Towards integrated neural-symbolic systems for human-level AI: Two research programs helping to bridge the gaps

Autor(en): Besold, Tarek R.
Kuehnberger, Kai-Uwe 
Stichwörter: Agent architectures; ALGORITHM; Cognitive architectures; Complexity theory; Computer Science; Computer Science, Artificial Intelligence; NETWORKS; Neural-symbolic integration; Neurosciences; Neurosciences & Neurology; Research program; RULES
Erscheinungsdatum: 2015
Herausgeber: ELSEVIER
Journal: BIOLOGICALLY INSPIRED COGNITIVE ARCHITECTURES
Volumen: 14
Startseite: 97
Seitenende: 110
Zusammenfassung: 
After a human-level AI-oriented overview of the status quo in neural symbolic integration, two research programs aiming at overcoming long-standing challenges in the field are suggested to the community: The first program targets a better understanding of foundational differences and relationships on the level of computational complexity between symbolic and subsymbolic computation and representation, potentially providing explanations for the empirical differences between the paradigms in application scenarios and a foothold for subsequent attempts at overcoming these. The second program suggests a new approach and computational architecture for the cognitively-inspired anchoring of an agent's learning, knowledge formation, and higher reasoning abilities in real-world interactions through a closed neural-symbolic acting/sensing processing reasoning cycle, potentially providing new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems and facilitating a deeper understanding of the development and interaction in human-technological settings. (C) 2015 Elsevier B.V. All rights reserved.
ISSN: 2212683X
DOI: 10.1016/j.bica.2015.09.003

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