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  • 1.
    Anjomshoae, Sule
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Najjar, Amro
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Calvaresi, Davide
    Främling, Kary
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Explainable Agents and Robots: Results from a Systematic Literature Review2019In: Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems / [ed] N. Agmon, M. E. Taylor, E. Elkind, M. Veloso, International Foundation for Autonomous Agents and MultiAgent Systems , 2019, p. 1078-1088Conference paper (Refereed)
    Abstract [en]

    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

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