Chapter 13 Intelligent Systems Over the Internet

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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 owners account
• Goals and metrics
• Determines tasks point of completion and value
  of results
• Subject Description
• Description of goals 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

• Franklin and Graessers 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

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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 users 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