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

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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.

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
Publisher Springer International Publishing AG
Publication date4 Apr 2018
Pages206-222
ISBN (Print)978-3-319-77582-1
ISBN (Electronic)978-3-319-77583-8
DOIs
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
Volume10783
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

Cite this

Linkola, S., & Hantula, O. (2018). On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study. In A. Liapis, J. J. R. Cardalda, & A. Ekárt (Eds.), Computational Intelligence in Music, Sound, Art and Design: 7th International Conference, EvoMUSART 2018, Parma, Italy, April 4-6, 2018, Proceedings (pp. 206-222). (Lecture Notes in Computer Science; Vol. 10783). Cham: Springer International Publishing AG. https://doi.org/10.1007/978-3-319-77583-8_14
Linkola, Simo ; Hantula, Otto. / On Collaborator Selection in Creative Agent Societies : An Evolutionary Art Case Study. Computational Intelligence in Music, Sound, Art and Design: 7th International Conference, EvoMUSART 2018, Parma, Italy, April 4-6, 2018, Proceedings. editor / Antonios Liapis ; Juan Jesús Romero Cardalda ; Anikó Ekárt. Cham : Springer International Publishing AG, 2018. pp. 206-222 (Lecture Notes in Computer Science).
@inproceedings{f01d8015014d4cd29a5a098fdb2cb4ad,
title = "On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study",
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.",
keywords = "113 Computer and information sciences, computational social creativity, evolutionary art, collaboration, learning from experience",
author = "Simo Linkola and Otto Hantula",
year = "2018",
month = "4",
day = "4",
doi = "10.1007/978-3-319-77583-8_14",
language = "English",
isbn = "978-3-319-77582-1",
series = "Lecture Notes in Computer Science",
publisher = "Springer International Publishing AG",
pages = "206--222",
editor = "Antonios Liapis and Cardalda, {Juan Jes{\'u}s Romero} and Anik{\'o} Ek{\'a}rt",
booktitle = "Computational Intelligence in Music, Sound, Art and Design",
address = "Switzerland",

}

Linkola, S & Hantula, O 2018, On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study. in A Liapis, JJR Cardalda & A Ekárt (eds), Computational Intelligence in Music, Sound, Art and Design: 7th International Conference, EvoMUSART 2018, Parma, Italy, April 4-6, 2018, Proceedings. Lecture Notes in Computer Science, vol. 10783, Springer International Publishing AG, Cham, pp. 206-222, International Conference on Computational Intelligence in Music, Sound, Art and Design, Parma, Italy, 04/04/2018. https://doi.org/10.1007/978-3-319-77583-8_14

On Collaborator Selection in Creative Agent Societies : An Evolutionary Art Case Study. / Linkola, Simo; Hantula, Otto.

Computational Intelligence in Music, Sound, Art and Design: 7th International Conference, EvoMUSART 2018, Parma, Italy, April 4-6, 2018, Proceedings. ed. / Antonios Liapis; Juan Jesús Romero Cardalda; Anikó Ekárt. Cham : Springer International Publishing AG, 2018. p. 206-222 (Lecture Notes in Computer Science; Vol. 10783).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

TY - GEN

T1 - On Collaborator Selection in Creative Agent Societies

T2 - An Evolutionary Art Case Study

AU - Linkola, Simo

AU - Hantula, Otto

PY - 2018/4/4

Y1 - 2018/4/4

N2 - 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.

AB - 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.

KW - 113 Computer and information sciences

KW - computational social creativity

KW - evolutionary art

KW - collaboration

KW - learning from experience

U2 - 10.1007/978-3-319-77583-8_14

DO - 10.1007/978-3-319-77583-8_14

M3 - Conference contribution

SN - 978-3-319-77582-1

T3 - Lecture Notes in Computer Science

SP - 206

EP - 222

BT - Computational Intelligence in Music, Sound, Art and Design

A2 - Liapis, Antonios

A2 - Cardalda, Juan Jesús Romero

A2 - Ekárt, Anikó

PB - Springer International Publishing AG

CY - Cham

ER -

Linkola S, Hantula O. On Collaborator Selection in Creative Agent Societies: An Evolutionary Art Case Study. In Liapis A, Cardalda JJR, Ekárt A, editors, Computational Intelligence in Music, Sound, Art and Design: 7th International Conference, EvoMUSART 2018, Parma, Italy, April 4-6, 2018, Proceedings. Cham: Springer International Publishing AG. 2018. p. 206-222. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-77583-8_14