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Marcos Maroto-Gómez1, Álvaro Castro-González1, José Carlos Castillo1

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Social robots can learn user preferences to suggest activities, enhancing human-robot interaction. This study shows personalized suggestions improve user experience and robot likeability.

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Area of Science:

  • Artificial Intelligence
  • Human-Robot Interaction
  • Robotics

Background:

  • Adapting to dynamic environments is crucial for artificial agents, particularly social robots interacting with humans.
  • Personalized interactions, where robots suggest preferred activities, can lead to more successful engagement.

Purpose of the Study:

  • To develop and evaluate an autonomous decision-making system for the social robot Mini to provide personalized interactive communication.
  • To compare the effectiveness of 'Top Label as Class' and 'Ranking by Pairwise Comparison' algorithms for predicting user preferences.

Main Methods:

  • Implemented an autonomous decision-making system in the social robot Mini, incorporating a preference learning system.
  • Compared 'Top Label as Class' and 'Ranking by Pairwise Comparison' algorithms for user preference prediction.
  • Conducted real-world case studies and a human-robot interaction experiment to assess personalized activity selection.

Main Results:

  • Both algorithms showed robust preference prediction, but 'Ranking by Pairwise Comparison' provided better estimations.
  • The system demonstrated adaptability in different modes, balancing activity exploration and favorite selection.
  • Human-robot interaction experiment participants found personalized activity selection more appropriate than random selection.

Conclusions:

  • The Ranking by Pairwise Comparison algorithm effectively enhances personalized human-robot interaction.
  • Personalized activity suggestions by social robots improve user perception, likeability, and perceived intelligence.
  • The developed system enables social robots to adapt behavior for more successful and engaging interactions.