1. Chapter 13 Intelligent Systems Over the Internet
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition
Chapter 13 Intelligent Systems Over the Internet
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

2. Understand intelligent systems operating across the Internet.
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.
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

6. Intelligence Levels Wooldridge Lee
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

7. Components Owner Author Account Goals and metrics Subject Description
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

11. Characteristics Agency Intelligence Mobility Mobile agents
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

12. Web Based Software Agents
/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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
Components
data., modeling, user interface
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

18. © Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

20. Advantages and Limitations
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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang