Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable and explainable. Unfortunately, researchers are struggling to converge towards an unambiguous definition of notions such as interpretation, or, explanation—which are often (and mistakenly) used interchangeably. Furthermore, despite the sound metaphors that Multi-Agent System (MAS) could easily provide to address such a challenge, and agent-oriented perspective on the topic is still missing. Thus, this paper proposes an abstract and formal framework for XAI-based MAS, reconciling notions, and results from the literature.
Détails
Titre
Agent-Based Explanations in AI : towards an abstract framework
Auteur(s)/ trice(s)
Ciatto, Giovanni (University of Bologna, Cesena, Italy) Schumacher, Michael (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) Omicini, Andrea (University of Bologna, Cesena, Italy) Calvaresi, Davide (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))
Date
2020-05
Publié dans
Proceedings of the EXTRAAMAS: International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems 2020
Publié par
Auckland, New Zealand, 9-13 May 2020
Pagination
Pp. 3-20
Présenté à
Second International Workshop, EXTRAAMAS 2020, Auckland, New Zealand, 2020-05-09, 2020-05-13
Note
Due to the COVID-19 outbreak, the EXTRAAMAS: International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systemsvenue in Auckland was cancelled. The proceedings of the online conference are however published according to the original schedule.