1. Intelligent Systems Over the Internet By Dr.S.Sridhar,Ph.D., RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc. email : drssridhar@yahoo.com web-site : http://drsridhar.tripod.comdrssridhar@yahoo.com

2. Learning Objectives Understand intelligent systems operating across the Internet. Examine the concept of intelligent agents. Learn intelligent agent applications. Explore the concept of Web-based semantic knowledge. Understand recommendation systems. Design recommendation systems.

3. Spartan Uses Intelligent Systems to Find the Right Person and Reduce Turnover Vignette Supermarket chains experience over 100% turnover Employee replacement expensive Front-end positions critical in terms of customer relationships Spartan employed automated hiring system Analyze applicant profile Selects candidates from huge applicant pool Reduced turnover rate to 59% Increased operational efficiency Integrated with other systems

4. Intelligent Systems Programs with tasks automated according to rules and inference mechanisms Web used as delivery platform May include semantic information Semantic Web Generally perform specific tasks Information agents Monitoring agents Recommendation agents

5. Intelligent Agents Program that helps user perform routine tasks Software agents, wizards, demons, bots Degree of independence or autonomy Three functions Perception of dynamic conditions Actions that affect environment Reasoning

6. Intelligence Levels Wooldridge Reactivity to changes in environment Ability to choose response Capability of interaction with other agents Lee Level 0 Retrieve documents from URLs specified by user Level 1 User-initiated search for relevant pages Level 2 Maintain user profiles Notify users when relevant materials located Level 3 Learning and deductive reasoning component to assist user in expressing queries

7. Components Owner User name, parent process name, or master agent name Author Development owner, service, or master agent name Account Anchor to owner’s account Goals and metrics Determines task’s point of completion and value of results Subject Description Description of goal’s attributes Creation and Duration Request and response date Background information Intelligent subsystem Can provide several of the above characteristics

8. Agents Can act on own or be empowered Can make some decisions Can decide when to initiate actions Unscripted actions Designed to interact with other agents, programs, or humans Automates repetitive, narrowly defined tasks Continuously running process Must be believable Should be transparent Should work on a variety of machines May be capable of learning

9. Successful Intelligent Agents Decision support systems Employee empowerment for customer service Automation of routine tasks Search and retrieval of data Expert models Mundane personal activity

10. Classifications Organization agents Task execution for processes or applications Personal agents Perform tasks for users Private or public agents Used by single user or many Software or intelligent agents Ability to learn Franklin and Graesser’s autonomous agents

11. Characteristics Agency Degree of measurable autonomy Ability to run asynchronously Intelligence Degree of reasoning and learned behavior Mobility Degree to which agents move through networks and transmit and receive data Mobile agents Nonmobile are two dimensional Mobile are three dimensional

12. Web Based Software Agents E-mail/Mailbot agents Softbots: Agents offering assistance with Web browsing Assistance with frequently asked questions Search engines Metasearch engines Network agents Monitor Diagnose problems Security Resource management

13. E-commerce Agents Identify needs Search for product Find best bargain Negotiate price Arrangement of payment Arrange delivery After sales service Advertisement Payment support Fraud detection

14. Other Agents Computer interfaces Agents to facilitate learning Speech agents Intelligent tutoring Support for activities along supply chain Administrative office management Workflow, computer-telephone integration Web mining for information Monitoring for alerts Collaboration among agents Mobile commerce using WAP-based services

15. DSS Agents Agent types Data monitoring, data gathering, modeling, domain management, learning preferences Holsapple and Whinston Map types against − Characteristics – Homeostatic goals, persistence, reactivity − Reference points – Client, task,domain Hess Map types against − Components – data., modeling, user interface

16. Multi-agent Systems Multiple software agents used to perform tasks Multiple designers Agents work toward different goals Can cooperate or compete Distributed artificial intelligence Single designer Decomposes tasks into subtasks Distributed problem solving Single goal

17. Semantic Web Content presentation Organization standard Enables access to Web-based knowledge Allows Web-based collaboration and cooperation Technologies XML Scripting language employing user defined tags Web services XML-based technologies comprised of four layers − Transport, XML messaging, service description, publication and integration

19. Components of Semantic Web Resource Description Framework data model Relate Uniform Resource Identifiers to each other Point to Web resources Language with defined semantics Standardized terminologies for knowledge domain Service logic establishes rules governing use Proof Trust

20. Advantages and Limitations Advantages: Easy to understand Systems and modules easily integrated Saves development time and expense Allows for incremental and rapid development Updates automatically Resources reuse Limitations: Oversimplified graphical representation Needs additional tools Incorrect definitions Information may be incorrect or inconsistent Security

21. Recommendation Systems Personalized Collect and analyze each user’s information and needs Profile generation and maintenance − Profiling method determination − Initial profile generation − Data processing for pattern recognition − Feedback collection − Analyze feedback and adapt Profile exploitation and recommendation − Identify useful information − Compare user profile to new items − Locate similar users, create neighborhood, make prediction

22. Recommendation Systems Collaborative filtering Market segmentation used to predict preferences Compares individual to population in order to locate similar users Similarity index metrics Infer interests Predicts preferences based on weighted sums Content-based filtering Recommendations-based on similarities between products Attribute based Works with small base of data Neglects aesthetic aspects of products

23. Management Issues Expense Security Systems integration and flexibility Hardware and software requirements Agent accuracy Agent learning Invasion of privacy Competitive intelligence and industrial intelligence Other ethical issues Heightened expectations Systems acceptance