research-article

A novel human-computer collaboration: combining novelty search with interactive evolution

Authors:

Brian G. Woolley,

Kenneth O. StanleyAuthors Info & Claims

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

Pages 233 - 240

https://doi.org/10.1145/2576768.2598353

Published: 12 July 2014 Publication History

Abstract

Recent work on novelty and behavioral diversity in evolutionary computation has highlighted the potential disadvantage of driving search purely through objective means. This paper suggests that leveraging human insight during search can complement such novelty-driven approaches. In particular, a new approach called novelty-assisted interactive evolutionary computation (NA-IEC) combines human intuition with novelty search to facilitate the serendipitous discovery of agent behaviors in a deceptive maze. In this approach, the human user directs evolution by selecting what is interesting from the on-screen population of behaviors. However, unlike in typical IEC, the user can now request that the next generation be filled with novel descendants. The experimental results demonstrate that combining human insight with novelty search not only finds solutions significantly faster and at lower genomic complexities than fully-automated processes guided purely by fitness or novelty, but it also finds solutions faster than the traditional IEC approach. Such results add to the evidence that combining human users and automated processes creates a synergistic effect in the search for solutions.

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Cited By

Lanzi PLoiacono DSilva SPaquete L(2023)ChatGPT and Other Large Language Models as Evolutionary Engines for Online Interactive Collaborative Game DesignProceedings of the Genetic and Evolutionary Computation Conference10.1145/3583131.3590351(1383-1390)Online publication date: 15-Jul-2023
https://dl.acm.org/doi/10.1145/3583131.3590351
Zhang J(2022)Researches advanced in human-computer collaboration and human-machine cooperation: from variances to common prospect2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)10.1117/12.2641865(142)Online publication date: 10-Nov-2022
https://doi.org/10.1117/12.2641865
Bedenkov AMoreno CAgustin LJain NNewman AFeng LKostello G(2021)Customer Centricity in Medical Affairs Needs Human-centric Artificial IntelligencePharmaceutical Medicine10.1007/s40290-020-00378-1Online publication date: 19-Jan-2021
https://doi.org/10.1007/s40290-020-00378-1
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Index Terms

A novel human-computer collaboration: combining novelty search with interactive evolution
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

GECCO '14: Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

July 2014

1478 pages

ISBN:9781450326629

DOI:10.1145/2576768

Editor-in-chief:
Christian Igel
Ruhr University of Bochum, University of Copenhagen
,
General Chair:
Dirk V. Arnold
Dalhousie University

Copyright © 2014 ACM.

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Published: 12 July 2014

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GECCO '14: Genetic and Evolutionary Computation Conference

July 12 - 16, 2014

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GECCO '14 Paper Acceptance Rate 180 of 544 submissions, 33%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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https://dl.acm.org/doi/10.1145/3583131.3590351
Zhang J(2022)Researches advanced in human-computer collaboration and human-machine cooperation: from variances to common prospect2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022)10.1117/12.2641865(142)Online publication date: 10-Nov-2022
https://doi.org/10.1117/12.2641865
Bedenkov AMoreno CAgustin LJain NNewman AFeng LKostello G(2021)Customer Centricity in Medical Affairs Needs Human-centric Artificial IntelligencePharmaceutical Medicine10.1007/s40290-020-00378-1Online publication date: 19-Jan-2021
https://doi.org/10.1007/s40290-020-00378-1
Grbic DPalm RNajarro EGlanois CRisi S(2021)EvoCraft: A New Challenge for Open-EndednessApplications of Evolutionary Computation10.1007/978-3-030-72699-7_21(325-340)Online publication date: 1-Apr-2021
https://doi.org/10.1007/978-3-030-72699-7_21
Schrum JGutierrez JVolz VLiu JLucas SRisi SCoello Coello C(2020)Interactive evolution and exploration within latent level-design space of generative adversarial networksProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3389821(148-156)Online publication date: 25-Jun-2020
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Lehman J(2020)Evolutionary Computation and AI SafetyGenetic Programming Theory and Practice XVII10.1007/978-3-030-39958-0_10(181-200)Online publication date: 8-May-2020
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Stanley K(2019)Why open-endedness mattersArtificial Life10.1162/artl_a_0029425:3(232-235)Online publication date: 1-Aug-2019
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Hagg AAsteroth ABäck TAuger AStützle T(2019)Modeling user selection in quality diversityProceedings of the Genetic and Evolutionary Computation Conference10.1145/3321707.3321823(116-124)Online publication date: 13-Jul-2019
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Melotti Ade Moraes C(2019)Evolving Roguelike Dungeons With Deluged Novelty Search Local CompetitionIEEE Transactions on Games10.1109/TG.2018.285942411:2(173-182)Online publication date: Jun-2019
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de Prado Salas PRisi STakadama K(2018)Collaborative interactive evolution in minecraftProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3205723(127-128)Online publication date: 6-Jul-2018
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