Learning Multiple Conflicting Tasks with Artificial Evolution

Delphine Nicolay; Roli Andrea; Timoteo Carletti

doi:10.1007/978-3-319-12745-3_11

Learning Multiple Conflicting Tasks with Artificial Evolution

Delphine Nicolay, Roli Andrea, Timoteo Carletti

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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Abstract

The paper explores the issue of learning multiple competing tasks in the domain of artificial evolution. In particular, a robot is trained so as to be able to perform two different tasks at the same time, namely a gradient following and rough terrain avoidance behaviours. It is shown that, if the controller is trained to learn two tasks of different difficulty, then the robot performance is higher if the most difficult task is learnt first, before the combined learning of both tasks. An explanation to this superiority is also discussed, in comparison with previous results.

Original language	English
Title of host publication	Advances in Artificial Life and Evolutionary Computation
Subtitle of host publication	9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers
Editors	Clara Pizzuti, Giandomenico Spezzano
Publisher	Springer
Pages	127-139
Number of pages	13
Volume	445
ISBN (Electronic)	978-3-319-12745-3
ISBN (Print)	978-3-319-12744-6
DOIs	https://doi.org/10.1007/978-3-319-12745-3_11
Publication status	Published - 6 Nov 2014
Event	9th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2014 - Vietri sul Mare, Italy Duration: 14 May 2014 → 15 May 2014

Publication series

Name	Communications in Computer and Information Science
Volume	445
ISSN (Print)	18650929

Conference

Conference	9th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2014
Country/Territory	Italy
City	Vietri sul Mare
Period	14/05/14 → 15/05/14

Keywords

Artificial Intelligence
neural networks
evolutionary robotics
genetic algorithms

Access to Document

10.1007/978-3-319-12745-3_11

Wivace_correctionSubmitted manuscript, 362 KB

http://link.springer.com/chapter/10.1007/978-3-319-12745-3_11

consortium des équipements de calcul intensif
Champagne, B. (CoI)
1/01/11 → 31/12/22
Project: Research

High Performance Computing Technology Platform
Champagne, B. (Manager)
Technological Platform High Performance Computing
Facility/equipment: Technological Platform

Modélisation et apprentissage des réseaux de neurones artificiels
NICOLAY, D. (Author)Carletti, T. (Supervisor), 22 Jun 2012
Student thesis: Master types › Master in Mathematics

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Nicolay, D., Andrea, R., & Carletti, T. (2014). Learning Multiple Conflicting Tasks with Artificial Evolution. In C. Pizzuti, & G. Spezzano (Eds.), Advances in Artificial Life and Evolutionary Computation: 9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers (Vol. 445, pp. 127-139). (Communications in Computer and Information Science; Vol. 445). Springer. https://doi.org/10.1007/978-3-319-12745-3_11

Nicolay, Delphine ; Andrea, Roli ; Carletti, Timoteo. / Learning Multiple Conflicting Tasks with Artificial Evolution. Advances in Artificial Life and Evolutionary Computation: 9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers. editor / Clara Pizzuti ; Giandomenico Spezzano. Vol. 445 Springer, 2014. pp. 127-139 (Communications in Computer and Information Science).

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Nicolay, D, Andrea, R & Carletti, T 2014, Learning Multiple Conflicting Tasks with Artificial Evolution. in C Pizzuti & G Spezzano (eds), Advances in Artificial Life and Evolutionary Computation: 9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers. vol. 445, Communications in Computer and Information Science, vol. 445, Springer, pp. 127-139, 9th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2014, Vietri sul Mare, Italy, 14/05/14. https://doi.org/10.1007/978-3-319-12745-3_11

Learning Multiple Conflicting Tasks with Artificial Evolution. / Nicolay, Delphine; Andrea, Roli; Carletti, Timoteo.
Advances in Artificial Life and Evolutionary Computation: 9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers. ed. / Clara Pizzuti; Giandomenico Spezzano. Vol. 445 Springer, 2014. p. 127-139 (Communications in Computer and Information Science; Vol. 445).

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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N2 - The paper explores the issue of learning multiple competing tasks in the domain of artificial evolution. In particular, a robot is trained so as to be able to perform two different tasks at the same time, namely a gradient following and rough terrain avoidance behaviours. It is shown that, if the controller is trained to learn two tasks of different difficulty, then the robot performance is higher if the most difficult task is learnt first, before the combined learning of both tasks. An explanation to this superiority is also discussed, in comparison with previous results.

AB - The paper explores the issue of learning multiple competing tasks in the domain of artificial evolution. In particular, a robot is trained so as to be able to perform two different tasks at the same time, namely a gradient following and rough terrain avoidance behaviours. It is shown that, if the controller is trained to learn two tasks of different difficulty, then the robot performance is higher if the most difficult task is learnt first, before the combined learning of both tasks. An explanation to this superiority is also discussed, in comparison with previous results.

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KW - neural networks

KW - evolutionary robotics

KW - genetic algorithms

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BT - Advances in Artificial Life and Evolutionary Computation

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Nicolay D, Andrea R, Carletti T. Learning Multiple Conflicting Tasks with Artificial Evolution. In Pizzuti C, Spezzano G, editors, Advances in Artificial Life and Evolutionary Computation: 9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers. Vol. 445. Springer. 2014. p. 127-139. (Communications in Computer and Information Science). doi: 10.1007/978-3-319-12745-3_11

Learning Multiple Conflicting Tasks with Artificial Evolution

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Projects

consortium des équipements de calcul intensif

Equipment

High Performance Computing Technology Platform

Student theses

Modélisation et apprentissage des réseaux de neurones artificiels

Cite this