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Signal Passing Self-Assembly Simulates Tile Automata

Authors Angel A. Cantu, Austin Luchsinger, Robert Schweller, Tim Wylie

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Subject Classification

ACM Subject Classification
  • Theory of computation → Models of computation
  • Applied computing → Computational biology
Keywords
  • self-assembly
  • signal-passing tile assembly model
  • tile automata
  • cellular automata
  • simulation

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Abstract

The natural process of self-assembly has been studied through various abstract models due to the abundant applications that benefit from self-assembly. Many of these different models emerged in an effort to capture and understand the fundamental properties of different physical systems and the mechanisms by which assembly may occur. A newly proposed model, known as Tile Automata, offers an abstract toolkit to analyze and compare the algorithmic properties of different self-assembly systems. In this paper, we show that for every Tile Automata system, there exists a Signal-passing Tile Assembly system that can simulate it. Finally, we connect our result with a recent discovery showing that Tile Automata can simulate Amoebot programmable matter systems, thus showing that the Signal-passing Tile Assembly can simulate any Amoebot system.

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Angel A. Cantu, Austin Luchsinger, Robert Schweller, and Tim Wylie. Signal Passing Self-Assembly Simulates Tile Automata. In 31st International Symposium on Algorithms and Computation (ISAAC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 181, pp. 53:1-53:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.ISAAC.2020.53

Author Details

Angel A. Cantu
  • Department of Computer Science, University of Texas - Rio Grande Valley, TX, USA
Austin Luchsinger
  • Department of Electrical and Computer Engineering, University of Texas at Austin, TX, USA
Robert Schweller
  • Department of Computer Science, University of Texas - Rio Grande Valley, TX, USA
Tim Wylie
  • Department of Computer Science, University of Texas - Rio Grande Valley, TX, USA

Funding

This research was supported in part by National Science Foundation Grant CCF-1817602.

Supplementary Materials

References

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