A neuroevolutionary approach to emergent task decomposition

Jekanthan Thangavelautham, Gabriele M.T. D'Eleuterio

Research output: Chapter in Book/Report/Conference proceedingChapter

11 Scopus citations

Abstract

A scalable architecture to facilitate emergent (self-organized) task decomposition using neural networks and evolutionary algorithms is presented. Various control system architectures are compared for a collective robotics (3 × 3 tiling pattern formation) task where emergent behaviours and effective task -decomposition techniques are necessary to solve the task. We show that bigger, more modular network architectures that exploit emergent task decomposition strategies can evolve faster and outperform comparably smaller non emergent neural networks for this task. Much like biological nervous systems, larger Emergent Task Decomposition Networks appear to evolve faster than comparable smaller networks. Unlike reinforcement learning techniques, only a global fitness function is specified, requiring limited supervision, and self-organized task decomposition is achieved through competition and specialization. The results are derived from computer simulations.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsXin Yao, John A. Bullinaria, Jonathan Rowe, Peter Tino, Ata Kaban, Edmund Burke, Jose A. Lozano, Jim Smith, Juan J. Merelo-Guervos, Hans-Paul Schwefel
PublisherSpringer-Verlag
Pages991-1000
Number of pages10
ISBN (Print)3540230920, 9783540230922
DOIs
StatePublished - 2004
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3242
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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