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eBook Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach ePub

by Yang Xiang

Author: Yang Xiang
Language: English
ISBN: 0521813085
ISBN13: 978-0521813082
Publisher: Cambridge University Press; 1 edition (August 26, 2002)
Pages: 308
Category: Computer Science
Subcategory: Computers
Rating: 4.4
Votes: 469
Formats: mbr lrf mobi txt
ePub file: 1878 kb
Fb2 file: 1993 kb

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Modelling conditional independence relations using graphical models lets us take advantage of efficient . Multiply Sectioned Bayesian Network (MSBN) provides a model for probabilistic reasoning in multi-agent systems

Modelling conditional independence relations using graphical models lets us take advantage of efficient inference when representing a JP. .Multiply Sectioned Bayesian Network (MSBN) provides a model for probabilistic reasoning in multi-agent systems. So the approximate techniques are used as an alternative in such cases.

The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm

Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become . Book Condition: A copy that has been read, but remains in excellent condition.

Book Condition: A copy that has been read, but remains in excellent condition. Pages are intact and are not marred by notes or highlighting, but may contain a neat previous owner name. The spine remains undamaged.

A Constructive Graphical Model Approach for Knowledge-Based Systems: A Vehicle Monitoring Case Study. Xiangdong An Yang Xiang and Cercone, N. 2004. Revising Markov boundary for multiagent probabilistic inference. Computational Intelligence, Vol. 19, Issue. Rezek, I. Roberts, . and Jennings, N. 2005. Unifying learning in games and graphical models.

oceedings{icRI, title {Probabilistic reasoning in multiagent systems - a graphical models .

oceedings{icRI, title {Probabilistic reasoning in multiagent systems - a graphical models approach}, author {Yang Xiang}, year {2002} }. Yang Xiang.

Автор: Yang Xiang Название: Probabilistic Reasoning in Multiagent Systems Издательство: Cambridge Academ . Yang Xiang extends graphical dependence models to the distributed and multi-agent paradigm

Yang Xiang 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.

Probalistic reasoning with graphical models . In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm.

Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. Probabilistic Proof Systems: A Primer. Report "Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach".

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Manufacturers, suppliers and others provide what you see here, and we have not verified it. See our disclaimer. Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments

Manufacturers, suppliers and others provide what you see here, and we have not verified it. Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments. Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach. Cambridge University Press.

Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical 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 paradim 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 gleaned from a decade's research.

Qumen

This book can be placed in the area of research in AI that brings single agent, centralised techniques to a distributed multi-agent context. The central idea of the multi-agent paradigm is to solve complex problems with a collection of autonomous and possibly distributed agents.
In the first 5 chapters this book gives a thorough understanding of exact inference in Bayesian networks.
In the 6th chapter Y. Xiang introduces Multiply Sectioned Bayesian networks (MSBN), a knowledge representation formalism for probabilistic inference in multi-agent systems. He clearly informs the reader of the constraints that are associated with MSBNs and how they are the unevitable consequence of a few high level choices.
Some of the choices are:
- the beliefs of the agents are represented by probabilities
- the internal representation of an individual agent is a DAG
- the least amount of communication between agents possible
Some of the contraints that follow from these choices are:
- a hypertree agent organisation which prevents agents from communicating with any other agent
- only communication between agents on variables they share between their local models
In subsequent chapters the author introduces algorithms for cooperative, distributed probabilistic inference. First how to compile an MSBN to a linked junction forest (= the multi-agent version of a junction tree) through moralization, triangulation, and the construction of linkage trees. Then, how to perform the actual probabilistic inference in such a linked junction forest.
In the 9th chapter algorithms are shown that allow to verify whether a structure does not violate the constraints imposed by the MSBN paradigm.
Finally, in the last chapter Xiang gives an overview of all the possible extensions and future work, such as dynamic formation, learning, negotiation etc.
The book is clearly written and very understandable, even for people with little knowledge of probabilistic reasoning. In my opinion this is because of the clear and not unnecessarily complicated language and because throughout the entire book the same example is used (monitoring of digital circuits ).
A few points of critique are that some more space could have been devoted to possible applications and related work.
To conclude, a very interesting and clear book on a new and promising paradigm, suited for everyone interested in Bayesian networks and multi-agent systems.

HelloBoB:D

Describes a selection of message-passing approaches, including the techniques of MSBNs and LJFs initiated by Xiang and his colleagues in earlier publications.

Concepts are introduced laboriously and the page count could be halved with negligible loss of clarity. Separately, the style of prose demands more patience than that of, say, Russell and Norvig ('AI: A Modern Approach'). Yet I imagine that sections of the book could function as a reference, for advanced readers who knew what to look for.

Zonama

This book is a fabulous resource. Concise prerequisite knowledge is provided where necessary, examples are clear and relevant.