Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

The study and understanding of human behaviour is relevant to computer science, artificial
intelligence, neural computation, cognitive science, philosophy, psychology, and several …

In his review of neural binding problems, Feldman (Cogn Neurodyn 7: 1–11, 2013)
addressed two types of models as solutions of (novel) variable binding. The one type uses …

Working memory (WM) is a key component of human memory and cognition. Computational
models have been used to study the underlying neural mechanisms, but neglected the …

The paper presents the implementation of an intelligent decision support system (IDSS) to
solve a real manufacturing problem at JSC “Savushkin Product”. The proposed system is …

Among the problems of neural network design the challenge of explicit representing
conditional structural manipulations on a sub-symbolic level plays a critical role. In response …

Much has been promised about quantum computing accelerators, but few actual commercial
technologies have been made available so far. The D-Wave Computers Series constitutes …

Relational learning can be described as the task of learning first-order logic rules from
examples. It has enabled a number of new machine learning applications, eg graph mining …

This thesis moves towards reconciliation of two of the major paradigms of artificial
intelligence: by exploring the representation of symbolic logic in an artificial neural network …