Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Subsymbolic natural language processing: an integrated model of scripts, lexicon, and memory
Subsymbolic natural language processing: an integrated model of scripts, lexicon, and memory
Hybrid Intelligent Systems
Computational Architectures Integrating Neural and Symbolic Processes: A Perspective on the State of the Art
Artificial Intelligence and Neural Networks; Steps toward Principled Integration
Artificial Intelligence and Neural Networks; Steps toward Principled Integration
A theory of grammatical induction in the connectionist paradigm
A theory of grammatical induction in the connectionist paradigm
Hybrid preference machines based on inspiration from neuroscience
Cognitive Systems Research
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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.