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\begin{document}

\title{Explaining Autonomous Drones: An XAI Journey}



\author[1]{Mark Stefik}%
\author[1]{Michael Youngblood}%
\author[2]{Peter Pirolli}%
\author[3]{Christian Lebiere}%
\author[4]{Robert Thomson}%
\author[1]{Robert Price}%
\author[1]{Lester Nelson}%
\author[1]{Robert Krivacic}%
\author[1]{Jacob Le}%
\author[3]{Konstantinos Mitsopoulos}%
\author[3]{Sterling Somers}%
\author[2]{Joel Schooler}%
\affil[1]{Palo Alto Research Center Incorporated}%
\affil[2]{Florida Institute for Human and Machine Cognition}%
\affil[3]{Carnegie Mellon University}%
\affil[4]{West Point}%


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  \date{May 23, 2024}


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\begin{abstract}
COGLE (COmmon Ground Learning and Explanation) is an explainable
artificial intelligence (XAI) system for autonomous drones that deliver
supplies in mountainous areas to field units. The drone missions have
risks that vary with topography, flight decisions, and mission goals in
a simulated environment. Users must determine which AI-controlled drone
is better for a mission. Narrative explanations identify the advantages
of a drone's plan (``What?'') and reasons that the better drone is able
to do them (``Why?''). Visual explanations highlight risks from
obstacles that users may have overlooked (``Where?''). A model induction
user study showed that post-decision explanations produced a small
effect on the participants' abilities to identify the better of two
imperfect drones and their plans for a mission, but they did not teach
participants to judge the multiple success factors in complex missions
as well as the AI pilots. In a decision support variation of the task,
users would receive pre-decision explanations to help them to decide
when to trust the XAI's decision. In a fielded XAI application, every
drone available for a mission may lack some competencies. We created a
proof-of-concept demonstration of automatic ways to combine knowledge
from multiple imperfect AIs to get better solutions that the individual
AIs do not find on their own. This paper reports on the research
challenges, technical approach, and findings of the project and also
reflects on the multidisciplinary journey that we took.%
\end{abstract}%



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\verb`Explaining Autonomous Drones v4.docx`  available at \url{https://authorea.com/users/422029/articles/527828-explaining-autonomous-drones-an-xai-journey}

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