Kaspar Causally Explains: Causal Explanation in an Assistive Robot for Children with Autism Spectrum Disorder

The Kaspar robot has been used with great success to work as an education and social mediator with children with autism spectrum disorder. Enabling the robot to automatically generate causal explanations is considered a key to enrich the interaction scenarios for children and thereby promote additional trust in the robot. To this end, we present a formal theory of causal explanation to be embedded in Kaspar. Based on this theory, we build a causal model and an efficient causal analysis method to calculate causal explanations. We implement our method using Java with inputs provided by a human operator. This model automatically generates the causal explanation that are then spoken by Kaspar. We validate our explanations for user satisfaction in an empirical evaluation.