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The Grossone Methodology Perspective on Turing Machines

  • Yaroslav D. Sergeyev
  • Alfredo Garro
Part of the Emergence, Complexity and Computation book series (ECC, volume 12)

Abstract

This chapter discusses how the mathematical language used to describe and to observe automatic computations influences the accuracy of the obtained results. The chapter presents results obtained by describing and observing different kinds ofTuringmachines (single andmulti-tape, deterministic and non-deterministic) through the lens of a new mathematical language named Grossone. This emerging language is strongly based on threemethodological ideas borrowed from Physics and applied toMathematics: the distinction between the object (indeedmathematical object) of an observation and the instrument used for this observation; interrelations holding between the object and the tool used for the observation; the accuracy of the observation determined by the tool. In the chapter, the new results are compared to those achievable by using traditional languages. It is shown that both languages do not contradict each other but observe and describe the same object (Turingmachines) but with different accuracies.

Keywords

Turing Machine Ultrametric Analysis Numeral System Physical Machine Computational Step 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yaroslav D. Sergeyev
    • 1
    • 2
    • 3
  • Alfredo Garro
    • 4
  1. 1.Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica (DIMES)Università della CalabriaRendeItaly
  2. 2.N.I. Lobatchevsky State UniversityNizhni NovgorodRussia
  3. 3.Istituto di Calcolo e Reti ad Alte Prestazioni, C.N.R.RendeItaly
  4. 4.Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica (DIMES)Universitá della CalabriaRendeItaly

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