An adaptive decision-making system supported on user preference predictions for human–robot interactive communication
User Modeling and User-adapted Interaction

Adapting to dynamic environments is essential for artificial agents,
especially those aiming to communicate with people interactively. In
this context, a social robot that adapts its behaviour to different
users and proactively suggests their favourite activities may produce a
more successful interaction. In this work, we describe how the
autonomous decision-making system embedded in our social robot Mini can
produce a personalised interactive communication experience by
considering the preferences of the user the robot interacts with. We
compared the performance of Top Label as Class and Ranking by Pairwise
Comparison, two promising algorithms in the area, to find the one that
best predicts the user preferences. Although both algorithms provide
robust results in preference prediction, we decided to integrate Ranking
by Pairwise Comparison since it provides better estimations. The method
proposed in this contribution allows the autonomous decision-making
system of the robot to work on different modes, balancing activity
exploration with the selection of the favourite entertaining activities.
The operation of the preference learning system is shown in three real
case studies where the decision-making system works differently
depending on the user the robot is facing. Then, we conducted a
human–robot interaction experiment to investigate whether the robot
users perceive the personalised selection of activities more appropriate
than selecting the activities at random. The results show how the study
participants found the personalised activity selection more
appropriate, improving their likeability towards the robot and how
intelligent they perceive the system. query Please check the edit made
in the article title.