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Behavior models for learning and receptionist dialogs

We present a dialog model for identifying persons, learning person names, and associated face IDs in a receptionist dialog. The proposed model allows a decomposition of the main dialog task into separate dialog behaviors which can be implemented separately and allow a mixture of handcrafted models and dialog strategies trained with reinforcement learning. The dialog model was implemented on our robot and tested in a number of experiments in a receptionist task. A Wizard-of-Oz experiment is used to evaluate the dialog structure, delivers information for the definition of metrics, and delivers a data corpus which is used to train a user simulation and component error model. Using these models we train a dialog module for learning a person’s name with reinforcement learning.