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

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Cecilia L. Ridgeway,Sandra Nakagawalainable AI (XAI) research community has focused on making Machine Learning (ML) and Deep Learning methods . and ., seeking .. This work is a preliminary study on the applicability of Neural Architecture Search (NAS) (a sub-field of DL looking for automatic design of NN structures) in XAI. We propos

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Jan E. Stets,Jonathan H. Turner, and distributed allocation problems. Such problems have been studied for decades, and various solutions have been proposed. However, even the most straightforward resource allocation mechanisms lead to debates on efficiency vs. fairness, business quality vs. passenger’s user experience, or perform

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Vapor-Liquid Critical Constants of Fluids,trees (GBDT) approach to predict large S&P 500 price drops from a set of 150 technical, fundamental and macroeconomic features. We report an improved accuracy of GBDT over other machine learning (ML) methods on the S&P 500 futures prices. We show that retaining fewer and carefully selected features

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https://doi.org/10.1007/978-3-319-22041-3most people are unfamiliar with how AIs make their decisions and many of them feel anxious about AI decision-making. A result of this is that AI methods suffer from trust issues and this hinders the full-scale adoption of them. In this paper we determine what the main application domains of Reinforc