The More the Merrier? Effects of Humanlike Learning Abilities on Humans’ Perception and Evaluation of a Robot

In this paper, we present three experimental studies in which subjects trained a robot to do a card game via reinforcement learning. In the first two studies participants interacted with the robot either without any learning ability (control group) or with one out of three versions of a learning algorithm implementing gradually aspects of more humanlike learning abilities. Results show that the implementation of a learning algorithm had positive effects regarding the evaluation of the robot, its learning abilities and the interaction. We found that more humanlike learning abilities do not always lead to better performance and evaluation and that results were to some extend influenced by longer or shorter interaction times. In a third study, we additionally explored the influence of other behavioral variations such as low or high verbal skills and interaction modalities in perceived intelligence of the robot irrespective of the implemented learning algorithm, but did not find significant effects. Results are discussed with regard to the socialness of future interaction scenarios.