TY  - CONF
T1  - Expectation: Personalized Explainable ArtificialIntelligence for Decentralized Agents withHeterogeneous Knowledge
A1  - Calvaresi, Davide
A1  - giovanni ciatto
A1  - Najjar, Amro
A1  - Aydoğan, Reyhan
A1  - der Torre, Leon Van
A1  - Omicini, Andrea
A1  - Schumacher, Michael
TI  - Proceedings of the 3rd International Workshop on Explainable and Transparent AI and Multi-Agent Systems (EXTRAAMAS 2021)
Y1  - 2021
KW  - Chist-Era IV
KW  - Decentralisation
KW  - Expectation
KW  - eXplanable AI
KW  - Multi-Agent Systems
KW  - Personalisation
N2  - 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 centralized, 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 Expectationproject, focusing on the theoretical and practical advancement of the state of the art of XAI towards the construction of personalized explanations in spite of decentralization and heterogeneity of knowledge, agents, and explainees (both humans or virtual).To tackle the challenges posed by personalization, decentralization, and heterogeneity, the project fruitfully combines abstractions, methods, and approaches from the multi-agent systems, knowledge extraction/injection, negotiation, argumentation, and symbolic reasoning communities.
ER  -