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Olga Grjasnowa liest aus "JULI, AUGUST, SEPTEMBER
04.02.2025 um 19:30 Uhr
Artificial Intelligence And International Politics
von Valerie M Hudson
Verlag: Taylor & Francis
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ISBN: 978-0-429-71890-8
Erschienen am 30.08.2019
Sprache: Englisch
Umfang: 422 Seiten

Preis: 54,99 €

Klappentext
Inhaltsverzeichnis

This book focuses on the early stirrings of interest in artificial intelligence as a potentially useful method of political analysis. It examines three general areas of artificial intelligence: international interaction, decision making groups, and cognitive processes in international politics.



0. Introduction 1. Artificial Intelligence and International Relations: An Overview Part One: Conceptual Issues and Practical Concerns 2. Artificial Intelligence and Intuitive Foreign Policy Decision-Makers Viewed as Limited Information Processors: Some Conceptual Issues and Practical Concerns for the Future 3. Steps Toward Artificial Intelligence: Rule-Based, Case-Based, and Explanation-Based Models of Politics 4. Text Modeling for International Politics: A Tourist's Guide to RELATUS 5. Reasoning and Intelligibility 6. The Computational Modeling of Strategic Time Part Two: AI/IR Research: International Events and Foreign Policy Decision Making 7. Pattern Recognition of International Event Sequences: A Machine Learning Approach 8. Scripting International Power Dramas: A Model of Situational Predisposition 9. UNCLESAM: The Application of a Rule-Based Model of U.S. Foreign Policy Making 10. Modeling Foreign Policy Decision Making as Knowledge-Based Reasoning 11. Decision Making and Development: A "Glass Box" Approach to Representation Part Three: AI/IR Research: The Discourse of Foreign Policy 12. The Expertise of the Senate Foreign Relations Committee 13. Reproduction of Perception and Decision in the Early Cold War 14. Theoretical Categories and Data Construction in Computational Models of Foreign Policy 15. Semantic Content Analysis: A New Methodology for the RELATUS Natural Language Environment 16. Time Space: Representing Historical Time for Efficient Event Retrieval


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