SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation

13 citations recorded by the source corpus.

How researchers see this paper

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Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots (2018, background): “In previous work, explanations have been categorised as either explaining 1) machine learning as in [11] who showed that they can increase trust; 2) explaining plans [2, 13]; 3) verbalising robot [12] or agent rationalisation [3].”
A demonstration of multimodal debrief generation for AUVs, post-mission and in-mission (2016, background): “Previous work has looked at enabling the mission plan to be more scrutable and less opaque [3].”
Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models (2018, background): “…models for developers, as in (Ribeiro et al., 2016) who showed that such explanations can increase trust; 2) explanations of planning systems (Tintarev and Kutlak, 2014; Chakraborti et al., 2017); and 3) verbalis-ing robot (Rosenthal et al., 2016) or agent (Harrison et al., 2017)…”
Trust triggers for multimodal command and control interfaces (2017, background): “Previousworkspecificallylookingattransparencyofautonomous systems investigates enabling the mission plan to be more scrutable and less opaque [15].”