Modeling Decisions for Artificial Intelligence

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Bol The papers discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: Decision making and uncertainty; This book constitutes the refereed proceedings of the 20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023, held in Umeå, Sweden, during June19–22,2023. The 17 papers presented in this volume were carefully reviewed and selected from 28 submissions. Additionally, 1 invited paper were included. The papers discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: Decision making and uncertainty; Machine Learning and data science; and Data privacy.

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The papers discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: Decision making and uncertainty; This book constitutes the refereed proceedings of the 20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023, held in Umeå, Sweden, during June19–22,2023. The 17 papers presented in this volume were carefully reviewed and selected from 28 submissions. Additionally, 1 invited paper were included. The papers discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: Decision making and uncertainty; Machine Learning and data science; and Data privacy.

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Pages: 285, Edition: 1st ed. 2023, Paperback, Springer


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  • 9783031334979

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