Abstract
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 key to enrich the interaction scenarios for children and promote trust in the robot. We present a theory of causal explanation to be embedded in Kaspar. Based on this theory, we build a causal model and an analysis method to calculate causal explanations. We implement our method in 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.
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Notes
- 1.
There’s a slight abuse of notation since \(w'\) might not be the same size as \(W'\).
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Acknowledgements
This work has been supported by the UKRI TAS Hub, Grant Award Reference EP/V00784X/1 and UKRI TAS Node in Verifiability, Grant Award Reference EP/V026801/2.
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Araujo, H. et al. (2022). Kaspar Causally Explains. In: Cavallo, F., et al. Social Robotics. ICSR 2022. Lecture Notes in Computer Science(), vol 13818. Springer, Cham. https://doi.org/10.1007/978-3-031-24670-8_9
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DOI: https://doi.org/10.1007/978-3-031-24670-8_9
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