Towards XMAS: eXplainability through Multi-Agent Systems


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Giovanni Ciatto, Roberta Calegari, Andrea Omicini, Davide Calvaresi

Claudio Savaglio, Giancarlo Fortino, Giovanni Ciatto, Andrea Omicini (eds.)
“AI&IoT 2019 – Artificial Intelligence and Internet of Things 2019”, pages 40-53
CEUR Workshop Proceedings 2502
Sun SITE Central Europe, RWTH Aachen University
November 2019

In the context of the Internet of Things (IoT), intelligent systems (IS) are increasingly relying on Machine Learning (ML) techniques.
Given the opaqueness of most ML techniques, however, humans have to rely on their intuition to fully understand the IS outcomes: helping them is the target of eXplainable Artificial Intelligence (XAI).
Current solutions – mostly too specific, and simply aimed at making ML easier to interpret – cannot satisfy the needs of IoT, characterised by heterogeneous stimuli, devices, and data-types concurring in the composition of complex information structures.
Moreover, Multi-Agent Systems (MAS) achievements and advancements are most often ignored, even when they could bring about key features like explainability and trustworthiness.

Accordingly, in this paper we (i) elicit and discuss the most significant issues affecting modern IS, and (ii) devise the main elements and related interconnections paving the way towards reconciling interpretable and explainable IS using MAS.

(keywords) MAS; XMAS; XAI; explainability; road map

Journals & Series

Events

  • 1st Workshop "AI & IoT" @ AI*IA 2019 (AI&IoT 2019) — 22/11/2019

Publication

— authors

— editors

Claudio Savaglio, Giancarlo Fortino, Giovanni Ciatto, Andrea Omicini

— status

published

— sort

paper in proceedings

— publication date

November 2019

— volume

AI&IoT 2019 – Artificial Intelligence and Internet of Things 2019

— series

CEUR Workshop Proceedings

— volume

2502

— pages

40-53

URLs

original PDF

identifiers

— DBLP

conf/aiia/CiattoCOC19

— IRIS

11585/707345

— Scholar

16440456382689731727

— Scopus

2-s2.0-85075953890

— print ISSN

1613-0073

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