An Ansatz for Computational Undecidability in RNA Automata.
Automata
RNA automata
Turing’s oracle
novelty generation
self-reference
undecidability
Journal
Artificial life
ISSN: 1530-9185
Titre abrégé: Artif Life
Pays: United States
ID NLM: 9433814
Informations de publication
Date de publication:
01 05 2023
01 05 2023
Historique:
medline:
16
6
2023
pubmed:
6
8
2022
entrez:
5
8
2022
Statut:
ppublish
Résumé
In this ansatz we consider theoretical constructions of RNA polymers into automata, a form of computational structure. The bases for transitions in our automata are plausible RNA enzymes that may perform ligation or cleavage. Limited to these operations, we construct RNA automata of increasing complexity; from the Finite Automaton (RNA-FA) to the Turing machine equivalent 2-stack PDA (RNA-2PDA) and the universal RNA-UPDA. For each automaton we show how the enzymatic reactions match the logical operations of the RNA automaton. A critical theme of the ansatz is the self-reference in RNA automata configurations that exploits the program-data duality but results in computational undecidability. We describe how computational undecidability is exemplified in the self-referential Liar paradox that places a boundary on a logical system, and by construction, any RNA automata. We argue that an expansion of the evolutionary space for RNA-2PDA automata can be interpreted as a hierarchical resolution of computational undecidability by a meta-system (akin to Turing's oracle), in a continual process analogous to Turing's ordinal logics and Post's extensible recursively generated logics. On this basis, we put forward the hypothesis that the resolution of undecidable configurations in RNA automata represent a novelty generation mechanism and propose avenues for future investigation of biological automata.
Identifiants
pubmed: 35929772
pii: 112511
doi: 10.1162/artl_a_00370
doi:
Substances chimiques
RNA
63231-63-0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
261-288Informations de copyright
© 2023 Massachusetts Institute of Technology.