Learning Multiple Conflicting Tasks with Artificial Evolution

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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 languageEnglish
Title of host publicationAdvances in Artificial Life and Evolutionary Computation
Subtitle of host publication9th Italian Workshop, WIVACE 2014, Vietri sul Mare, Italy, May 14-15, Revised Selected Papers
EditorsClara Pizzuti, Giandomenico Spezzano
Number of pages13
ISBN (Electronic)978-3-319-12745-3
ISBN (Print)978-3-319-12744-6
Publication statusPublished - 6 Nov 2014
Event9th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2014 - Vietri sul Mare, Italy
Duration: 14 May 201415 May 2014

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)18650929


Conference9th Italian Workshop on Artificial Life and Evolutionary Computation, WIVACE 2014
CityVietri sul Mare


  • Artificial Intelligence
  • neural networks
  • evolutionary robotics
  • genetic algorithms

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