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Advice from Robots: Would You Choose a Robot that Looked More or Less Human?
LocationGrand Salon I
DescriptionHumans are increasingly turning to non-human agents for advice. Therefore, it is important to investigate if human-likeness of a robot affects advice-seeking. In this experiment, participants chose robot advisors with different levels of human-likeness when completing either social or analytical tasks, and the task was either known or unknown when the robot advisor was selected. In the agent-first condition, participants chose the advisor before receiving their task assignment, and in the task-first condition participants re-ceived their task assignment before choosing the advisor. Results indicated that task type did not play a role in agent selection in either condition. However, in the agent-first condition, more human-like robots (Nao and Kodomoroid) were selected at a higher rate than machine-like robots (Cozmo) and, in the task-first condition, Nao was selected at a higher rate than Cozmo or Kodomoroid. These results should be con-sidered when designing robots for giving advice to improve human-robot interaction.