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Learning Multiple Conflicting Tasks with Artificial Evolution

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Advances in Artificial Life and Evolutionary Computation (WIVACE 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 445))

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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.

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Notes

  1. 1.

    Let us observe that such parameters have been set to the present values after a careful analysis of some generic simulations and by a test and error procedure.

  2. 2.

    More precisely, one network composed by 9 inputs, 5 hidden neurons and 4 outputs, has only the inputs able to measure the local heights and the second one, formed by 8 inputs, 5 hidden neurons and 4 outputs, to detect the dangerous zones.

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Acknowledgements

This research used computational resources of the “Plateforme Technologique de Calcul Intensif (PTCI)” located at the University of Namur, Belgium, which is supported by the F.R.S.-FNRS.

This paper presents research results of the Belgian Network DYSCO (Dynamical Systems, Control, and Optimisation), funded by the Interuniversity Attraction Poles Programme, initiated by the Belgian State, Science Policy Office. The scientific responsibility rests with its authors.

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Authors and Affiliations

  1. Department of Mathematics and NaXys, University of Namur, Namur, Belgium

    Delphine Nicolay & Timoteo Carletti

  2. Department of Computer Science and Engineering (DISI), University of Bologna, Campus of Cesena, Bologna, Italy

    Andrea Roli

Authors
  1. Delphine Nicolay
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  2. Andrea Roli
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  3. Timoteo Carletti
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Corresponding author

Correspondence to Delphine Nicolay .

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Editors and Affiliations

  1. CNR-ICAR, Rende, Italy

    Clara Pizzuti

  2. CNR-ICAR, Rende, Italy

    Giandomenico Spezzano

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Nicolay, D., Roli, A., Carletti, T. (2014). Learning Multiple Conflicting Tasks with Artificial Evolution. In: Pizzuti, C., Spezzano, G. (eds) Advances in Artificial Life and Evolutionary Computation. WIVACE 2014. Communications in Computer and Information Science, vol 445. Springer, Cham. https://doi.org/10.1007/978-3-319-12745-3_11

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  • DOI: https://doi.org/10.1007/978-3-319-12745-3_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12744-6

  • Online ISBN: 978-3-319-12745-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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