Contributions

  • Venable, Kristen Brent - Contributor
  • Walsh, Toby - Contributor

Publication

2011 - Morgan & Claypool, San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA), California

Language

English

Word Count

0 words, Guess

Page Count

0 pages

Physical Format

Electronic resource

Identifiers

  • Internet Archiveshortintroductio00ross
  • ISBN-139781608455874
  • ISBN-139781608455867
  • ISBN-101608455874
  • ISBN-101608455866
and 3 more
  • Better World Books9781608455867
  • Better World Books9781608455874
  • Open LibraryOL25552361M

Classifications

  • DDC006.3
  • LCCQ336 .R677 2011

Alternate Titles

  • Synthesis digital library of engineering and computer science.

Description

Computational social choice is an expanding field that merges classical topics like economics and voting theory with more modern topics like artificial intelligence, multiagent systems, and computational complexity. This book provides a concise introduction to the main research lines in this field, covering aspects such as preference modelling, uncertainty reasoning, social choice, stable matching, and computational aspects of preference aggregation and manipulation. The book is centered around the notion of preference reasoning, both in the single-agent and the multi-agent setting. It presents the main approaches to modeling and reasoning with preferences, with particular attention to two popular and powerful formalisms, soft constraints and CP-nets. The authors consider preference elicitation and various forms of uncertainty in soft constraints. They review the most relevant results in voting, with special attention to computational social choice. Finally, the book considers preferences in matching problems. The book is intended for students and researchers who may be interested in an introduction to preference reasoning and multi-agent preference aggregation, and who want to know the basic notions and results in computational social choice.

Subjects

Series Statement

  • Synthesis lectures on artificial intelligence and machine learning -- # 14

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