Skip to main content

Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy

  • Chapter
  • First Online:
New Trends in Business Information Systems and Technology

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 294))


Many computer models have been developed and successfully applied. However, in some cases, these models might be restrictive on the possible solutions or their solutions might be difficult to interpret. To overcome this problem, we outline a new approach, the so-called allagmatic method, that automatically programs and executes models with as little limitations as possible while maintaining human interpretability. Earlier we described a metamodel and its building blocks according to the philosophical concepts of structure and operation. They are entity, milieu, and update function that together abstractly describe a computer model. By automatically combining these building blocks in an evolutionary computation, interpretability might be increased by the relationship to the metamodel, and models might be translated into more interpretable models via the metamodel. We propose generic and object-oriented programming to implement the entities and their milieus as dynamic and generic arrays and the update function as a method. We show two experiments where a simple cellular automaton and an artificial neural network are automatically programmed, compiled, and executed. A target state is successfully reached in both cases. We conclude that the allagmatic method can create and execute cellular automaton and artificial neural network models in an automated manner with the guidance of philosophy.

Both authors contributed equally to this work.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Similar content being viewed by others


  1. Wolfram, S.: Cellular automata as models of complexity. Nature 311, 419–424 (1984).

  2. Ding, R., Guo, Z.X.: Microstructural modelling of dynamic recrystallisation using an extended cellular automaton approach. Comput. Mater. Sci. 23(1–4), 209–218 (2002).

  3. Jiao, Y., Torquato, S.: Emergent behaviors from a cellular automaton model for invasive tumor growth in heterogeneous microenvironments. PLOS Comput. Biol. 7(12) (2011).

  4. Keller, F., Christen, P.: Bone as a complex system: computing bone remodelling across biological hierarchies. In: Proceedings of the 8th World Congress of Biomechanics, O1389 (2018)

    Google Scholar 

  5. Ohs, N., Keller, F., Blank, O., Lee, Y.-W.W., Cheng, C.-Y.J., Arbenz, P., Müller, R., Christen, P.: Towards in silico prognosis using big data. Curr. Dir. Biomed. Eng. 2(1), 57–60 (2016).

  6. Christen, P.: Moving beyond the genome with computer modeling. Pers. Med. 15(3), 145–148 (2018).

  7. Hooft, G.T.: The Cellular Automaton Interpretation of Quantum Mechanics. Springer, Cham Heidelberg New York Dordrecht London (2016)

    Google Scholar 

  8. Chen, X., Li, H.Y., Miao, J.R., Jiang, S.X., Jiang, X.: A multiagent-based model for pedestrian simulation in subway stations. Simul. Model. Pract. Theory 71, 134–148 (2017).

  9. Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

  10. Carleo, G., Troyer, M.: Solving the quantum many-body problem with artificial neural networks. Science 355, 602–605 (2017).

  11. Bezerra, L.C.T., Lopez-Ibanez, M., Stutzle, T.: Automatic component-wise design of multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 20(3), 403–417 (2016).

  12. Bidlo, M.: On routine evolution of complex cellular automata. IEEE Trans. Evol. Comput. 20(5), 742–754 (2016).

  13. Christen, P., Del Fabbro, O.: Cybernetical concepts for cellular automaton and artificial neural network modelling and implementation. 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 4124–4130 (2019). arXiv:2001:02037

  14. Wiener, N.: Cybernetics: Or Control and Communication in the Animal and the Machine. The MIT Press, Cambridge (1961)

    MATH  Google Scholar 

  15. Ashby, W.R.: An Introduction to Cybernetics. Chapman & Hall, London (1956)

    Book  Google Scholar 

  16. Simondon, G.: L’individuation à la lumière des notions de forme et d’information. Editions Jérôme Millon, Grenoble (2013)

    Google Scholar 

  17. Simondon, G.: On the Mode of Existence of Technical Objects. University of Minnesota Press, Minneapolis London (2017)

    Google Scholar 

  18. Cormen, T.H., Leiserson, C.E., Rivest, R.L., Stein, C.: Introduction to Algorithms. The MIT Press, Cambridge London (2009)

    MATH  Google Scholar 

  19. Ilachinski, A.: Cellular Automata: A Discrete Universe. World Scientific, Singapore (2001)

    Book  Google Scholar 

  20. Mainzer, K., Chua, L.: The Universe as Automaton: From Simplicity and Symmetry to Complexity. Springer, Heidelberg Dordrecht London New York (2012)

    Book  Google Scholar 

  21. Wolfram, S.: A New Kind of Science. Wolfram Media, Champaign (2002)

    MATH  Google Scholar 

  22. Kaufmann, S.A.: The Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, New York (1993)

    Google Scholar 

  23. Cook, M.: Universality in elementary cellular automata. Complex Syst. 15(1), 1–40 (2004)

    MathSciNet  MATH  Google Scholar 

  24. Russel, S.J., Norvig, P.: Artificial Intelligence: A modern Approach. Prentice Hall, Upper Saddle River (2010)

    Google Scholar 

Download references


This work was supported by the Hasler Foundation under Grant 18067.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Patrik Christen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Christen, P., Del Fabbro, O. (2021). Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy. In: Dornberger, R. (eds) New Trends in Business Information Systems and Technology. Studies in Systems, Decision and Control, vol 294. Springer, Cham.

Download citation

Publish with us

Policies and ethics