Titlebook: Explainable and Transparent AI and Multi-Agent Systems; Third International Davide Calvaresi,Amro Najjar,Kary Fr?mling Conference proceedi

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Explainable AI (XAI) Models Applied to the Multi-agent Environment of Financial Marketsthrough a consistent features attribution. We apply this methodology to analyse in detail the March 2020 financial meltdown, for which the model offered a timely out of sample prediction. This analysis unveils in particular the contrarian predictive role of the tech equity sector before and after the crash.

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https://doi.org/10.1007/978-3-319-52308-8se require a means to understand why a schedule is reasonable and what would happen with different schedules. Building upon a recently proposed approach to argumentation-supported explainable scheduling, we present a tool, ., that provides interactive explanations in makespan scheduling easily and with clarity.

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Schedule Explainer: An Argumentation-Supported Tool for Interactive Explanations in Makespan Schedulse require a means to understand why a schedule is reasonable and what would happen with different schedules. Building upon a recently proposed approach to argumentation-supported explainable scheduling, we present a tool, ., that provides interactive explanations in makespan scheduling easily and with clarity.

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0302-9743 2021, which was held virtually due to the COVID-19 pandemic..The 19 long revised papers and 1 short contribution were carefully selected from 32 submissions. The papers are organized in the following topical sections: XAI & machine learning; XAI vision, understanding, deployment and evaluation; XAI

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https://doi.org/10.1007/978-3-030-82017-6artificial intelligence; autonomous agents; computer networks; computer programming; computer science; co

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https://doi.org/10.1007/978-1-4684-6066-7 the objects not important to those goals (distractor objects). Previous investigations have shown that the mechanisms of selective attention contribute to enhancing perception in both simple daily tasks and more complex activities requiring learning new information..Recently, it has been verified t

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