Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models

Francisco Javier Chiyah Garcia, David A. Robb, Xingkun Liu, Atanas Laskov, Pedro Patron, Helen Hastie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)
41 Downloads (Pure)

Abstract

As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert ‘speak aloud’ and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.
Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Natural Language Generation
PublisherAssociation for Computational Linguistics
Pages99-108
Number of pages10
ISBN (Electronic)9781948087865
Publication statusPublished - Nov 2018
Event11th International Conference of Natural Language Generation 2018 - Tilburg University, Tilburg, Netherlands
Duration: 5 Nov 20168 Nov 2018
https://inlg2018.uvt.nl/

Conference

Conference11th International Conference of Natural Language Generation 2018
Abbreviated titleINLG'18
CountryNetherlands
CityTilburg
Period5/11/168/11/18
Internet address

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