1. An Intelligent System for Dynamic Online Allocation of Information on Demand from the Internet Thamar E. Mora, Rene V. Mayorga Faculty of Engineering, University of Regina, Regina, Saskatchewan, Canada

2. Intelligent System and Objective  Proof of concept of Intelligent System  Intelligent System based on Fuzzy Inference System  To Customize and Allocate Dynamically Online Information from the Internet

3. Introduction  Advances in Computer and Communications Technology have led to - Information Convergence - Information Convergence  No longer Video on Demand; but rather - Information on Demand - Information on Demand  The Internet contains plenty of data, leading to - Information Saturation

4. Background  Already available some tools for: - Interfaces - Browsers - Customized Web sites  The authors recently proposed: - Intelligent System, based on a - Intelligent System, based on a - Fuzzy Inference System, for - Fuzzy Inference System, for - Dynamic On-Line Portal Customization, and - Dynamic On-Line Portal Customization, and - Intelligent Web Advertising - Intelligent Web Advertising  The authors also recently proposed: - Intelligent System, based on a - Fuzzy Inference System, for - Dynamic On-Line - TV Programming Allocation from - TV Internet Braodcasting

5. Proposed Intelligent System  The user provides as inputs the type of information customization that he/she desires to receive  According to the user preferred selection, a data gathering process (if the information is not already available in a database) is started  This data is processed though a - Fuzzy Inference System - prompting as output - the kind and amount of information, and - the most appropriate media from which the - information is to be received

7. User Preferred Selection  The user makes a preferred selection to receive information based on:  Language - Country;  Level of preference about certain topics - World, business, politics, technology, entertainment, sports, health, weather, etc.; entertainment, sports, health, weather, etc.;  Level of preference about certain media - Television, Radio, Newspapers, Magazines, Journals, Photojournalism, etc.; Journals, Photojournalism, etc.;  Level of desired detail in the output diagnostic - Low, Medium, High It is an input to our Intelligent Agent, but not the FIS It is an input to our Intelligent Agent, but not the FIS

8. Fig.2. Screen to input the preferences

9. Inputs to the FIS  Six inputs to the Fuzzy Inference System:  Three inputs with a high level of preference, each to set the level of preference of interest for a particular topic  Three inputs with a high level of preference, each to set the level of preference to receive information from a particular media  The number of inputs can be changed for a larger or smaller number  In this project the number of inputs is considered relatively small in order to provide better-customized options, and not just a large list

10. Outputs from the FIS  Two outputs:  The kind (particular links), and the amount (number of links) of information to be displayed, and  And the most appropriate media from which the information is to be displayed  A portal-type customization is dynamically generated online with proper links according to the user preferences  The number of links plays the role of pondering the importance in the decision

11. Fuzzy Inference System  FIS is a Mamdani type  Uses the centroid as the defuzzification method  The membership functions (MFs) for all the linguistic values are triangular  Matlab based

12. Figure 3. Fuzzy Inference System Structure

13. Rules Structure  The linguistic values for the inputs are “ not too much”, “ regular” and “ too much” “ not too much”, “ regular” and “ too much”  For the outputs, numbers are defined as the labels.  In general, a Fuzzy knowledge model consists of a set of rules of the form: - If x is A then y is B - If x is A then y is B  The current prototype includes 54 rules. These rules are determined according to the smoothness of the rules surface.  The structure of the rules follows the following pattern - Topic1 and Topic2 and Topic3  Links  - Media1 and Media2 and Media3  Media

14. Figure 4. Example of a View of the Rules’ Surface

15. Rules Structure  It is also possible to consider the option of allowing the specification of the media’s preference for each topic.  However, this will give a Fuzzy Inference System similar to the one shown in Figure 5.  In this case a much larger set of rules is needed than the current prototype.

16. Fig.5. Option that allows setting the preference for media in each topic

17. Example 1  The user preferred selection: - Language Country (USA);  - Level of interest for a particular Topic: Fig. 6; - Level of preference for a particular media: Fig 6;  - Detail Level: Medium  According to the user preferences in Fig. 6, our Intelligent System prompts the FIS output taking into account the preferred level of detail to display the information

18. Fig. 6. User preferences for Example

19. Example 1  The Intelligent System output is:  Display - three links of news around the World, - three links related to Business news; and - two links for the latest on Politics  From - Newspapers sites - in United States of America

20. Fig. 7. Output of the agent for the preferences shown in Figure 6 for Example 1

21. Example 1  Figure 7 shows the corresponding icons: the gateway for the user to reach the desired information.  Here the example is illustrated with icons, but behind the icons the corresponding web site addresses are:  http:/nytimes.com/pages/world/index.html  http://nytimes.com/pages/business/index.html  http://nytimes.com/pages/politics/index.html

22. Example 2  The user preferred selection: - Language Country (USA);  - Level of interest for a particular Topic: Fig. 8; - Level of preference for a particular media: Fig 8;  - Detail Level: Low  According to the user preferences in Figure 8, our Intelligent System prompts the FIS output taking into account the preferred level of detail to display the information

23. Fig. 8. User preferences for Example 2

24. Example 2  The Intelligent System output is:  Display - two links of news around the World, - two links related to Technology news; and - one link for the latest on the Whether  From - Television sites - in United States of America

25. Fig.9. Output of the agent with links for Television programs about news around the World broadcast in the Internet

26. Fig.10. Links for Television programs related to Technology

27. Fig.11. Link for a Television program with the latest about current Weather news broadcast in the Internet

28. Example 2  The Agent prompts the links for programs available at the time.  Here, the television screen obtained after selecting and clicking a link is shown.  The user has access to the different broadcast options inferred from his/her preferences as shown Figures 9 - 11.  For the special case of Television, our Intelligent System prompts online programming or the latest recorded programs that were broadcast in the net.

29. Conclusions  An Intelligent System as a Proof of Concept  Dynamic Online Information Allocation from the Internet  FIS architecture as a framework for intelligent decisions about the kind, the quantity of information, and the media from which it is to be displayed  Current Intelligent System, a Generalization from our own previous Intelligent System: Intelligent System for Dynamic On-Line TV Programming Allocation from TV Internet Broadcasting - IASTED ISC’2001 - IASTED ISC’2001

30. Thanks !