Skip to main content

Spatial Complexity Measure for Characterising Cellular Automata Generated 2D Patterns

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9273))

Included in the following conference series:

Abstract

Cellular automata (CA) are known for their capacity to generate complex patterns through the local interaction of rules. Often the generated patterns, especially with multi-state two-dimensional CA, can exhibit interesting emergent behaviour. This paper addresses quantitative evaluation of spatial characteristics of CA generated patterns. It is suggested that the structural characteristics of two-dimensional (2D) CA patterns can be measured using mean information gain. This information-theoretic quantity, also known as conditional entropy, takes into account conditional and joint probabilities of cell states in a 2D plane. The effectiveness of the measure is shown in a series of experiments for multi-state 2D patterns generated by CA. The results of the experiments show that the measure is capable of distinguishing the structural characteristics including symmetry and randomness of 2D CA patterns.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Andrienko, Y.A., Brilliantov, N.V., Kurths, J.: Complexity of two-dimensional patterns. Eur. Phys. J. B 15(3), 539–546 (2000)

    Article  Google Scholar 

  2. Bates, J.E., Shepard, H.K.: Measuring complexity using information fluctuation. Physics Letters A 172(6), 416–425 (1993)

    Article  Google Scholar 

  3. Brown, P.: Stepping stones in the mist. In: Creative Evolutionary Systems, pp. 387–407. Morgan Kaufmann Publishers Inc. (2001)

    Google Scholar 

  4. Cover, T.M., Thomas, J.A.: Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing). Wiley-Interscience (2006)

    Google Scholar 

  5. Frazer, J.: An evolutionary architecture. Architectural Association Publications, Themes VII (1995)

    Google Scholar 

  6. Javaheri Javid, M.A., Al-Rifaie, M.M., Zimmer, R.: Detecting symmetry in cellular automata generated patterns using swarm intelligence. In: Dediu, A.-H., Lozano, M., Martín-Vide, C. (eds.) TPNC 2014. LNCS, vol. 8890, pp. 83–94. Springer, Heidelberg (2014)

    Google Scholar 

  7. Javaheri Javid, M.A., te Boekhorst, R.: Cell dormancy in cellular automata. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006. LNCS, vol. 3993, pp. 367–374. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Langton, C.G.: Studying artificial life with cellular automata. Physica D: Nonlinear Phenomena 22(1), 120–149 (1986)

    Article  MathSciNet  Google Scholar 

  9. Miranda, E.: Composing Music with Computers. No. 1 in Composing Music with Computers. Focal Press (2001)

    Google Scholar 

  10. Scha, I.R.: Kunstmatige Kunst. De Commectie 2(1), 4–7 (2006)

    Google Scholar 

  11. Schwartz, L., Schwartz, L.: The Computer Artist’s Handbook: Concepts, Techniques, and Applications. W W Norton & Company Incorporated (1992)

    Google Scholar 

  12. Shannon, C.: A mathematical theory of communication. The Bell System Technical Journal 27, 379–423, 623–656 (1948)

    Google Scholar 

  13. Wackerbauer, R., Witt, A., Atmanspacher, H., Kurths, J., Scheingraber, H.: A comparative classification of complexity measures. Chaos, Solitons & Fractals 4(1), 133–173 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  14. Wolfram, S.: Statistical mechanics of cellular automata. Reviews of Modern Physics 55(3), 601–644 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  15. Wolfram, S.: Universality and complexity in cellular automata. Physica D: Nonlinear Phenomena 10(1), 1–35 (1984)

    Article  MathSciNet  Google Scholar 

  16. Wolfram, S.: A New Kind of Science. Wolfram Media Inc. (2002)

    Google Scholar 

  17. Xenakis, I.: Formalized music: thought and mathematics in composition. Pendragon Press (1992)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohammad Ali Javaheri Javid .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Javaheri Javid, M.A., Blackwell, T., Zimmer, R., Al-Rifaie, M.M. (2015). Spatial Complexity Measure for Characterising Cellular Automata Generated 2D Patterns . In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-23485-4_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23484-7

  • Online ISBN: 978-3-319-23485-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics