Explainable Agents and Robots: Results from a Systematic Literature Review

By S. Anjomshoae, A. Najjar, Davide Calvaresi, Kary Främling. Adaptive Agents and Multi-Agent Systems, 2019

383 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health Informatics, Safety Research

View Paper: https://www.semanticscholar.org/paper/b89594e658b233f9c057f0f79e68738d0c107458

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Citations in the corpus

A comprehensive taxonomy for explainable artificial intelligence: a systematic survey of surveys on methods and concepts (2021, background): “Application type interactive machine learning [8,14,6]”
Benchmarking and survey of explanation methods for black box models (2021, background): “…literature reviews related to explainability are focused not just on ML and AI but also on social studies (Miller 2019; Byrne 2019), recommendation systems (Zhang and Chen 2020), model-agents(Anjomshoae et al. 2019), and domain-specific applications such as health and medicine (Tjoa and Guan 2019).”
Evaluating XAI: A comparison of rule-based and example-based explanations (2021, background): “The systematic literature review by [5] shows that 97% of the 62 reviewed articles underline that explanations serve a user need but 41% did not evaluate their explanations with such users.”
A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021, background): “As a result, the need to motivate automatic decisions with a clear explanation of why the algorithm outputs a particular decision has made the XAI research ﬁeld grow quickly [5].”
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities (2021, background): “Some considerations should be taken into account to ensure an accurate reception [67].”
Explanations in Autonomous Driving: A Survey (2021, background): “[20] provided a systematic literature review generally on explainable agencies (i.”
A Review of Taxonomies of Explainable Artificial Intelligence (XAI) Methods (2022, background): “[7] on explainability of robots and human-robot interaction, Abdul et al.”
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, neutral): “rve as stand-alone ML algorithms, but also as building blocks for many IML approaches. 3 Today IML has reached a rst state of readiness. Research-wise, the eld is maturing in terms of methods surveys [75,41,120,96,1,6,23,15], further consolidation of terms and knowledge [42,22,82,97,88,17], and work”