{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/explainable-agency-by-revealing-suboptimality","title":"Explainable Agency by Revealing Suboptimality in Child-Robot Learning Scenarios","arxiv_id":null,"date":"2020-11-06","proceeding":null,"authors":["Silvia Tulli","Marta Couto","Miguel Vasco","Elmira Yadollahi","Francisco Melo","Ana Paiva"],"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.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-62056-1_3","url_pdf":"https://doi.org/10.1007/978-3-030-62056-1_3","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"explainable-agency-by-revealing-suboptimality","repo_url":"https://github.com/Silviatulli/suboptimax","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"explanation-generation","task_name":"Explanation Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}