Dialogue based management of user feedback in an autonomous preference learning system

Abstract

We present an enhanced method for user feedback in an autonomous learning system that includes a spoken dialogue system to manage the interactions between the users and the system. By means of a rule-based natural language understanding module and a state-based dialogue manager we allow the users to update the preferences learnt by the system from the data obtained from different sensors. The design of the dialogue together with the storage of context information (the previous dialogue turns and the current state of the dialogue) ensures highly natural interactions, reducing the number of dialogue turns and making it possible to use complex linguistic constructions instead of isolated commands.