Agent-Based Explanations in AI: Towards an Abstract Framework


Giovanni Ciatto, Michael I. Schumacher, Andrea Omicini, Davide Calvaresi

Davide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling (eds.)
Explainable, Transparent Autonomous Agents and Multi-Agent Systems, pages 3-20
Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence) 12175
Springer, Cham
2020

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 that Multi-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-based MAS, reconciling notions and results from the literature.

(keywords) Explainable artificial intelligence; Multi-agent systems; Understandability; Explainability; Interpretability 

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  • 2nd International Workshop on EXplainable TRansparent Autonomous Agents and Multi-Agent Systems (EXTRAAMAS 2020) — 09/05/2020

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Publication

— authors

Giovanni Ciatto, Michael I. Schumacher, Andrea Omicini, Davide Calvaresi

— editors

Davide Calvaresi, Amro Najjar, Michael Winikoff, Kary Främling

— status

published

— sort

paper in proceedings

— publication date

2020

— volume

Explainable, Transparent Autonomous Agents and Multi-Agent Systems

— series

Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence

— volume

12175

— pages

3-20

— number of pages

18

URLs

original page  |  original PDF

identifiers

— DOI

10.1007/978-3-030-51924-7_1

— DBLP

conf/atal/CiattoSOC20

— IRIS

11585/765952

— Scopus

2-s2.0-85088588789

— WoS / ISI

000695272300001

— print ISBN

978-3-030-51923-0

— online ISBN

978-3-030-51924-7

notes

— note

Second International Workshop, EXTRAAMAS 2020, Auckland, New Zealand, May 9–13, 2020, Revised Selected Papers

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