A dialog management methodology based on evolving Fuzzy-rule-based (FRB) classifiers

This paper proposes a statistical methodology based on evolving Fuzzy-rule-based (FRB) classifiers to develop dialog managers for spoken dialog systems. The dialog managers developed by means of our proposal select the next system action by considering a set of dynamic rules that are automatically obtained by means of the application of the FRB classification process. Our approach has the main advantage of taking into account the data supplied by the user throughout the complete dialog history without causing scalability problems, also considering confidence measures provided by the recognition and understanding modules. The use of EFS allows to process streaming data on-line in real time, thus dynamically evolving the structure and operation of the dialog model based on the interaction of the dialog system with its users. We also describe the application of our proposal for the eClass0 classifier and a codification of the different information sources to facilitate the correct operation of this classification function.