Personalized causal explanations of a robot's behavior

José Galeas¹Suna Bensch^2*Thomas Hellström²Antonio Bandera¹

• ¹University of Malaga, Málaga, Spain
• ²Umea Universitet, Umeå, Sweden

The deployment of robots in environments shared with humans implies that they must be able to justify or explain their behavior to non-expert users when the user, or the situation itself, requires it. We propose a framework for robots to generate personalized explanations of their behavior by integrating cause-and-effect, social roles, and natural language queries. Robot events are stored as cause–effect pairs in a causal log. Given a human natural language query, the system uses machine learning to identify the matching cause-and-effect entry in the causal log and to identify the social role of the inquirer. An initial explanation is generated and is then further refined by a large language model to produce linguistically diverse responses tailored to the social role and the query. This approach maintains causal and factual accuracy, while providing language variation in the generated explanations. Qualitative as well as quantitative experiments show that combining the causal information with the social role and the query when generating the explanations yields the most appreciated explanations.