TY  - CONF
T1  - Agent-Based Explanations in AI: Towards an Abstract Framework
A1  - giovanni ciatto
A1  - Omicini, Andrea
A1  - Schumacher, Michael
A1  - Calvaresi, Davide
TI  - Post-Proceedings o EXTRAAMAS 2020
Y1  - 2020
PB  - Springer
KW  - explainability
KW  - explainable artificial intelligence
KW  - interpretability
KW  - Multi-Agent Systems
KW  - understandability
KW  - XAI
KW  - XMAS
N2  - Recently, the eXplainable AI (XAI) research community has focused on developing methods making Machine Learning (ML) predictors more interpretable or 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, in spite of the sound metaphors thatMulti-Agent System (MAS) could easily provide to address such a challenge, an agent-oriented perspective on the topic is still missing. Thus, this paper proposes an abstract and formal framework for  XAI-basedMAS, reconciling notions and results from the literature.
ER  -