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Probabilistic reasoning in multiagent systems : a graphical models approach / Yang Xiang.

By: Material type: TextTextPublication details: Cambridge ; New York : Cambridge University Press, 2002.Description: 1 online resource (xii, 294 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 0511020740
  • 9780511020742
  • 0511045441
  • 9780511045448
  • 9780521813082
  • 0521813085
  • 0511148127
  • 9780511148125
  • 9780511546938
  • 0511546939
  • 1280434031
  • 9781280434037
  • 1107133157
  • 9781107133150
  • 9786610434039
  • 6610434034
  • 0511177720
  • 9780511177729
  • 0511305168
  • 9780511305160
Subject(s): Genre/Form: Additional physical formats: Print version:: Probabilistic reasoning in multiagent systems.DDC classification:
  • 006.3 21
LOC classification:
  • Q337 .X53 2002eb
Online resources:
Contents:
Cover; Half-title; Title; Copyright; Contents; Preface; 1 Introduction; 2 Bayesian Networks; 3 Belief Updating and Cluster Graphs; 4 Junction Tree Representation; 5 Belief Updating with Junction Trees; 6 Multiply Sectioned Bayesian Networks; 7 Linked Junction Forests; 8 Distributed Multiagent Inference; 9 Model Construction and Verification; 10 Looking into the Future; Bibliography; Index.
Summary: This book identifies the technical challenges in building intelligent agents that can cooperate on complex tasks in an uncertain environment and provides a rigorous framework for meeting these challenges. It is the first book that addresses the subject of probabilistic inference by multiple agents using graphical knowledge representations.
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBSCO Computers Available
Total holds: 0

Includes bibliographical references (pages 287-291) and index.

Cover; Half-title; Title; Copyright; Contents; Preface; 1 Introduction; 2 Bayesian Networks; 3 Belief Updating and Cluster Graphs; 4 Junction Tree Representation; 5 Belief Updating with Junction Trees; 6 Multiply Sectioned Bayesian Networks; 7 Linked Junction Forests; 8 Distributed Multiagent Inference; 9 Model Construction and Verification; 10 Looking into the Future; Bibliography; Index.

This book identifies the technical challenges in building intelligent agents that can cooperate on complex tasks in an uncertain environment and provides a rigorous framework for meeting these challenges. It is the first book that addresses the subject of probabilistic inference by multiple agents using graphical knowledge representations.

Print version record.

English.

Added to collection customer.56279.3

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