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.
Einzelheiten
Titel
Agent-Based Explanations in AI : towards an abstract framework
Autor(en)/ in(nen)
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))
Datum
2020-05
Veröffentlich in
Proceedings of the EXTRAAMAS: International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent Systems 2020
Verlag
Auckland, New Zealand, 9-13 May 2020
Umfang
Pp. 3-20
Vorgestellt auf
Second International Workshop, EXTRAAMAS 2020, Auckland, New Zealand, 2020-05-09, 2020-05-13
Fussnote
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.