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A multi-agent based user context Bayesian neural network analysis system

A multi-agent based user context Bayesian neural network analysis system The increasing user mobility demands placed upon IT services necessitates an environment that enables users to access optimal services at any time and in any place. This study presents research conducted to develop a system that is capable of analyzing user IT service patterns and tendencies and provides the necessary service resources by sharing each user’s context information. First, each user’s context information is gathered to provide the multi-agent software training data necessary to describe user operations in a hybrid peer-to-peer (P2P) structured communication environment. Next, the data collected about each user’s mobile device is analyzed through a Bayesian based neural network system to identify the user’s tendency and extract essential service information. This information provides a communication configuration allowing the user access to the best communication service between the user’s mobile device and the local server at any time and in any place, thereby enhancing the ubiquitous computing environment. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Intelligence Review Springer Journals

A multi-agent based user context Bayesian neural network analysis system

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References (11)

Publisher
Springer Journals
Copyright
Copyright © 2010 by Springer Science+Business Media B.V.
Subject
Computer Science; Computer Science, general; Artificial Intelligence (incl. Robotics)
ISSN
0269-2821
eISSN
1573-7462
DOI
10.1007/s10462-010-9174-x
Publisher site
See Article on Publisher Site

Abstract

The increasing user mobility demands placed upon IT services necessitates an environment that enables users to access optimal services at any time and in any place. This study presents research conducted to develop a system that is capable of analyzing user IT service patterns and tendencies and provides the necessary service resources by sharing each user’s context information. First, each user’s context information is gathered to provide the multi-agent software training data necessary to describe user operations in a hybrid peer-to-peer (P2P) structured communication environment. Next, the data collected about each user’s mobile device is analyzed through a Bayesian based neural network system to identify the user’s tendency and extract essential service information. This information provides a communication configuration allowing the user access to the best communication service between the user’s mobile device and the local server at any time and in any place, thereby enhancing the ubiquitous computing environment.

Journal

Artificial Intelligence ReviewSpringer Journals

Published: Jul 22, 2010

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