Download - DSS-AI
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Decision and Intelligent Systems
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Benefits of Decision and Intelligent
Systems
Dramatically improved business processes
which reduce costs
Better coordination and support for
individuals and groups in the firm
The ability to capture and retain knowledge
that exists in the organization
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Measures of the Contribution of
Decision and Intelligent Systems
Improvements in efficiency
Better customer service
Cost savings per project
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Decision Support Systems (DSS)
A computer-based system that helps the decision maker
utilize data and models to solve unstructured problems
E.g., a spreadsheet package
DSS can be classified as
data-oriented
provide tools for the manipulation and analysis of data
model-based
generally have some kind of mathematical model of the decision
being supported
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Other DSS Examples
General Motors OnStar
a two-way vehicle satellite communication system offering a number of
services for safety, security and entertainment
GM has an 80% share of the telematics market, and OnStar has a market value
of several billion dollars
Merrill Lynchs Integrated Choice Account Structure
helped design appropriate account structures and pricing for the company
Integrated Choice program
analysis considered the total revenue at risk, estimated what accounts
customers would choose, and the impact of their choice on revenues
Helped the company increase assets and customers
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Executive Information Systems (EIS)
EIS bring to senior management information that
needs its attention
summarize data and makes them available for
downloading to a personal computer in theexecutive's office
have an appealing interface and an easy to use system
drill down feature
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Group Decision Support Systems
(GDSS)
GDSS consist of special software and physical
facilities such as a conference room containing
PCs for each person in the room
The software helps identify issues and
evaluate alternative decisions and actions
Enable workgroup decision making
Technology-assisted meetings
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Intelligent Systems
Turings test for Artificial Intelligence (AI)
place a computer and a human in two separaterooms
an interviewer in a third room, who cannot seethe human or the computer user, asks questionsthat are passed to the computer and to thehuman
if the interviewer cannot tell the differencebetween the answers from the computer and thehuman, the machine is said to exhibit intelligentbehavior
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AI Versus Traditional Programs
AI programs manipulate symbols rather than
numbers
AI programs are non-algorithmic often
employing heuristics or rules of thumb
Many AI programs are concerned with pattern
recognition
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Components of Expert Systems
The user interface
a good interface make the system much more pleasant to use and
helps promote its acceptance
The knowledge base For example, the use of rules to represent the experts knowledge
a rule in a knowledge base contains some of the logic of an
application
The inference engine the reasoning part of the expert system
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The Inference Engine
Forward chaining involves going through the
rules one at a time to infer the best
recommendation
In backward chaining, the system begins with
a goal and at each stage, the inference engine
establishes sub-goals
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Expert System Development
Prototyping
Learning through test cases
The person designing the system is sometime referred to as
the knowledge engineer
Knowledge engineering is difficult because
experts are not conscious of decision-making steps and have difficulty
explaining their logic
in some cases, experts are reluctant to reveal their expertise to
systems developers
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Examples of Expert Systems
AESOP: A System for Stock Options Pricing
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Examples of Expert Systems
The Port of Singapore Authority Expert Systems
planning and managing all operations of the port
E.g., allocating berths to ships, planning the stowage of containers,
the allocation of resources in general, and reading container
numbers and operating trucking gates managing shipping traffic and the activities of the port
E.g., assigning ships to anchorages, scheduling the movement of
vessels through channels to terminals, deploying pilots to tugs and
launches, routing launches, and deploying tugboats
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Knowledge Discovery
Combines AI with large databases
Knowledge discovery programs look for
patterns in the data and report the results to
the user
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Neural Networks
The first neural networks were loosely based
on how the brain functions
The most popular type of such programs is
used to classify input into different categories
A neural network has to be first trained by
presenting it with past cases
After training the network can be used for
classification
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Case-Based Reasoning
Captures lessons from past experience anduses them to find solutions to a new problem
It is both a problem-solving approach and a
model of how some experts think individualslearn, remember, and think about problems
A CBR needs cases, a similarity index, a case
retrieval mechanism, and an explanationmodule
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Genetic Algorithms
Involves generating a population of possible problemsolutions and rating them based on some fitness function
applying a selection function to the un-rated population to select``parents'' for the next generation of solutions
using a reproduction function to generate copies of the parents
The inversion operation reverses the order of randomly selected,contiguous portions of the vector
A point mutation alters a single feature, replacing it with arandomly chosen value
The crossover operator randomly selects a sequence of featuresand swaps them between two parents
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Intelligent Agents
An agent is a piece of software that performs
a task for its owner
involves AI combined with networks
applications for intelligent agents have been for
consumer tasks like shopping and providing
recommendations based on profile matches
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Summary
IT can do more than process transactions and
help people communicate
Some of the decision and Intelligent Systems
applications carry high risks but can also leadto substantial rewards