Preference Moore Machines for Neural Fuzzy Integration

This paper describes multidimensional neural preference classes and preference Moore machines as a principle for integrating different neural and/or symbolic knowledge sources. We relate neural preferences to multidimensional fuzzy set representations. Furthermore, we introduce neural preference Moore machines and relate traditional symbolic transducers with simple recurrent networks by using neural preference Moore machines. Finally, we demonstrate how the concepts of preference classes and preference Moore machines can be used to integrate knowledge from different neural and/or symbolic machines. We argue that our new concepts for preference Moore machines contribute a new potential approach towards general principles of neural symbolic integration.