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

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Linkola:2018:evoMusArt,

• author = "Simo Linkola and Otto Hantula",
• title = "On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study",
• booktitle = "7th International Conference on Computational Intelligence in Music, Sound, Art and Design, EvoMUSART 2018",
• year = "2018",
• editor = "Juan Romero and Antonios Liapis and Aniko Ekart",
• series = "LNCS",
• volume = "10783",
• publisher = "Springer",
• pages = "206--222",
• address = "Parma, Italy",
• month = "4-6 " # apr,
• organisation = "Species",
• keywords = "genetic algorithms, genetic programming, Computational social creativity, Evolutionary art, Collaboration, Learning from experience",
• isbn13 = "978-3-319-77582-1",
• DOI = "doi:10.1007/978-3-319-77583-8_14",
• abstract = "We study how artistically creative agents may learn to select favourable collaboration partners. We consider a society of creative agents with varying skills and aesthetic preferences able to interact with each other by exchanging artefacts or through collaboration. The agents exhibit interaction awareness by modelling 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.",
• notes = "EvoMusArt2018 held in conjunction with EuroGP'2018 EvoCOP2018 and EvoApplications2018 http://www.evostar.org/2018/cfp_evomusart.php",

}

Genetic Programming entries for Simo Linkola Otto Hantula

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