Transcript Organizational Foundations of Information Systems

Chapter 4 Decision Support and Artificial
Intelligence
Four Types of Decisions (p.128 - p.130)
• Structured vs. Nonstructured (Examples?)
– Structured: Follow rules and criteria. The right
exists. No “feel” or “intuition”.
answer
– Nonstructured: No rules or criteria. Several “right”
answers but there is no precise way to get the right answer.
• Recurring vs. Nonrecurring
(Examples?)
– Recurring: repeatedly or periodically
– Nonrecurring: infrequently, perhaps only once
Information Technologies that Help Decision
Making
• Decision support systems (DSS)
• Group decision support systems (GDSS)
• Geographic information systems (GIS)
• Artificial Intelligence (AI)
– Expert systems
– Neural networks
– Genetic algorithms
– Artificial Intelligence
Decision Support Systems
Decision Support System and Its Components
DSS is an interactive IT system that creates
new information on demand to help you make
nonstructural decisions. (p.131)
Components of DSS:
(p.132 Fig. 4.5)
• User interface
• Model base
• Database
Group Decision Support Systems
Group Decision Support System (p.135)
A GDSS is a type of DSS that facilitates the
formulation of and solution to problems by a
team. It integrates (Fig. 4.7)
• Groupware (email, conferencing systems,
collaborative authoring systems, coordination
systems)
• DSS capability
• telecommunications
Group Decision Support Systems
IT Supports Team Decision-Making Process
(p.136)
• Brainstorming - GDSS allows team members
to enter comments and
suggestions anonymously.
• Issue categorization and analysis - GDSS
sorts and classifies the team’s
ideas into folders.
• Ranking and voting - GDSS calculates and
displays the outcome.
Group Decision Support Systems
Advantages of GDSS Enhanced Meeting
(p.138 - p.140)
• GDSS introduces independent thought and
anonymity that reduce bias by influential
members, conflict between members and
groupthink.
• Different locations
• Different-time meetings by using an
electronic bulletin board, a central database,
or email.
Geographic Information Systems
What is a GIS?
(p.140)
• A computer system that records, stores, and analyses
information about the earth’s surface.
• GIS can generate two or three dimensional images of
an area, showing natural and man-made features.
• GIS databases consist of sets of information called
layers. Each layer represents a particular type of
geographic data such as roads, utilities, population,
elevation, and so on. The GIS can combine these
layers into one image.
(continued)
Geographic Information Systems
What is a GIS?
(continued)
• GIS sensors can scan some of geographic data directly
from a variety of sources. GIS convert all data into a
digital code and store in database.
• GIS provides an easy means of trying various “what if
scenarios”.
• An example of GIS:
Massachusetts Geographic Information System.
Artificial Intelligence
(p.143)
What is artificial intelligence?
AI is the science of making machines imitate human
thinking and behavior. IT techniques and software can
enable computers to mimic human behavior in various
ways.
What are major categories of AI uses by
businesses?
• Expert systems
• Neural networks
• Genetic algorithms
• Intelligent agents
1. Expert Systems
(p.144)
• What is an expert system? – applies rules and
reasoning capability to reach a conclusion. It’s
typically for a specific field.
• What are components of an expert system? (p.146)
• What is knowledge representation and production
rules? (p.147 – domain expertise captured as rules
by the knowledge engineer)
• Reading: Joseph Schmuller, “Expert Systems:
A Quick Tutorial,” Journal of IS Education.
• An example of expert system.
2. Neural Networks
(p.150)
What is a neural network (e-book)?
• Neural network is an information-processing system
patterned after the human brain (neurons and
synapses).
• Neural network is an artificial intelligence system
which is capable of learning to differentiate patterns.
• Neural network is capable of adaptive learning.
It can be trained (attributes and weights).
• Example: Good stocks (p.150-p.151)
2. Neural Networks
For more details, read
Introduction to neural network
For applications, read
Synergistic Market Analysis with Neural Network
3. Genetic Algorithms
What is GA?
(p.151)
John Holland, 1960’s
• GA is an algorithm that mimics the evolutionary,
survival-of-the-fittest process to generate
increasingly better solutions to a problem. GA is
based on an evolution of random tries, not on logic
as regular optimal algorithms.
• GA borrowed ideas from biological evolution: only
the combination of different genomes can lead to the
optimal or better solution.
(Continued)
3. Genetic Algorithms
What is GA?
(Continued)
• GA search for better solution through iterations.
Each cycle includes:
(p.152)
– Selection: select survivors with objective function.
– Crossover: combine portions of outcomes in the
hope of creating an even better outcomes.
– Mutation: randomly trying combinations and
evaluating the outcomes.
3. Genetic Algorithms
An example of GA: Maze Solver
• The targets: red dot.
• Genes: alternate green-blue dots.
• Bombs: light red dots.
• Randomize button: generate new maze
pattern.
• The GA completes when either the target is
encountered or maximum number of
generation steps is reached.
4. Intelligent Agents
(p.152)
What is Intelligent Agent?
• An intelligent agent is an artificial intelligent system
which can act as personal assistant to perform repetitive
tasks independently, adapting itself to your preference.
• Four types of intelligent agents
– Find-and-retrieve agents
– User agents (help an individual, work in the
background, e.g., check email)
– Monitor and surveillance agents (monitor large
network for potential problems).
– Data-mining agents (identify new relationships and
patterns, and alert you)