@article { tulli20, abstract = {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.}, address = {Cham}, booktitle = {Social Robotics}, editor = {Wagner, Alan R. and Feil-Seifer, David and Haring, Kerstin S. and Rossi, Silvia and Williams, Thomas and He, Hongsheng and Sam Ge, Shuzhi}, isbn = {978-3-030-62056-1}, journal = {Lecture Notes in Computer Science book series}, keywords = {Explainable agency; Decision-making; Explainable HRI ;Social Robotic Companions;}, month = {November}, pages = {23-35}, publisher = {International Conference on Social Robotics}, title = {Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios}, volume = {12483}, year = {2020}, author = {Silvia Tulli} }