Introduction to MIS1 Copyright © 1998-2002 by Jerry Post Introduction to MIS Chapter 9 Complex...
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Transcript of Introduction to MIS1 Copyright © 1998-2002 by Jerry Post Introduction to MIS Chapter 9 Complex...
Introduction to MIS 1
Copyright © 1998-2002 by Jerry Post
Introduction to MIS
Chapter 9
Complex Decisions and Artificial Intelligence
Introduction to MIS 2
Computer analysisof data and model.
Decision
Operations
Tactics
Strategy
Neural network
Company
Complex Decisions& Artificial Intelligence
Introduction to MIS 3
Outline Specialized Problems Expert Systems DSS and ES Building Expert Systems Knowledge Management Other Specialized Problems Pattern Recognition DSS, ES, and AI Machine Intelligence E-Business and Software Agents Cases: Franchises Appendix: E-mail Rules
Introduction to MIS 4
Specialized Problems Diagnostics Speed Consistency Training Case-based reasoning
Introduction to MIS 5
Expert System ExampleCamcorder selection by ExSys
Link: http://www.exsys.com/
http://www.exsys.com/crdemo.html
Test It
Introduction to MIS 6
Expert System
Knowledge Base
Symbolic & Numeric Knowledge
If income > 20,000or expenses < 3000
and good credit historyor . . .
Then 10% chance of default
Rules
Expert decisionsmade bynon-experts
Expert
Introduction to MIS 7
DSS and ES
Introduction to MIS 8
ES Example: bank loan
Welcome to the Loan Evaluation System.What is the purpose of the loan? carHow much money will be loaned? 10,000For how many years? 5
The current interest rate is 10%.The payment will be $212.47 per month.
What is the annual income? 24,000
What is the total monthly payments of other loans? Why?
Because the payment is more than 10% of the monthly income.
What is the total monthly payments of other loans? 50.00
The loan should be approved, there is only a 2% chance of default.
Forward Chaining
Introduction to MIS 9
Payments< 10%
monthly income?
Other loanstotal < 30%
monthly income?CreditHistory
JobStabilityApprove
the loanDenythe loan
NoYes
Good
Yes
NoBad
So-so
Good Poor
Decision Tree (bank loan)
Introduction to MIS 11
ES Examples United Airlines GADS: Gate Assignment American Express Authorizer's Assistant Stanford Mycin: Medicine DEC Order Analysis + more Oil exploration Geological survey analysis IRS Audit selection Auto/Machine repair(GM:Charley) Diagnostic
Introduction to MIS 12
ES Problem Suitability Narrow, well-defined domain Solutions require an expert Complex logical processing Handle missing, ill-structured data Need a cooperative expert Repeatable decision
Introduction to MIS 13
ES screensseen by user
Rulesand
decisiontrees
enteredby designer
Expert
Forwardand
backwardchaining
by ES shell
Knowledgeengineer
Knowledgedatabase
(for (k 0 (+ 1 k) ) exit when ( ?> k cluster-size) do (for (j 0 (+ 1 j )) exit when (= j k) do (connect unit cluster k output o -A to unit cluster j input i - A )) . . . )
Maintained by expert system shell
Programmer
Custom program in LISP
ES Development ES Shells Guru Exsys
Custom Programming LISP PROLOG
Introduction to MIS 14
Some Expert System Shells CLIPS
Originally developed at NASA Written in C Available free or at low cost http://www.ghg.net/clips/CLIPS.html
Jess Written in Java Good for Web applications Available free or at low cost http://herzberg.ca.sandia.gov/jess/
ExSys Commercial system with many features www.exsys.com
Introduction to MIS 15
Limitations of ES Fragile systems
Small environmental. changes can force revision. of all of the rules.
Mistakes Who is responsible?
Expert? Multiple experts? Knowledge engineer? Company that uses it?
Vague rules Rules can be hard to define.
Conflicting experts With multiple opinions, who
is right? Can diverse methods be
combined? Unforeseen events
Events outside of domain can lead to nonsense decisions.
Human experts adapt. Will human novice recognize
a nonsense result?
