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A coevolutionary approach to learn animal behavior through controlled interaction

Published: 06 July 2013 Publication History

Abstract

This paper proposes a method that allows a machine to infer the behavior of an animal in a fully automatic way. In principle, the machine does not need any prior information about the behavior. It is able to modify the environmental conditions and observe the animal; therefore it can learn about the animal through controlled interaction. Using a competitive coevolutionary approach, the machine concurrently evolves animats, that is, models to approximate the animal, as well as classifiers to discriminate between animal and animat. We present a proof-of-concept study conducted in computer simulation that shows the feasibility of the approach. Moreover, we show that the machine learns significantly better through interaction with the animal than through passive observation. We discuss the merits and limitations of the approach and outline potential future directions.

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  • (2020)Twenty Years Beyond the Turing Test: Moving Beyond the Human Judges TooMinds and Machines10.1007/s11023-020-09549-0Online publication date: 4-Nov-2020
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cover image ACM Conferences
GECCO '13: Proceedings of the 15th annual conference on Genetic and evolutionary computation
July 2013
1672 pages
ISBN:9781450319638
DOI:10.1145/2463372
  • Editor:
  • Christian Blum,
  • General Chair:
  • Enrique Alba
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 06 July 2013

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Author Tags

  1. animal behavior
  2. artificial life
  3. coevolution
  4. evolutionary robotics
  5. interaction
  6. science automation
  7. turing test

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GECCO '13
Sponsor:
GECCO '13: Genetic and Evolutionary Computation Conference
July 6 - 10, 2013
Amsterdam, The Netherlands

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GECCO '13 Paper Acceptance Rate 204 of 570 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

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  • (2024)Application of a Neural Network to Solve a Two-Dimensional Problem of Flowing around an Obstacle of Arbitrary Shape in a ChannelPhysics of Particles and Nuclei10.1134/S106377962403015855:3(627-629)Online publication date: 6-Jun-2024
  • (2020)Turing learning with hybrid discriminatorsProceedings of the 2020 Genetic and Evolutionary Computation Conference Companion10.1145/3377929.3390051(121-122)Online publication date: 8-Jul-2020
  • (2020)Twenty Years Beyond the Turing Test: Moving Beyond the Human Judges TooMinds and Machines10.1007/s11023-020-09549-0Online publication date: 4-Nov-2020
  • (2020)Quality Assessment Method for GAN Based on Modified Metrics Inception Score and Fréchet Inception DistanceSoftware Engineering Perspectives in Intelligent Systems10.1007/978-3-030-63322-6_8(102-114)Online publication date: 16-Dec-2020
  • (2019)Competitive Coevolution for Color Image Steganography2019 International Conference on Intelligent Computing and Control Systems (ICCS)10.1109/ICCS45141.2019.9065844(719-723)Online publication date: May-2019
  • (2018)Modelling Human Movements With Turing Learning2018 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI.2018.8628691(2254-2261)Online publication date: Nov-2018
  • (2017)Generalizing GANsProceedings of the 31st International Conference on Neural Information Processing Systems10.5555/3295222.3295379(6319-6329)Online publication date: 4-Dec-2017
  • (2016)Turing learning: a metric-free approach to inferring behavior and its application to swarmsSwarm Intelligence10.1007/s11721-016-0126-110:3(211-243)Online publication date: 30-Aug-2016
  • (2014)Coevolutionary learning of swarm behaviors without metricsProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598349(201-208)Online publication date: 12-Jul-2014

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