Artificial Intelligent Lecture 7

1. Artificial Intelligent Lecture 7 Prepared by Natalie Rose

2. An Overview of Artificial Intelligence Prepared by Natalie Rose

3. The Nature of Intelligence • Learn from experience & apply the knowledge • Handle complex situations • Solve problems when important information is missing • Determine what is important Prepared by Natalie Rose

4. The Nature of Intelligence • React quickly & correctly to new situations • Understand visual images • Process & manipulate symbols • Be creative & imaginative • Use heuristics Prepared by Natalie Rose

5. The Difference Between Natural and Artificial Intelligence Prepared by Natalie Rose

6. The Major Branches of ArtificialIntelligence Prepared by Natalie Rose

7. Artificial Intelligencein Business (Cont.) • Artificial Intelligence • Attempt to emulate the human mind in machines • Robotics • Robots used to replace human laborers • Artificial Vision • Allows robots that move in space sense obstacles • Used in machines for sorting and identification Prepared by Natalie Rose

8. Artificial Intelligencein Business (Cont.) • Natural Language Processing • Programs that recognize human commands • Expert Systems • Programs that simulate human expertise • Neural Networks • Programs built to solve problems while learning and refining their knowledge • Genetic Algorithms • Use the Darwinian principle to provide an optimal result and improve an application. Prepared by Natalie Rose

9. Artificial Intelligencein Business (Cont.) Network of Neurons Prepared by Natalie Rose

10. Artificial Intelligencein Business (Cont.) • Fuzzy Logic • Based on rules that have no discrete boundaries • More closely mimics human problem solving • Used in appliances, locomotives, managerial decision making Prepared by Natalie Rose

11. Artificial Intelligencein Business (Cont.) Prepared by Natalie Rose

12. Artificial Intelligencein Business (Cont.) Prepared by Natalie Rose

13. Artificial Intelligencein Business (Cont.) • Intelligent Agents • Automatically wade through massive amounts of data to select and deliver the most suitable information Prepared by Natalie Rose

14. Contribution ofExpert Systems • Planning • Decision making • Monitoring • Diagnosis • Training Prepared by Natalie Rose

15. Contribution ofExpert Systems (Cont.) • Incidental learning • Timely response Prepared by Natalie Rose

16. Development ofExpert Systems • What is Expertise? • Skill and knowledge whose input into a process results in performance high above the norm • Components of Expert Systems • The interface or dialog • The knowledge base • The interface engine Prepared by Natalie Rose

17. Development ofExpert Systems (Cont.) Prepared by Natalie Rose

18. Construction of Expert Systems • IF-THEN Rules • Most popular method of knowledge representation • Also called production rules • Systems hold facts in the form of IF-THEN statements Prepared by Natalie Rose

19. Construction of Expert Systems (Cont.) Prepared by Natalie Rose

20. Expert System Knowledge Base Expert decisions made by non-experts Rules Ifincome > 20,000 or expenses < 3000 and good credit history Then 10% chance of paying the loan Expert

21. 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 paying loan With ease. Forward Chaining

22. 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

23. 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

24. 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

25. 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).

26. DSS and ES

27. 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. Prepared by Natalie Rose

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

29. 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?

30. Construction of Expert Systems (Cont.) • Knowledge Engineering • Asking experts appropriate questions and translating into a knowledge base • Some ESs take years • Knowledge engineer: programmer who specializes in developing ESs Prepared by Natalie Rose

31. Construction of Expert Systems (Cont.) • Expert System Shells • Expert System that has been emptied of its knowledge • Used to build new ES • Forward Chaining • Result-driven process • Backward Chaining • Goal-driven process Prepared by Natalie Rose

32. Construction of Expert Systems (Cont.) Prepared by Natalie Rose

33. Factors Justifying the Acquisition of Expert Systems Prepared by Natalie Rose

34. Expert Systems in Action • Medical management • Telephone network maintenance • Credit evaluation • Tax planning • Detection of insider securities trading • Detection of common metals Prepared by Natalie Rose

35. Expert Systems in Action (Cont.) • Mineral exploration • Irrigation and pest management • Diagnosis and prediction of mechanical failure • Class selection for students Prepared by Natalie Rose

36. Limitations of Expert Systems • Three limitations of ESs: • Can handle only narrow domains • Do not possess common sense • Have a limited ability to learn Prepared by Natalie Rose