INFSY540
Information Resources in
Management
Lesson 11
ECommerce
Finalizing Artificial Intelligence
Some AI Technologies
Expert Systems: Diagnose, respond & act like a human expert
Neural Networks: Use data to predict outputs or interpret inputs
Genetic Algorithms: Use data to find “optimal” solutions
Fuzzy Logic: Facilitate solutions to human vagueness problems
Robotics: Mimic physical human processes
Natural-Language Processing: Mimic human communication
Intelligent Tutorials: Facilitate human learning
Computer Vision: Mimic human sensory(visual) process
Virtual Reality: Mimic human reality inside a computer
Game Playing: Beat humans in games, e.g. chess
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Cognitive vs Biological AI
Cognitive-based Artificial Intelligence
Top Down approach
 Attempts to model psychological processes
 Concentrates on what the brain gets done
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Biological-based Artificial Intelligence
Bottom Up approach
 Attempts to model biological processes
 Concentrates on how the brain works
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Cognitive vs Biological AI
Cognitive AI Tools:
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Expert Systems
Natural Language
Fuzzy Logic
Intelligent Agents
Intelligent Tutorials
Planning Systems
Virtual Reality
Biological AI Tools
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Neural Networks
Speech Recognition
Computer Vision
Genetic Algorithms
Evolutionary
Programming
Machine Learning
Robotics
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Neural Networks vs Expert Systems
Neural Nets is to Expert Systems....
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As Recognition is to Thought Process
Some problems can use either one
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How do the experts solve it?
Logical step-by-step fashion? … Expert System
 Recognizing the big picture? … Neural Network
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Is enough historical data present?
Yes. … Neural Network
 No. … Expert System
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Neural Networks vs. Expert Systems
Can we use both together? YES!
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Output of neural net used as a fact in expert system:
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Vehicle suspension system diagnostics.
Neural net classifies the behavior pattern of the shock absorber
(shock is worn, ok, etc.)
Expert system uses result to perform diagnosis of the whole
system.
Expert System output as input to neural network:
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Different expert systems can perform interpretation of individual
events (ex. terrorist activities)
Interpretation can serve as input to neural network
Network identifies likelihood of perpetrator or commonalities
among events
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Genetic Algorithms vs Neural Nets
Neural Networks:
 Build models of the real world
 Use models to make predictions
Genetic Algorithms:
 Typically uses an existing model (Fitness Function)
 Searches for a good (or optimal) solution to the
model.
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Difference between Prediction
and Optimization
Prediction: What is the nutrition content
of a McDonald’s Happy Meal?
Optimization: What is the most
nutritious meal at McDonald’s?
Solving optimization problems typically
requires solving many iterations of
smaller prediction problems.
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Genetic Algorithms with
Expert Systems & Neural Nets
• GA can use ES to test feasibility of a chromosome.
• Constraints often easy to express in rules......
• GA can use trained NN as the Fitness Function.
GA
Fitness Value
Is it feasible?
ES
NN
How good is it?
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Genetic Algorithms with
Expert Systems & Neural Nets
If infeasible, return an
extremely bad Fitness
GA
Fitness Value
ES
NN
If it is a feasible solution,
send to Neural Network
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Questions about
Artificial Intelligence?
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ECommerce
Learning Objectives
Identify advantages of e-commerce
Outline how e-commerce works
Identify challenges companies must
overcome to succeed in e-commerce
Identify the major issues that threaten
the continued growth of e-commerce
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Learning Objectives
List the key technology components that must
be in place for successful e-commerce
Discuss key features of electronic payments
systems needed for e-commerce
Identify some e-commerce applications
Outline key components of a successful ecommerce strategy
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An Introduction to Electronic
Commerce
Fig 8.1
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E-Commerce Challenges
Define strategy
Change distribution systems & work
processes
Integrate web-based order processing
with traditional systems
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Can you find examples
of community, content
& commerce on
www.drugstore.com?
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Fig 8.3
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Fig 8.4
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Forms of E-Commerce
Business to Business (B2B)
Business to Consumer (B2C)
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E-Commerce Applications
Retail and Wholesale
E-tailing: electronic retailing
Cybermalls
Wholesale e-commerce: B2B
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Fig 8.5
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Marketing
DoubleClick
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Table 8.1
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Table 8.2
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Priceline
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Technology Infrastructure
Fig 8.6
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Web Server Hardware
Server platform
Hardware
 Operating system
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Website hosting
Capital investment
 Technical staff
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Must run 24-7-365 to avoid disrupting
business & losing customers
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Web Server Software
Security & identification
Encryption
Retrieving & sending web pages
Web site tracking
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E-Commerce Software
Catalog management
Product configuration
Shopping cart
Transaction processing
Traffic data analysis
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Network Selection
Cost
Availability
Reliability
Security
Redundancy
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Electronic Payment Systems
Payment Security
Authentication
Digital certificate
 Certificate authority (CA)
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Encryption
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Secure Sockets Layer (SSL)
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Payment Mechanisms
Electronic cash
Identified electronic cash
 Anonymous electronic cash (digital cash)
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Electronic wallets
Smart, credit,charge & debit cards
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Threats to E-Commerce
Threats to E-Commerce
Security
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Threats to E-Commerce
Intellectual property
Fraud
On-line auctions
 Spam
 Pyramid schemes
 Investment fraud
 Stock scams
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Threats to E-Commerce
Privacy
Online profiling
 Clickstream data
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Fig 8.8
TRUSTe Seal
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Fig 8.9
BBB Online
Privacy Seal
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How to Protect Your Privacy While On-Line
Table 8.3
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Strategies for Successful
E-Commerce
Developing an Effective Web
Presence
Obtain information
Learn about products or services
Buy products or services
Check order status
Provide feedback or complaints
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Putting Up a Web Site
In-house development
Web site hosting companies
Storefront brokers
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Driving Traffic to Your Web
Site
Domain names
Meta tags
Traffic logs
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