
Judgment Aggregation by Davide Grossi
Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation problems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This Synthesis Lecture aims at filling this gap presenting the key motivations, results, abstractions and techniques underpinning it. Table of Contents: Preface / Acknowledgments / Logic Meets Social Choice Theory / Basic Concepts /Impossibility / Coping with Impossibility / Manipulability / Aggregation Rules / Deliberation / Bibliography / Authors' Biographies / Index-
Graph Representation Learning
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A Concise Introduction to Models and Methods for Automated Planning
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Learning with Support Vector Machines
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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Reasoning with Probabilistic and Deterministic Graphical Models
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Action Programming Languages
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Human Computation
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Learning and Decision-Making from Rank Data
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Explainable Human-AI Interaction
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Network Embedding
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Predicting Human Decision-Making
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Game Theory for Data Science
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Strategic Voting
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Statistical Relational Artificial Intelligence
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A Short Introduction to Preferences
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Case-Based Reasoning
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Representing and Reasoning with Qualitative Preferences
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Robot Learning from Human Teachers
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Visual Object Recognition
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General Game Playing
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Essential Principles for Autonomous Robotics
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Federated Learning
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Graph-Based Semi-Supervised Learning
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An Introduction to Constraint-Based Temporal Reasoning
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Intelligent Autonomous Robotics
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Answer Set Solving in Practice
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Representation Discovery using Harmonic Analysis
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Representations and Techniques for 3D Object Recognition and Scene Interpretation
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Introduction to Intelligent Systems in Traffic and Transportation
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Introduction to Symbolic Plan and Goal Recognition
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Metric Learning
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Data Integration
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Trading Agents
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Introduction to Semi-Supervised Learning
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Transfer Learning for Multiagent Reinforcement Learning Systems
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Introduction to Graph Neural Networks
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Introduction to Logic Programming
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An Introduction to the Planning Domain Definition Language
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Lifelong Machine Learning, Second Edition
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Adversarial Machine Learning
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Multi-Objective Decision Making
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Active Learning
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Planning with Markov Decision Processes
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Computational Aspects of Cooperative Game Theory
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Algorithms for Reinforcement Learning
Davide Grossi is a Lecturer (Assistant Professor) at the Computer Science Department of the University of Liverpool (UK). He holds a master's degree cum laude in Philosophy from the Scuola Normale Superiore of Pisa (Italy) and a Ph.D. in Computer Science from Utrecht University (the Netherlands). His Ph.D. thesis was nominated for the Christiaan Huygens Prijs 2009 of the Royal Netherlands Academy of Arts and Sciences. Prior to joining the University of Liverpool, he worked as a postdoctoral researcher at the Computer Science and Communications Department of the University of Luxembourg (on a personal grant sponsored by the National Research Fund of Luxembourg) and at the Institute for Logic, Language, and Computation of the University of Amsterdam (on a personal grant sponsored by the Netherlands Organisation for Scientific Research). He is author of over 40 peer-reviewed articles in international journals and conferences in philosophy, logic, artificial intelligence and multi-agent systems. He regularly serves as reviewer for top journals and conferences in his areas of expertise.Gabriella Pigozzi is an Associate Professor in Computer Science at Universite Paris Dauphine (France) and a member of the LAMSADE Lab. After a Ph.D. in Philosophy from the University of Genova (Italy), she held postdoc positions at the Center for Junior Research Fellows of the University of Konstanz (Germany), at the Computer Science Department of King's College London (on a personal postdoctoral fellowship sponsored by the Economic and Social Research Council), and at the Computer Science and Communications Department of the University of Luxembourg. She obtained grants from the Engineering and Physical Sciences Research Council (UK) and the National Agency for Research (France). Her research focuses on judgment aggregation, computational social choice, argumentation theory, and normative multi-agent systems. She published over 40 peer-reviewed articles in international conferences and journals.
| SKU | Unavailable |
| ISBN 13 | 9783031004407 |
| ISBN 10 | 303100440X |
| Title | Judgment Aggregation |
| Author | Davide Grossi |
| Series | Synthesis Lectures On Artificial Intelligence And Machine Learning |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | Springer International Publishing AG |
| Year published | 2014-03-18 |
| Number of pages | 133 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |













































