Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Admin (Ivan Eggel) for a login account.
 [BibTeX] [RIS]
Agent-Based Explanations in AI: Towards an Abstract Framework
Type of publication: Inproceedings
Citation:
Booktitle: Post-Proceedings o EXTRAAMAS 2020
Year: 2020
Publisher: Springer
Abstract: 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.
Keywords: explainability, explainable artificial intelligence, interpretability, Multi-Agent Systems, understandability, XAI, XMAS
Authors giovanni ciatto
Omicini, Andrea
Schumacher, Michael
Calvaresi, Davide
Added by: []
Total mark: 0
Attachments
  • ccso_extraamas2020.pdf
Notes
    Topics