Neural fuzzy preference integration using neural preference Moore machines.
This paper describes preference classes and preference Moore machines as a basis for integrating different hybrid neural representations. Preference classes are shown to provide a basic link between neural preferences and fuzzy representations at the preference class level. Preference Moore machines provide a link between recurrent neural networks and symbolic transducers at the preference Moore machine level. We demonstrate how the concepts of preference classes and preference Moore machines can be used to interpret neural network representations and to integrate knowledge from hybrid neural representations. One main contribution of this paper is the introduction and analysis of neural preference Moore machines and their link to a fuzzy interpretation. Furthermore, we illustrate the interpretation and combination of various neural preference Moore machines with additional real-world examples.
Wermter
S
S
University of Sunderland, Informatics Centre, SCET, United Kingdom. stefan.wermter@sunderland.ac.uk
D003661:Decision Support Techniques; D017143:Fuzzy Logic; D008433:Mathematics; D016571:Neural Networks, Computer