intelligent systems over the internet by dr.s.sridhar,ph.d.,...
TRANSCRIPT
Intelligent Systems Over the Internet
ByDr.S.Sridhar,Ph.D.,
RACI(Paris),RZFM(Germany),RMR(USA),RIEEEProc.
email : [email protected] : http://drsridhar.tripod.com
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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