Andere Kunden interessierten sich auch für
![Probabilistic Reasoning in Multiagent Systems]( "Probabilistic Reasoning in Multiagent Systems")
Probabilistic Reasoning in Multiagent Systems57,99 €![Interactions in Multiagent Systems]( "Interactions in Multiagent Systems")
Interactions in Multiagent Systems151,99 €![Probabilistic Proof Systems]( "Probabilistic Proof Systems")
Probabilistic Proof Systems82,99 €![Retrival of aggregate data in uncertain and probabilistic database]( "Retrival of aggregate data in uncertain and probabilistic database")
Retrival of aggregate data in uncertain and probabilistic database30,99 €![Probabilistic Model of Canonically Conjugated Fuzzy Subsets]( "Probabilistic Model of Canonically Conjugated Fuzzy Subsets")
Probabilistic Model of Canonically Conjugated Fuzzy Subsets23,99 €![Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems]( "Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems")
Diagnostic Test Approaches to Machine Learning and Commonsense Reasoning Systems194,99 €![Learning Probabilistic Graphical Models in R]( "Learning Probabilistic Graphical Models in R")
Learning Probabilistic Graphical Models in R40,99 €
This book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artificial intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results from a decade's research. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.
Table of contents:
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 multi-agent 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.
Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments.
Table of contents:
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 multi-agent 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.
Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments.