Modular Preference Moore Machines in News Mining Agents

Stefan Wermter and Garen Arevian
University of Sunderland
The Informatics Centre, SCET
St. Peter's Campus, St Peter's Way
Sunderland SR6 0DD, United Kingdom
www.his.sunderland.ac.uk

Abstract
This paper focuses on Hybrid Symbolic Neural Architectures 
that support the task of classifying textual 
information in learning agents. We give an
outline of these symbolic and neural preference
Moore machines. Furthermore, we demonstrate
how they can be used in the context of information
mining and news classification. Using the Reuters
newswire text data, we demonstrate how hybrid
symbolic and neural machines can provide an effective 
foundation for learning news agents.



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