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In this chapter you will learn: How do businesses make decisions? How do you make a good decision? Why do people make bad decisions? How do you find and retrieve data to analyze it? How can you quickly examine data and view subtotals without writing hundreds of queries? How does a decision support system help you analyze data?
Introduction to MIS Chapter Complex Decisions and Artificial Intelligence Copyright © 1998-2002 by Jerry Post Introduction to MIS Complex Decisions & Artificial Intelligence Strategy Decision Computer analysis of data and model Neural network Tactics Operations Company Introduction to MIS 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 Specialized Problems Diagnostics Speed Consistency Training Case-based reasoning Introduction to MIS Link: http://www.exsys.com/ Expert System Example Camcorder selection by ExSys Test It http://www.exsys.com/crdemo.html Introduction to MIS Expert System Expert Knowledge Base Symbolic & Numeric Knowledge Rules Expert decisions made by non-experts If income > 20,000 or expenses < 3000 and good credit history or Then 10% chance of default Introduction to MIS DSS and ES Introduction to MIS ES Example: bank loan Welcome to the Loan Evaluation System What is the purpose of the loan? car Forward Chaining How much money will be loaned? 10,000 For how many years? 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 Introduction to MIS Decision Tree (bank loan) Payments < 10% monthly income? No Yes Other loans total < 30% monthly income? Yes Credit History Good Bad So-so Approve the loan Introduction to MIS Job Stability Good Poor No Deny the loan Frame-Based ES Job History Customer Data Employer, Salary, Date Hired Name Address Years at address Co-applicant _ Rules Job History Employer, Salary, Date Hired Loan Details Data for Boat Loans Purpose Boat Loan Amount _ Time _ Introduction to MIS Recommendation Lend $$$$ at _ interest rate for _ months, with _ initial costs Length: Engine: Cost New: Cost Used: 10 Neural Network: Pattern recognition Output Cells Input weights -2 Hidden Layer Some of the connections Incomplete pattern/missing inputs Introduction to MIS Sensory Input Cells 18 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 19 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 Introduction to MIS Emergency Vehicles No Parking Any Time 20 Subjective (fuzzy) Definitions Subjective Definitions reference point cold hot temperature e.g., average temperature Moving farther from the reference point increases the chance that the temperature is considered to be different (cold or hot) Introduction to MIS 21 DSS, ES, and AI: Bank Example Decision Support System Loan Officer Data Model Output Expert System Artificial Intelligence ES Rules Determine Rules Income What is the monthly income? Existing loans 3,000 Credit report What are the total monthly payments on other loans? 450 Lend in all but worst cases Monitor for late and missing payments Name Brown Jones Smith Loan #Late Amount 25,000 1,250 62,000 135 83,000 2,435 How long have they had the current job? years Data/Training Cases loan data: paid loan data: late loan data: lost loan data: late Neural Network Weights Should grant the loan since there is only a 5% chance of default Evaluate new data, make recommendation Introduction to MIS 22 DSS, ES and AI: Inventory Example Decision Support System Expert System Choosing an Inventory System Data a estimate sales K order setup cost h estimate holding cost What is the cost of running out of inventory? 45,000 per day What are daily profits? 250,000 Model Q* = sqrt ( 2ak / h ) How many suppliers are there? Artificial Intelligence Automatically Analyze Data/Training Cases site data: JIT site data: EOQ site data: JIT site data: hybrid Can more suppliers be added in an emergency? no Output How close is the nearest supplier? 10 kilometres Inventory Levels Q* Neural Network Weights How reliable is this supplier? very reorder points Introduction to MIS Best choice is to use Just-In-Time inventory system Only a 2% chance of running out of inventory for more than days time Evaluate new data, make recommendation 23 Software Agents Independent Networks/Communication Uses Locate & book trip Software agent Search Negotiate Monitor Vacation Resorts Resort Databases Introduction to MIS 24 AI Questions What is intelligence? Creativity? Learning? Memory? Ability to handle unexpected events? More? Can machines ever think like humans? How humans think? Do we really want them to think like us? Introduction to MIS 25 Cases: Franchises Introduction to MIS 26 Cases: Mrs Fields Blockbuster Video www.mrsfields.com www.blockbuster.com 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? Introduction to MIS 27 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 28 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 29 Rules: Actions Choose an action You can choose multiple actions, but be careful The marking options are often combined Introduction to MIS 30 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 31 Rule Sequences: Decision Tree Rule Message from Expense Accounting Expenses Folder Set expenses category Move it Rule From boss, Subject: Expenses Rule Expenses category Subject: Payment Action: Mark important and notify Introduction to MIS 32 ... 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... result? 15 Knowledge Management A collection of a documents and data Emphasizing context Example business decisions Created by experts Searchable With links to related... 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