Issues, Trends and Strategies for Computer Systems Management

1. Issues, Trends and Strategies for Computer Systems Management UMUC Graduate School of Management and Technology Chapter 10. Complex Decisions and Artificial Intelligence

2. Specialized Problems Diagnostic Problems Speed Consistency Training DSS and ES Building Expert Systems Knowledge Base Knowledge Engineers Creating an ES Reasoning Limitations of Expert Systems Additional Specialized Problems Pattern Recognition/Neural Nets Voice & Speech Recognition Language Comprehension Massively Parallel Computers Robotics and Motion Statistics, Uncertainty, Fuzzy Logic DSS, ES and AI Intelligence Object Orientation Agenda

3. Complex Decisions& Artificial Intelligence Strategy Decision Computer analysis of data and model. Neural network Tactics Operations Company

4. Diagnostics Speed Consistency Training Case-based reasoning Specialized Problems

5. Expert System Knowledge Base Expert Expert decisions made by non-experts Symbolic & Numeric Knowledge Rules Ifincome > 20,000 or expenses < 3000 and good credit history or . . . Then 10% chance of default

6. Link: http://www.exsysinfo.com/ Expert System ExampleBears by ExSys Type of Food: Fish Berries . . .

7. DSS and ES

8. ES Example: bank loan Welcome to the Loan Evaluation System. What is the purpose of the loan? car How much money will be loaned? 10,000 For 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

9. Decision Tree (bank loan) Payments < 10% monthly income? No Yes Other loans total < 30% monthly income? Yes Credit History Good Bad No So-so Job Stability Approve the loan Deny the loan Good Poor

10. Frame-Based ES Job History Employer, Salary, Date Hired ... ... Customer Data Name ____ Address ____ Years at address__ Co-applicant___ Rules Job History Employer, Salary, Date Hired ... ... Recommendation Lend $$$$ at ___ interest rate for ___ months, with ___ initial costs. Loan Details Purpose Boat Loan Amount _____ Time _____ Data for Boat Loans Length: Engine: Cost New: Cost Used:

11. ES Examples • United Airlines GADS: Gate Assignment • American Express Authorizer's Assistant • Stanford Mycin: Medicine/blood • DEC Order Analysis + more • Oil exploration Geological survey analysis • IRS Audit selection • Auto/Machine repair (GM:Charley) Diagnostic

12. ES Problem Suitability • Narrow, well-defined domain • Solutions require an expert • Complex logical processing • Handle missing, ill-structured data • Need a cooperative expert

13. ES Development • ES Shells • Guru • Exsys • Custom Programming • LISP • PROLOG Rules and decision trees entered by designer Forward and backward chaining by ES shell Maintained by expert system shell ES screens seen by user Expert Knowledge database (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 )) . . . ) Knowledge engineer Programmer Custom program in LISP

14. 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? Limitations of ES

15. 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 AI Research Areas

16. Neural Network: Pattern recognition Output Cells Input weights 7 3 -2 4 Hidden Layer Some of the connections Incomplete pattern/missing inputs. Sensory Input Cells

17. 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.

18. 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. Emergency Vehicles No Parking Any Time I saw the Grand Canyon flying to New York.

19. 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).

20. DSS, ES, and AI: Bank Example Decision Support System Expert System Artificial Intelligence Loan Officer Determine Rules ES Rules Data/Training Cases Income Existing loans Credit report 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. Data loan 1 data: paid loan 2 data: 5 late loan 3 data: lost loan 4 data: 1 late Lend in all but worst cases Monitor for late and missing payments. Model Neural Network Weights Name Loan #Late Amount Brown 25,000 5 1,250 Jones 62,000 1 135 Smith 83,000 3 2,435 ... Output Evaluate new data, make recommendation.

21. Software Agents • Independent • Networks/Communication • Uses • Search • Negotiate • Monitor Locate & book trip. Software agent Vacation Resorts Resort Databases

22. 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?

23. Appendix: Optimization Sample Problem You have three factories (Cheap, Intermediate, and Expensive). Output from the factories can be mixed and is measured in kilotons. You just received two orders to fill in the same time frame. How much production should come from each plant to fill the orders and minimize total costs?

24. Optimization Setup Variables: things you can control Output from each factory (c, i, e) given to each order (1, 2): Qc1, Qi1, Qe1, Qc2, Qi2, Qe2 Goal: objective function Minimize total cost: Cost = 75 Qc1 + 75 Qc2 + 100 Qi1 + 100 Qi2 + 120 Qe1 + 120 Qe2 Constraints: restrictions or limits Plant Limit C: Qc1 + Qc2 <= 100 Plant Limit I: Qi1 + Qi2 <= 100 Plant Limit E: Qe1 + Qe2 <= 100 Quantity Order 1: Qc1 + Qi1 + Qe1 = 100 Quantity Order 2: Qc2 + Qi2 + Qe2 = 150 Quality Order 1: 2.0 Qc1 + 2.5 Qi1 + 3.0 Qe1 > 250 (2.5 * 100) Quality Order 2: 2.0 Qc2 + 2.5 Qi2 + 3.0 Qe2 > 225 (1.5 * 150)

25. Spreadsheet Layout =C3*C2+D3*D2+E3*E2+F3*F2+G3*G2+H3*H2 =C4*C2+D4*D2 Put one variable in a column. Put objective function in a row. Enter formula for the objective value: sum of coefficient times variable. Put constraints in following rows with limit value. Enter each constraint formula: sum of coefficient times variable.

26. Excel Solver

27. Solution Plant output for order 1: C=28.555, I=41.327, E=30.118 Plant output for order 2: C=71.445, I=58.673, E=19.882 Total cost: 23,500 You can easily change the parameters and resolve the problem.