1. Computable model discovery and high-level-programming approximations to algorithmic complexity.
- Author
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Lemus, Vladimir, Acuña-Yeomans, Eduardo, Zamora, Víctor, Hernández-Quiroz, Francisco, and Zenil, Hector
- Subjects
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RECURSIVE functions , *TURING machines , *PROGRAMMING languages , *INFORMATION theory , *INVERSE problems - Abstract
Motivated by algorithmic information theory, the problem of program discovery can help find candidates of underlying generative mechanisms of natural and artificial phenomena. The uncomputability of such inverse problem, however, significantly restricts a wider application of exhaustive methods. Here we present a proof of concept of an approach based on IMP, a high-level imperative programming language. Its main advantage is that conceptually complex computational routines are more succinctly expressed, unlike lower-level models such as Turing machines or cellular automata. We investigate if a more expressive higher-level programming language can be more efficient at generating approximations to algorithmic complexity of recursive functions. • We explore an inverse problem for model discovery based on principles of algorithmic information. • The method can help find candidates of underlying generative mechanisms and first principles. • A proof of concept based on IMP, a high-level imperative programming language, is provided. • IMP expressions are succinct, unlike lower-level models such as formal automata. • We investigate if a more expressive higher-level programming language can be more efficient. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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