Résumé
Humans are increasingly relying on complex systems that heavily
adopts Artificial Intelligence (AI) techniques. Such systems are
employed in a growing number of domains, and making them
explainable is an impelling priority. Recently, the domain of eXplainable
Artificial Intelligence (XAI) emerged with the aims of
fostering transparency and trustworthiness. Several reviews have
been conducted. Nevertheless, most of them deal with data-driven
XAI to overcome the opaqueness of black-box algorithms. Contributions
addressing goal-driven XAI (e.g., explainable agency for
robots and agents) are still missing. This paper aims at filling this
gap, proposing a Systematic Literature Review. The main findings
are (i) a considerable portion of the papers propose conceptual
studies, or lack evaluations or tackle relatively simple scenarios;
(ii) almost all of the studied papers deal with robots/agents explaining
their behaviors to the human users, and very few works
addressed inter-robot (inter-agent) explainability. Finally, (iii) while
providing explanations to non-expert users has been outlined as a
necessity, only a few works addressed the issues of personalization
and context-awareness.