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

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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.

Original languageEnglish
Title of host publicationComputational Intelligence in Music, Sound, Art and Design : 7th International Conference, EvoMUSART 2018, Parma, Italy, April 4-6, 2018, Proceedings
EditorsAntonios Liapis, Juan Jesús Romero Cardalda, Anikó Ekárt
Number of pages17
Place of PublicationCham
PublisherSpringer International Publishing AG
Publication date4 Apr 2018
ISBN (Print)978-3-319-77582-1
ISBN (Electronic)978-3-319-77583-8
Publication statusPublished - 4 Apr 2018
MoE publication typeA4 Article in conference proceedings
EventInternational Conference on Computational Intelligence in Music, Sound, Art and Design - Parma, Italy
Duration: 4 Apr 20186 Apr 2018
Conference number: 7

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing AG
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fields of Science

  • 113 Computer and information sciences
  • computational social creativity
  • evolutionary art
  • collaboration
  • learning from experience

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