Neural network models achieved impressive results in many areas of Artificial Intelligence: in image processing, natural language understanding, and reinforcement learning. However, many tasks solvable by rigorous symbolic methods, such as sequential decision-making, representation of conceptual knowledge, and modeling of reasoning, are solved unreliably or not solved at all by connectionist models. In addition, the symbol grounding problem, identified back in 1990 by Harnad, still dominates research topics in the field of Artificial Intelligence.
This research topic is intended to provide a showcase of the state of the art and new ideas in the field of neuro-symbolic integration in order to identify promising directions and notable advances in this field. Another goal is to put developed methods and algorithms in the general context of research on cognitive systems, models, and cognitive architects, to clarify the role and essential place of integrating approaches.