences — where agents interact with each other by means of the Ultimatum Game, our approach proves to be successful for small-sized social networks independently on the underlying social structure of the society; promising results are also registered for mid-size societies.

Original language	English
Title of host publication	2013 IEEE Symposium on Artificial Life, Proceedings : ALife 2013
Number of pages	8
Publisher	IEEE Press
Publication date	2013
Pages	126-133
ISBN (Print)	9781467358620
DOIs	https://doi.org/10.1109/ALIFE.2013.6602441
Publication status	Published - 2013
Event	2013 IEEE Symposium on Artificial Life - Singapore, Singapore Duration: 16 Apr 2013 → 19 Apr 2013 http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=31367

Conference

Conference	2013 IEEE Symposium on Artificial Life
Country/Territory	Singapore
City	Singapore
Period	16/04/2013 → 19/04/2013
Internet address	http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=31367

Keywords

Group Identity Detection, Evolutionary Computa- tion, Artificial Societies, Emergence of Complexity.

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10.1109/ALIFE.2013.6602441

Cite this

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title = "Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies",

abstract = "This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in- group and out-group, (2) reinforcement learning for deriving the existing levels of collaboration within the society, and (3) an evolutionary algorithm for the detection of group structures and the assignment of group identities to the agents. Under a case study of static societies — i.e. the agents do not evolve their social preferences — where agents interact with each other by means of the Ultimatum Game, our approach proves to be successful for small-sized social networks independently on the underlying social structure of the society; promising results are also registered for mid-size societies.",

keywords = "Group Identity Detection, Evolutionary Computa- tion, Artificial Societies, Emergence of Complexity.",

author = "Corrado Grappiolo and Julian Togelius and Yannakakis, {Georgios N.}",

note = "Please add publication information and choose publication type, eg. conference article, journal article etc.; 2013 IEEE Symposium on Artificial Life, ALife 2013 ; Conference date: 16-04-2013 Through 19-04-2013",

year = "2013",

doi = "10.1109/ALIFE.2013.6602441",

language = "English",

isbn = "9781467358620",

pages = "126--133",

booktitle = "2013 IEEE Symposium on Artificial Life, Proceedings",

publisher = "IEEE Press",

url = "http://www.ieee.org/conferences_events/conferences/conferencedetails/index.html?Conf_ID=31367",

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Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies. / Grappiolo, Corrado; Togelius, Julian; Yannakakis, Georgios N.
2013 IEEE Symposium on Artificial Life, Proceedings: ALife 2013. IEEE Press, 2013. p. 126-133.

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

TY - GEN

T1 - Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies

AU - Grappiolo, Corrado

AU - Togelius, Julian

AU - Yannakakis, Georgios N.

N1 - Please add publication information and choose publication type, eg. conference article, journal article etc.

PY - 2013

Y1 - 2013

N2 - This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in- group and out-group, (2) reinforcement learning for deriving the existing levels of collaboration within the society, and (3) an evolutionary algorithm for the detection of group structures and the assignment of group identities to the agents. Under a case study of static societies — i.e. the agents do not evolve their social preferences — where agents interact with each other by means of the Ultimatum Game, our approach proves to be successful for small-sized social networks independently on the underlying social structure of the society; promising results are also registered for mid-size societies.

AB - This paper aims at detecting the presence of group structures in complex artificial societies by solely observing and analysing the interactions occurring among the artificial agents. Our approach combines: (1) an unsupervised method for clustering interactions into two possible classes, namely in- group and out-group, (2) reinforcement learning for deriving the existing levels of collaboration within the society, and (3) an evolutionary algorithm for the detection of group structures and the assignment of group identities to the agents. Under a case study of static societies — i.e. the agents do not evolve their social preferences — where agents interact with each other by means of the Ultimatum Game, our approach proves to be successful for small-sized social networks independently on the underlying social structure of the society; promising results are also registered for mid-size societies.

KW - Group Identity Detection, Evolutionary Computa- tion, Artificial Societies, Emergence of Complexity.

U2 - 10.1109/ALIFE.2013.6602441

DO - 10.1109/ALIFE.2013.6602441

M3 - Article in proceedings

SN - 9781467358620

SP - 126

EP - 133

BT - 2013 IEEE Symposium on Artificial Life, Proceedings

PB - IEEE Press

T2 - 2013 IEEE Symposium on Artificial Life

Y2 - 16 April 2013 through 19 April 2013

ER -

Artificial Evolution for the Detection of Group Identities in Complex Artificial Societies

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Conference

Keywords

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