Advertisement

Consolidation of Results

  • Azlan IqbalEmail author
  • Matej Guid
  • Simon Colton
  • Jana Krivec
  • Shazril Azman
  • Boshra Haghighi
Chapter
Part of the SpringerBriefs in Cognitive Computation book series (BRIEFSCC, volume 3)

Abstract

With reference to the experimental work in Chap.  4, we summarize the main findings in point form for the benefit of a wider readership. Notably, the fact that processing power and memory do not seem to have a significant effect on composing efficiency using the Digital Synaptic Neural Substrate (DSNS) approach, and how the quality of the compositions is higher compared to a random piece placement approach. In addition, the DSNS happens to be better than the present state-of-the-art ‘experience table’ approach that is also better at composing than simple random piece placement. Furthermore, in certain cases, variations in the number of photographs or chess games used to seed the DSNS process may be significant. Similarly, so do variations in the number of attributes used that represent the fragments of information taken from the aforemention domains. Possible limitations of the DSNS are discussed, including a brief exploration of its potential applications in other domains and fields.

Keywords

Results Findings DSNS Efficiency Limitations Applications Domains Fields 

Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Azlan Iqbal
    • 1
    Email author
  • Matej Guid
    • 2
  • Simon Colton
    • 3
  • Jana Krivec
    • 4
  • Shazril Azman
    • 5
  • Boshra Haghighi
    • 5
  1. 1.College of Information TechnologyUniversiti Tenaga NasionalKajangMalaysia
  2. 2.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  3. 3.Department of Computing of Goldsmiths CollegeUniversity of LondonLondonUnited Kingdom
  4. 4.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  5. 5.College of Graduate StudiesUniversiti Tenaga NasionalKajangMalaysia

Personalised recommendations