Introduction to MIS 16
Knowledge Management A collection of a documents and data
Created by experts Searchable With links to related topics Highly organized groupware
Emphasizing context Example—business decisions
Store problem, all notes, decision factors, comments Future problems, managers can search the database and find
similar problems Better and more efficient decisions if you know the original
problems, discussions, and contingency plans Main problem—convincing everyone to enter and
update the documents
Introduction to MIS 17
AI Research Areas Computer Science
Parallel Processing Symbolic Processing Neural Networks
Robotics Applications Visual Perception Tactility Dexterity Locomotion & Navigation
Natural Language Speech Recognition Language Translation Language Comprehension
Cognitive Science Expert Systems Learning Systems Knowledge-Based Systems
Introduction to MIS 18
Output Cells
Sensory Input Cells
Hidden Layer
Some of the connections
3
-2
7
4
Input weights
Incompletepattern/missing inputs.
Neural Network: Pattern recognition
Introduction to MIS 19
Machine Vision Example
The Department of Defense has funded Carnegie Mellon University to develop software that is used to automatically drive vehicles. One system (Ranger) is used in an army ambulance that can drive itself over rough terrain for up to 16 km. ALVINN is a separate road-following system that has driven vehicles at speeds over 110 kph for as far as 140 km.
Introduction to MIS 20
Speech Recognition
Look at the user’s voice command: Copy the red, file the blue, delete the yellow mark. Now, change the commas slightly. Copy the red file, the blue delete, the yellow mark.
I saw the Grand Canyon flying to New York.
EmergencyVehicles
NoParking
Any Time
Introduction to MIS 21
Subjective Definitions
temperature
reference point
e.g., averagetemperature
cold hot
Moving farther from the reference pointincreases the chance that the temperature isconsidered to be different (cold or hot).
Subjective (fuzzy) Definitions
Introduction to MIS 22
DSS, ES, and AI: Bank Example
Decision Support System Expert System Artificial Intelligence
Name Loan #Late AmountBrown 25,000 5 1,250Jones 62,000 1 135Smith 83,000 3 2,435...
DataIncome
Existing loans
Credit report
Model Lend in all but worst casesMonitor for late and missing payments.
Output
ES Rules
What is the monthly income?
3,000
What are the total monthly payments on other loans? 450
How long have they had the current job? 5 years
. . .
Should grant the loan since there is only a 5% chance of default.
Determine Rules
loan 1 data: paidloan 2 data: 5 lateloan 3 data: lostloan 4 data: 1 late
Data/Training Cases
Neural Network Weights
Evaluate new data,make recommendation.
Loan Officer
Introduction to MIS 24
Vacation Resorts
Software agent
Resort Databases
Locate &book trip.
Software Agents Independent Networks/Communication Uses
Search Negotiate Monitor
Introduction to MIS 25
AI Questions What is intelligence?
Creativity? Learning? Memory? Ability to handle unexpected events? More?
Can machines ever think like humans? How do humans think? Do we really want them to think like us?
Introduction to MIS 26
Cases: Franchises
Introduction to MIS 27
Cases: Mrs. FieldsBlockbuster Video
What is the company’s current status?
What is the Internet strategy?
How does the company use information technology?
What are the prospects for the industry?
www.mrsfields.com
www.blockbuster.com
Introduction to MIS 28
Appendix: E-Mail Rules - Folders
Folders make it easy to organize and handle your mail.
Simple rules from the Tools + Organize button move messages directly to the specified folder.
Introduction to MIS 29
Rules: Conditions
The Tools + Rules Wizard makes it easy to create rules. Begin with a blank rule.
Set the Conditions
Set the Actions
Define Exceptions
A sample rule to handle unsolicited credit card applications.
Introduction to MIS 30
Rules: Actions
Choose an action.
You can choose multiple actions, but be careful. The marking options are often combined.
Introduction to MIS 31
Rules: Exceptions
Rules can have exceptions. For example, you might want to delete company newsletters—unless one has your name in it.
Introduction to MIS 32
Rule Sequences: Decision Tree
From boss, Subject: Expenses
Message from Expense
Accounting Expenses Folder
Set expenses categoryMove it
Rule 1
Rule 2
Expenses categorySubject: Payment
Rule 3
Action: Mark important and notify.