Intelligent Systems Over the Internet

1. Intelligent Systems Over the Internet By Dr.S.Sridhar,Ph.D.,RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc.email : drssridhar@yahoo.comweb-site : http://drsridhar.tripod.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 • Franklin and Graesser’s autonomous agents • 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

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: 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 Advantages and Limitations

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