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

Besold, T. R. and Kuhnberger, K-U. (2015). Towards integrated neural-symbolic systems for human-level AI: Two research programs helping to bridge the gaps. Biologically Inspired Cognitive Architectures, 14, pp. 97-110. doi: 10.1016/j.bica.2015.09.003


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.

Publication Type: Article
Additional Information: © 2015, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Research program, Neural–symbolic integration, Complexity theory, Cognitive architectures, Agent architectures
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
Date Deposited: 12 Dec 2017 11:33
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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