Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios

6 Nov 2020  ·  Silvia Tulli, Marta Couto, Miguel Vasco, Elmira Yadollahi, Francisco Melo, Ana Paiva ·

Revealing the internal workings of a robot can help a human better understand the robot’s behaviors. How to reveal such workings, e.g., via explanation generation, remains a significant challenge. This gets even more complex when these explanations are targeted towards children. Therefore, we propose a search-based approach to generate contrastive explanations using optimal and sub-optimal plans and implement it in a scenario for children. In the application scenario, the child and the robot learn together how to play a zero-sum game that requires logical and mathematical thinking. We report results around our explanation generation system that was successfully deployed among seven-year-old children. Our results show trends that the generated explanations were able to positively affect the children’s perceived difficulty in learning the zero-sum game.

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