  The study and understanding of human behaviour is relevant to computer
science, artificial intelligence, neural computation, cognitive science,
philosophy, psychology, and several other areas. Presupposing cognition as
basis of behaviour, among the most prominent tools in the modelling of
behaviour are computational-logic systems, connectionist models of cognition,
and models of uncertainty. Recent studies in cognitive science, artificial
intelligence, and psychology have produced a number of cognitive models of
reasoning, learning, and language that are underpinned by computation. In
addition, efforts in computer science research have led to the development of
cognitive computational systems integrating machine learning and automated
reasoning. Such systems have shown promise in a range of applications,
including computational biology, fault diagnosis, training and assessment in
simulators, and software verification. This joint survey reviews the personal
ideas and views of several researchers on neural-symbolic learning and
reasoning. The article is organised in three parts: Firstly, we frame the scope
and goals of neural-symbolic computation and have a look at the theoretical
foundations. We then proceed to describe the realisations of neural-symbolic
computation, systems, and applications. Finally we present the challenges
facing the area and avenues for further research.
