Online adaptation of dialog strategies based on probabilistic planning

In this paper, a dialog modeling approach for long-term interaction between a service robot and a single user is presented, which enables a user-adaptive interaction behavior of the robot. Central element of the dialog system is a probabilistic model of the user's reactions to the robot's behavior, which is learned online and used for a probabilistic planning process based on message passing in a dynamic factor graph. The suggested approach has been applied to implement a complex application on a mobile service robot, which has been tested in a 10 day evaluation study with 16 users in order to get a feedback on usability of the interaction design, adaptation skills, and feasibility of a rapid application development. Results and findings of that study are presented here briefly.