Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020: Proceedings of the 11th Annual Meeting of the BICA Society: Advances in Intelligent Systems and Computing, cartea 1310
Editat de Alexei V. Samsonovich, Ricardo R. Gudwin, Alexandre da Silva Simõesen Limba Engleză Paperback – 9 dec 2020
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Specificații
ISBN-13: 9783030655952
ISBN-10: 3030655954
Ilustrații: XIX, 613 p. 209 illus., 130 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.88 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Advances in Intelligent Systems and Computing
Locul publicării:Cham, Switzerland
ISBN-10: 3030655954
Ilustrații: XIX, 613 p. 209 illus., 130 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.88 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seria Advances in Intelligent Systems and Computing
Locul publicării:Cham, Switzerland
Textul de pe ultima copertă
The book focuses on original approaches intended to support the development of biologically inspired cognitive architectures. It bridges together different disciplines, from classical artificial intelligence to linguistics, from neuro- and social sciences to design and creativity, among others. The chapters, based on contributions presented at the Eleventh Annual Meeting of the BICA Society, held on November 10-14, 2020, in Natal, Brazil, discuss emerging methods, theories and ideas towards the realization of general-purpose humanlike artificial intelligence or fostering a better understanding of the ways the human mind works. All in all, the book provides engineers, mathematicians, psychologists, computer scientists and other experts with a timely snapshot of recent research and a source of inspiration for future developments in the broadly intended areas of artificial intelligence and biological inspiration.
Caracteristici
Reports on biologically inspired approaches to replicate human mind function Bridges together artificial intelligence, cognitive, neuro- and social sciences Addresses the topic of “biological inspiration" broadly, highlighting its application to cognitive robotics and machine learning