Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures

Should the internal structure of a system matter when it comes to autonomy? While there is still no consensus on a rigorous, quantifiable definition of autonomy, multiple candidate measures and related quantities have been proposed across various disciplines, including graph-theory, information-theo...

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Autor principal:	Larissa Albantakis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	agency artificial evolution causation integrated information intelligence Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/e29cc273cf0c4bb5a6aa212746a43fcf
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id	oai:doaj.org-article:e29cc273cf0c4bb5a6aa212746a43fcf
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spelling	oai:doaj.org-article:e29cc273cf0c4bb5a6aa212746a43fcf2021-11-25T17:29:29ZQuantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures10.3390/e231114151099-4300https://doaj.org/article/e29cc273cf0c4bb5a6aa212746a43fcf2021-10-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1415https://doaj.org/toc/1099-4300Should the internal structure of a system matter when it comes to autonomy? While there is still no consensus on a rigorous, quantifiable definition of autonomy, multiple candidate measures and related quantities have been proposed across various disciplines, including graph-theory, information-theory, and complex system science. Here, I review and compare a range of measures related to autonomy and intelligent behavior. To that end, I analyzed the structural, information-theoretical, causal, and dynamical properties of simple artificial agents evolved to solve a spatial navigation task, with or without a need for associative memory. By contrast to standard artificial neural networks with fixed architectures and node functions, here, independent evolution simulations produced successful agents with diverse neural architectures and functions. This makes it possible to distinguish quantities that characterize task demands and input-output behavior, from those that capture intrinsic differences between substrates, which may help to determine more stringent requisites for autonomous behavior and the means to measure it.Larissa AlbantakisMDPI AGarticleagencyartificial evolutioncausationintegrated informationintelligenceScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1415, p 1415 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	agency artificial evolution causation integrated information intelligence Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	agency artificial evolution causation integrated information intelligence Science Q Astrophysics QB460-466 Physics QC1-999 Larissa Albantakis Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
description	Should the internal structure of a system matter when it comes to autonomy? While there is still no consensus on a rigorous, quantifiable definition of autonomy, multiple candidate measures and related quantities have been proposed across various disciplines, including graph-theory, information-theory, and complex system science. Here, I review and compare a range of measures related to autonomy and intelligent behavior. To that end, I analyzed the structural, information-theoretical, causal, and dynamical properties of simple artificial agents evolved to solve a spatial navigation task, with or without a need for associative memory. By contrast to standard artificial neural networks with fixed architectures and node functions, here, independent evolution simulations produced successful agents with diverse neural architectures and functions. This makes it possible to distinguish quantities that characterize task demands and input-output behavior, from those that capture intrinsic differences between substrates, which may help to determine more stringent requisites for autonomous behavior and the means to measure it.
format	article
author	Larissa Albantakis
author_facet	Larissa Albantakis
author_sort	Larissa Albantakis
title	Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
title_short	Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
title_full	Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
title_fullStr	Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
title_full_unstemmed	Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures
title_sort	quantifying the autonomy of structurally diverse automata: a comparison of candidate measures
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e29cc273cf0c4bb5a6aa212746a43fcf
work_keys_str_mv	AT larissaalbantakis quantifyingtheautonomyofstructurallydiverseautomataacomparisonofcandidatemeasures
_version_	1718412318434394112

Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures

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