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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/11/1415 |
| _version_ | 1827676696704188416 |
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| author | Larissa Albantakis |
| author_facet | Larissa Albantakis |
| author_sort | Larissa Albantakis |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:31:57Z |
| format | Article |
| id | doaj.art-e29cc273cf0c4bb5a6aa212746a43fcf |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T05:31:57Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-e29cc273cf0c4bb5a6aa212746a43fcf2023-11-22T23:14:38ZengMDPI AGEntropy1099-43002021-10-012311141510.3390/e23111415Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate MeasuresLarissa Albantakis0Department of Psychiatry, University of Wisconsin–Madison, Madison, WI 53719, USAShould 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.https://www.mdpi.com/1099-4300/23/11/1415agencyartificial evolutioncausationintegrated informationintelligence |
| spellingShingle | Larissa Albantakis Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures Entropy agency artificial evolution causation integrated information intelligence |
| title | 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_short | 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 |
| topic | agency artificial evolution causation integrated information intelligence |
| url | https://www.mdpi.com/1099-4300/23/11/1415 |
| work_keys_str_mv | AT larissaalbantakis quantifyingtheautonomyofstructurallydiverseautomataacomparisonofcandidatemeasures |