Judgment Aggregation: A Primer
Synthesis Lectures on Artificial Intelligence and Machine Learning
Editors
Ronald J. Brachman, Yahoo! Labs
William W. Cohen, Carnegie Mellon University
Peter Stone, University of Texas at Austin
Judgment Aggregation: A Primer
Davide Grossi and Gabriella Pigozzi
2014
An Introduction to Constraint-Based Temporal Reasoning
Roman Bártak, Robert A. Morris, and K. Brent Venable
2014
Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms
Rina Dechter
2013
Introduction to Intelligent Systems in Traffic and Transportation
Ana L.C. Bazzan and Franziska Klügl
2013
A Concise Introduction to Models and Methods for Automated Planning
Hector Geffner and Blai Bonet
2013
Essential Principles for Autonomous Robotics
Henry Hexmoor
2013
Case-Based Reasoning: A Concise Introduction
Beatriz López
2013
Answer Set Solving in Practice
Martin Gebser, Roland Kaminski, Benjamin Kaufmann, and Torsten Schaub
2012
Planning with Markov Decision Processes: An AI Perspective
Mausam and Andrey Kolobov
2012
Active Learning
Burr Settles
2012
Computational Aspects of Cooperative Game Theory
Georgios Chalkiadakis, Edith Elkind, and Michael Wooldridge
2011
Representations and Techniques for 3D Object Recognition and Scene Interpretation
Derek Hoiem and Silvio Savarese
2011
A Short Introduction to Preferences: Between Artificial Intelligence and Social Choice
Francesca Rossi, Kristen Brent Venable, and Toby Walsh
2011
Human Computation
Edith Law and Luis von Ahn
2011
Trading Agents
Michael P. Wellman
2011
Visual Object Recognition
Kristen Grauman and Bastian Leibe
2011
Learning with Support Vector Machines
Colin Campbell and Yiming Ying
2011
Algorithms for Reinforcement Learning
Csaba Szepesvári
2010
Data Integration: The Relational Logic Approach
Michael Genesereth
2010
Markov Logic: An Interface Layer for Artificial Intelligence
Pedro Domingos and Daniel Lowd
2009
Introduction to Semi-Supervised Learning
XiaojinZhu and Andrew B.Goldberg
2009
Action Programming Languages
Michael Thielscher
2008
Representation Discovery using Harmonic Analysis
Sridhar Mahadevan
2008
Essentials of Game Theory: A Concise Multidisciplinary Introduction
Kevin Leyton-Brown and Yoav Shoham
2008
A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
Nikos Vlassis
2007
Intelligent Autonomous Robotics: A Robot Soccer Case Study
Peter Stone
2007
Copyright © 2014 by Morgan & Claypool
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher.
Judgment Aggregation: A Primer
Davide Grossi and Gabriella Pigozzi
www.morganclaypool.com
ISBN: 9781627050876 paperback
ISBN: 9781627050883 ebook
DOI 10.2200/S00559ED1V01Y201312AIM027
A Publication in the Morgan & Claypool Publishers series
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Lecture #27
Series Editors: Ronald J. Brachman, Yahoo! Labs
William W. Cohen, Carnegie Mellon University
Peter Stone, University of Texas at Austin
Series ISSN
Synthesis Lectures on Artificial Intelligence and Machine Learning
Print 1939-4608 Electronic 1939-4616
Judgment Aggregation: A Primer
Davide Grossi
University of Liverpool
Gabriella Pigozzi
Université Paris Dauphine
SYNTHESIS LECTURES ON ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING #27
ABSTRACT
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,