Approximations of algorithmic and structural complexity validate cognitive-behavioral experimental results

Being able to objectively characterize the intrinsic complexity of behavioral patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems. Yet complexity is difficult in practice, particularly when strings are s...

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Vydané v:	Frontiers in computational neuroscience Ročník 16; s. 956074
Hlavní autori:	Zenil, Hector, Marshall, James A. R., Tegnér, Jesper
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland Frontiers Research Foundation 24.01.2023 Frontiers Media S.A
Predmet:	Algorithms Animal behavior Animal cognition ant behavior Approximation Artificial intelligence behavioral biases Behavioral sciences behavioral sequences Cognitive ability communication complexity Decision making Entropy Environmental effects Flight Information theory Navigation behavior Neuroscience Prey Probability Shannon Entropy tradeoffs of complexity measures Shannon Entropy ant behavior behavioral sequences communication complexity tradeoffs of complexity measures behavioral biases
ISSN:	1662-5188, 1662-5188
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