Visualizing computation in large-scale cellular automata

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F20%3A00347772" target="_blank" >RIV/68407700:21730/20:00347772 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1162/isal_a_00277" target="_blank" >https://doi.org/10.1162/isal_a_00277</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1162/isal_a_00277" target="_blank" >10.1162/isal_a_00277</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Visualizing computation in large-scale cellular automata

Popis výsledku v původním jazyce

Emergent processes in complex systems such as cellular automata can perform computations of increasing complexity, and could possibly lead to artificial evolution. Such a feat would require scaling up current simulation sizes to allow for enough computational capacity. Understanding complex computations happening in cellular automata and other systems capable of emergence poses many challenges, especially in large-scale systems. We propose methods for coarse-graining cellular automata based on frequency analysis of cell states, clustering and autoencoders. These innovative techniques facilitate the discovery of large-scale structure formation and complexity analysis in those systems. They emphasize interesting behaviors in elementary cellular automata while filtering out background patterns. Moreover, our methods reduce large 2D automata to smaller sizes and enable identifying systems that behave interestingly at multiple scales.

Název v anglickém jazyce

Visualizing computation in large-scale cellular automata

Popis výsledku anglicky

Emergent processes in complex systems such as cellular automata can perform computations of increasing complexity, and could possibly lead to artificial evolution. Such a feat would require scaling up current simulation sizes to allow for enough computational capacity. Understanding complex computations happening in cellular automata and other systems capable of emergence poses many challenges, especially in large-scale systems. We propose methods for coarse-graining cellular automata based on frequency analysis of cell states, clustering and autoencoders. These innovative techniques facilitate the discovery of large-scale structure formation and complexity analysis in those systems. They emphasize interesting behaviors in elementary cellular automata while filtering out background patterns. Moreover, our methods reduce large 2D automata to smaller sizes and enable identifying systems that behave interestingly at multiple scales.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ALIFE 2020: The 2020 Conference on Artificial Life

ISBN

—

ISSN

1064-5462

e-ISSN

1530-9185

Počet stran výsledku

9

Strana od-do

239-247

Název nakladatele

University of Vermon

Místo vydání

Vermon

Místo konání akce

virtual

Datum konání akce

13. 7. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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