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.
Course material (slides) - final version:
Part I (pdf, 1.7MB): intro and Link to NETtalk
Part II (pdf, 800KB): propositional representation
Part III (pdf, 500KB): propositional applications
Part IV (pdf, 1.5MB): propositional extraction
Part V (pdf, 2.4MB): first-order case
Part VI (pdf, 400KB): wrap-up and Caledonian Crow movie