Utilization of neural network and agent technology combination for distributed intelligent applications and services
1University of Oulu, Faculty of Technology, Department of Electrical and Information Engineering
|Online Access:||PDF Full Text (PDF, 3.6 MB)|
|Persistent link:|| http://urn.fi/urn:isbn:9514278550
|Publish Date:|| 2005-10-25
|Thesis type:||Doctoral Dissertation
|Defence Note:||Academic Dissertation to be presented with the assent of the Faculty of Technology, University of Oulu, for public discussion in the Auditorium TS101, Linnanmaa, on November 4th, 2005, at 12 noon
Professor Martti Mäntylä
Professor Roope Raisamo
The use of agent systems has increased enormously, especially in the field of mobile services. Intelligent services have also increased rapidly in the web. In this thesis, the utilization of software agent technology in mobile services and decentralized intelligent services in the multimedia business is introduced and described. Both Genie Agent Architecture (GAA) and Decentralized International and Intelligent Software Architecture (DIISA) are described.
The common problems in decentralized software systems are lack of intelligence, communication of software modules and system learning. Another problem is the personalization of users and services. A third problem is the matching of users and service characteristics in web application level in a non-linear way. In this case it means that web services follow human steps and are capable of learning from human inputs and their characteristics in an intelligent way. This third problem is addressed in this thesis and solutions are presented with two intelligent software architectures and services.
The solutions of the thesis are based on a combination of neural network and agent technology. To be more specific, solutions are based on an intelligent agent which uses certain black box information like Self-Organized Map (SOM). This process is as follows; information agents collect information from different sources like the web, databases, users, other software agents and the environment. Information is filtered and adapted for input vectors. Maps are created from a data entry of an SOM. Using maps is very simple, input forms are completed by users (automatically or manually) or user agents. Input vectors are formed again and sent to a certain map. The map gives several outputs which are passed through specific algorithms. This information is passed to an intelligent agent.
The needs for web intelligence and knowledge representation serving users is a current issue in many business solutions. The main goal is to enable this by means of autonomous agents which communicate with each other using an agent communication language and with users using their native languages via several communication channels.
Acta Universitatis Ouluensis. C, Technica
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