Description

Intelligent systems based on logical knowledge processing on the one hand,
and on artificial neural networks (also called connectionist systems) on
the other, differ substantially. They are both standard approaches to
artificial intelligence and it would be very desirable to combine the
robust neural networking machinery with symbolic knowledge representation
and reasoning paradigms like logic programming in such a way that the
strengths of either paradigm will be retained. Current research, however,
fails by far to achieve this goal, in particular for non-propositional
logics.

We will present past and present achievements in neural-symbolic
integration. Starting from successful achievements for propositional
logic, we will work towards discussing state-of-the-art research on the
integration of first-order logic programming and connectionism, based on 
recent research publications by the organizors. The course shall emphasize
the importance of neural-symbolic integration and stimulate research in
this direction.