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International Conference on Computational Intelligence in Music, Sound, Art and Design

EvoMUSART 2018: Computational Intelligence in Music, Sound, Art and Design pp 206–222Cite as

On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10783))

Abstract

We study how artistically creative agents may learn to select favorable collaboration partners. We consider a society of creative agents with varying skills and aesthetic preferences able to interact with each other by exchanging artifacts or through collaboration. The agents exhibit interaction awareness by modeling their peers and make decisions about collaboration based on the learned peer models. To test the peer models, we devise an experimental collaboration process for evolutionary art, where two agents create an artifact by evolving the same artifact set in turns. In an empirical evaluation, we focus on how effective peer models are in selecting collaboration partners and compare the results to a baseline where agents select collaboration partners randomly. We observe that peer models guide the agents to more beneficial collaborations.

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Notes

  1. 1.

    Normalized value is the value of the artifact (using the agent’s aesthetic measure) divided by the value of the artifact the agent has valued the highest during that simulation run (clamped to 1.0).

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Acknowledgments

This work has been supported by the Academy of Finland under grant 313973 (CACS).

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Authors and Affiliations

  1. Department of Computer Science, University of Helsinki, Helsinki, Finland

    Simo Linkola & Otto Hantula

Authors
  1. Simo Linkola
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  2. Otto Hantula
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Correspondence to Simo Linkola .

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Editors and Affiliations

  1. University of Malta , Msida , Malta

    Antonios Liapis

  2. University of A Coruña , A Coruña, Spain

    Juan Jesús Romero Cardalda

  3. Aston University , Birmingham, United Kingdom

    Anikó Ekárt

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Linkola, S., Hantula, O. (2018). On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study. In: Liapis, A., Romero Cardalda, J., Ekárt, A. (eds) Computational Intelligence in Music, Sound, Art and Design. EvoMUSART 2018. Lecture Notes in Computer Science(), vol 10783. Springer, Cham. https://doi.org/10.1007/978-3-319-77583-8_14

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  • DOI: https://doi.org/10.1007/978-3-319-77583-8_14

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  • Print ISBN: 978-3-319-77582-1

  • Online ISBN: 978-3-319-77583-8

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