Document Open Access Logo

Testing Dynamic Environments: Back to Basics

Authors Yonatan Nakar, Dana Ron

PDF
Thumbnail PDF

File

LIPIcs.ICALP.2021.98.pdf
  • Filesize: 0.93 MB
  • 20 pages

Document Identifiers

Author Details

Yonatan Nakar
  • Tel Aviv University, Israel
Dana Ron
  • Tel Aviv University, Israel

Cite AsGet BibTex

Yonatan Nakar and Dana Ron. Testing Dynamic Environments: Back to Basics. In 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 198, pp. 98:1-98:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.ICALP.2021.98

Abstract

We continue the line of work initiated by Goldreich and Ron (Journal of the ACM, 2017) on testing dynamic environments and propose to pursue a systematic study of the complexity of testing basic dynamic environments and local rules. As a first step, in this work we focus on dynamic environments that correspond to elementary cellular automata that evolve according to threshold rules. Our main result is the identification of a set of conditions on local rules, and a meta-algorithm that tests evolution according to local rules that satisfy the conditions. The meta-algorithm has query complexity poly(1/ε), is non-adaptive and has one-sided error. We show that all the threshold rules satisfy the set of conditions, and therefore are poly(1/ε)-testable. We believe that this is a rich area of research and suggest a variety of open problems and natural research directions that may extend and expand our results.

Subject Classification

ACM Subject Classification
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • Property Testing

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Related Versions

References

  1. D. Burraston and E. Edmonds. Cellular automata in generative electronic music and sonic art: a historical and technical review. Digital Creativity, 16(3):165-185, 2005. Google Scholar
  2. Bastien C. and Michel D. Cellular Automata Modelling of Physical Systems. Cambridge University Press, 1998. Google Scholar
  3. P. P. Chaudhuri, D. R. Chowdhury, S. Nandi, and S. Chattopadhyay. Additive Cellular Automata Theory and Applications. Vol. 1. IEEE Press, 1997. IEEE Press advances in circuits and systems series. Google Scholar
  4. M. Cook. Universality in elementary cellular automata. Complex systems, 15(1):1-40, 2004. Google Scholar
  5. O. Goldreich, S. Goldwasser, and D. Ron. Property testing and its connection to learning and approximation. Journal of the ACM, 45(4):653-750, 1998. Google Scholar
  6. O. Goldreich and D. Ron. On learning and testing dynamic environments. Journal of the ACM, 64(3):1-90, 2017. Google Scholar
  7. L. Kier, C. Cheng, and P. Seybold. Cellular automata models of chemical systems. SAR and QSAR in Environmental Research, 11(2):79-102, 2000. Google Scholar
  8. A. Mustafa, A. Heppenstall, H. Omrani, I. Saadi, M. Cools, and J. Teller. Modelling built-up expansion and densification with multinomial logistic regression, cellular automata and genetic algorithm. Computers, Environment and Urban Systems, 67:147-156, 2018. Google Scholar
  9. Y. Nakar and D. Ron. Testing dynamic environments: Back to basics. arXiv preprint, 2021. URL: http://arxiv.org/abs/2105.00759.
  10. D. Peleg. Local majorities, coalitions and monopolies in graphs: a review. Theoretical Computer Science, 282(2):231-257, 2002. Google Scholar
  11. D. A Rosenblueth and C. Gershenson. A model of city traffic based on elementary cellular automata. Complex Systems, 19(4):305, 2011. Google Scholar
  12. R. Rubinfeld and M. Sudan. Robust characterization of polynomials with applications to program testing. SIAM Journal on Computing, 25(2):252-271, 1996. Google Scholar
  13. J. von Neumann. The general and logical theory of automata. Cerebral Mechanisms of Behavior: The Hixon Symposium, pages 1-41, 1951. Google Scholar
  14. S. Wolfram. A new kind of science, volume 5. Wolfram media Champaign, IL, 2002. Google Scholar
  15. J. Xu, B. Gu, Y. Guo, J. Chang, Y. Ge, Y. Min, and X. Jin. A cellular automata model for population dynamics simulation of two plant species with different life strategies. In 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, 2010. Google Scholar
  16. A. N. Zehmakan. On the spread of information through graphs. PhD thesis, ETH Zurich, 2019. Google Scholar
  17. Z. Zheng, W. Huang, S. Li, and Y. Zeng. Forest fire spread simulating model using cellular automaton with extreme learning machine. Ecological Modelling, 348:33-43, 2017. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH Imprint Privacy Contact