© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-1 Chapter 13 Intelligent Systems Over

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

13-1

Chapter 13Intelligent Systems Over the

Internet

Turban, Aronson, and Liang Decision Support Systems and Intelligent

Systems, Seventh Edition


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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.


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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

Documents

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 13-1 Chapter 13 Intelligent Systems Over