research-article

Accelerating human-computer collaborative search through learning comparative and predictive user models

Authors:

Gregory S. Hornby,

Josh C. BongardAuthors Info & Claims

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

Pages 225 - 232

https://doi.org/10.1145/2330163.2330196

Published: 07 July 2012 Publication History

Abstract

Interactive Evolutionary Algorithms (IEAs) have much potential for allowing a human user to guide a search algorithm, but have struggled to overcome the limitations of slow, easily-fatigued human users. Here we describe The Approximate User (TAU) system in which these limitations are overcome by using a model of the user's preferences - which are continuously built and refined during the search process - to drive the search algorithm. Two variations of a user-modeling approach are compared to determine if this approach can accelerate IEA search. The two user-modeling approaches compared are: 1. learning a classifier which correctly determines which of two designs is better; and 2. learning a model which predicts a fitness score. Rather than having people do the user-testing, we propose the use of a simulated user as an easier means to test IEAs. Both variants of the TAU IEA are compared against a basic IEA and it is shown that TAU is up to 2.7 times faster and 15 times more reliable at producing near optimal results. In addition, we see TAU as a step toward building a more general Human-Computer Collaborative system.

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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
Martinez JSottet JFrey ABissyandé TZiadi TKlein JTemple PAcher Mle Traon Y(2018)Towards Estimating and Predicting User Perception on Software Product VariantsNew Opportunities for Software Reuse10.1007/978-3-319-90421-4_2(23-40)Online publication date: 17-Apr-2018
https://doi.org/10.1007/978-3-319-90421-4_2
Gaspar ABari AKumar ABucci AWiegand RAlbert J(2016)Evolutionary Practice Problems Generation: Design Guidelines2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2016.0089(544-548)Online publication date: Nov-2016
https://doi.org/10.1109/ICTAI.2016.0089
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Index Terms

Accelerating human-computer collaborative search through learning comparative and predictive user models
1. Computing methodologies
  1. Machine learning

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cover image ACM Conferences

GECCO '12: Proceedings of the 14th annual conference on Genetic and evolutionary computation

July 2012

1396 pages

ISBN:9781450311779

DOI:10.1145/2330163

Editor:
Terence Soule
University of Idaho, USA
,
General Chair:
Jason H. Moore
Dartmouth College, USA

Copyright © 2012 Authors.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2012

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GECCO '12: Genetic and Evolutionary Computation Conference

July 7 - 11, 2012

Pennsylvania, Philadelphia, USA

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

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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
Martinez JSottet JFrey ABissyandé TZiadi TKlein JTemple PAcher Mle Traon Y(2018)Towards Estimating and Predicting User Perception on Software Product VariantsNew Opportunities for Software Reuse10.1007/978-3-319-90421-4_2(23-40)Online publication date: 17-Apr-2018
https://doi.org/10.1007/978-3-319-90421-4_2
Gaspar ABari AKumar ABucci AWiegand RAlbert J(2016)Evolutionary Practice Problems Generation: Design Guidelines2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI.2016.0089(544-548)Online publication date: Nov-2016
https://doi.org/10.1109/ICTAI.2016.0089
Christman JWoolley B(2015)Augmenting Interactive Evolution with Multi-objective Optimization2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA.2015.139(973-980)Online publication date: Dec-2015
https://doi.org/10.1109/ICMLA.2015.139
Woolley BStanley KArnold D(2014)A novel human-computer collaborationProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598353(233-240)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598353
Shi JMengshoel OPal DArnold D(2014)Feedback control for multi-modal optimization using genetic algorithmsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598231(839-846)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598231
Bongard JHornby GAlba E(2013)Combining fitness-based search and user modeling in evolutionary roboticsProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2500097(159-166)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2500097
Dorin AAlba E(2013)Aesthetic selection and the stochastic basis of art, design and interactive evolutionary computationProceedings of the 15th annual conference on Genetic and evolutionary computation10.1145/2463372.2463412(311-318)Online publication date: 6-Jul-2013
https://dl.acm.org/doi/10.1145/2463372.2463412

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