In this paper, we present an approach for the development of a statistical dialog manager, in which the system response is selected by means of a classification process which considers all the previous history of the dialog to select the next system response. In particular, we use decision trees for its implementation. The statistical model is automatically learned from training data which are labeled in terms of different SLU features. This methodology has been applied to develop a dialog manager within the framework of the European LUNA project, whose main goal is the creation of a robust natural spoken language understanding system. We present an evaluation of this approach for both human machine and human-human conversations acquired in this project. We demonstrate that a statistical dialog manager developed with the proposed technique and learned from a corpus of human-machine dialogs can successfully infer the task-related topics present in spontaneous humanhuman dialogs.
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