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

Abstract

Abstract 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.