EXPECTATION: Personalized explainable artificial intelligence for decentralized agents with heterogeneous knowledge

Name: EXPECTATION: Personalized explainable artificial intelligence for decentralized agents with heterogeneous knowledge
Author: Calvaresi, D., Ciatto, G., Najjar, A., Aydoğan, Reyhan, Van der Torre, L., Omicini, A., Schumacher, M.

İsim	EXPECTATION: Personalized explainable artificial intelligence for decentralized agents with heterogeneous knowledge
Yazar	Calvaresi, D., Ciatto, G., Najjar, A., Aydoğan, Reyhan, Van der Torre, L., Omicini, A., Schumacher, M.
Basım Tarihi:	2021
Basım Yeri	- Springer
Konu	Chist-Era IV, Decentralisation, Expectation, eXplanable AI, Multi-agent systems, Personalisation
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-303082016-9
Kayıt Numarası	e042e6e7-0328-4eca-a4da-a898eef164e4
Lokasyon	Computer Science
Tarih	2021
Notlar	Swiss National Science Foundation (SNSF) ; Ministry of Education, Universities and Research (MIUR) ; Luxembourg National Research Fund ; TÜBİTAK
Örnek Metin	Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis. However, explanation techniques are still embryotic, and they mainly target ML experts rather than heterogeneous end-users. Furthermore, existing solutions assume data to be centralised, homogeneous, and fully/continuously accessible—circumstances seldom found altogether in practice. Arguably, a system-wide perspective is currently missing. The project named “Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge ” (Expectation) aims at overcoming such limitations. This manuscript presents the overall objectives and approach of the Expectation project, focusing on the theoretical and practical advance of the state of the art of XAI towards the construction of personalised explanations in spite of decentralisation and heterogeneity of knowledge, agents, and explainees (both humans or virtual). To tackle the challenges posed by personalisation, decentralisation, and heterogeneity, the project fruitfully combines abstractions, methods, and approaches from the multi-agent systems, knowledge extraction/injection, negotiation, argumentation, and symbolic reasoning communities.
DOI	10.1007/978-3-030-82017-6_20
Cilt	12688 LNAI

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EXPECTATION: Personalized explainable artificial intelligence for decentralized agents with heterogeneous knowledge

Yazar Calvaresi, D., Ciatto, G., Najjar, A., Aydoğan, Reyhan, Van der Torre, L., Omicini, A., Schumacher, M.

Basım Tarihi 2021

Basım Yeri - Springer

Konu Chist-Era IV, Decentralisation, Expectation, eXplanable AI, Multi-agent systems, Personalisation

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-303082016-9

Kayıt Numarası e042e6e7-0328-4eca-a4da-a898eef164e4

Lokasyon Computer Science

Tarih 2021

Notlar Swiss National Science Foundation (SNSF) ; Ministry of Education, Universities and Research (MIUR) ; Luxembourg National Research Fund ; TÜBİTAK

Örnek Metin Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis. However, explanation techniques are still embryotic, and they mainly target ML experts rather than heterogeneous end-users. Furthermore, existing solutions assume data to be centralised, homogeneous, and fully/continuously accessible—circumstances seldom found altogether in practice. Arguably, a system-wide perspective is currently missing. The project named “Personalized Explainable Artificial Intelligence for Decentralized Agents with Heterogeneous Knowledge ” (Expectation) aims at overcoming such limitations. This manuscript presents the overall objectives and approach of the Expectation project, focusing on the theoretical and practical advance of the state of the art of XAI towards the construction of personalised explanations in spite of decentralisation and heterogeneity of knowledge, agents, and explainees (both humans or virtual). To tackle the challenges posed by personalisation, decentralisation, and heterogeneity, the project fruitfully combines abstractions, methods, and approaches from the multi-agent systems, knowledge extraction/injection, negotiation, argumentation, and symbolic reasoning communities.

DOI 10.1007/978-3-030-82017-6_20

Cilt 12688 LNAI