1. Chapter 13 Artificial Intelligence and Expert Systems

2. 2 Learning Objectives When you finish this chapter, you will Know the basic concepts of artificial intelligence. Know examples of how technology has been used in business and other professional domains, and how it can be further used in the future. Understand expertise, the purpose of expert systems in business and other professional domains, and why expert systems are so helpful in solving unstructured problems.

3. 3 Learning Objectives Recognize the major methods of representing knowledge in software. Understand the challenges involved in garnering knowledge for the construction of knowledge bases. Be able to explain the concept of knowledge engineering.

4. 4 Artificial Intelligence in Business Figure 13.1 The various research efforts in artificial intelligence

5. 5 Artificial Intelligence in Business 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 Natural Language Process Expert Systems Neural Networks

6. 6 Artificial Intelligence in Business Natural Language Process 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

7. 7 Artificial Intelligence in Business Figure 13.2 Neural nets simulate the association and inference that take place in a network of neurons in the human brain. Instead of a network of neurons, a network of nodes is developed.

8. 8 Artificial Intelligence in Business Fuzzy Logic Based on rules that have no discrete boundaries More closely mimics human problem solving Used in appliances, locomotives, managerial decision making

9. 9 Artificial Intelligence in Business Figure 13.3 The function young

10. 10 Artificial Intelligence in Business Figure 13.4 The functions young, middle-aged, and old

11. 11 Artificial Intelligence in Business Genetic Algorithms Mathematical functions that use Darwinian principals to improve an application Intelligent Agents Automatically wade through massive amounts of data to select and deliver the most suitable information

12. 12 Artificial Intelligence in Business Figure 13.5 Genetic algorithms are used to produce a combination of factors that combine to provide an optimal result. In this figure, spheres represent factors and their weights, and the combination with the darkest shading is the fittest solution.

13. 13 Contribution of Expert Systems Figure 13.6 Major reasons for using expert systems

14. 14 Contribution of Expert Systems Areas where ESs can help in business Planning Decision making Monitoring Diagnosis Training Incidental learning Replication of expertise Timely response Consistent solutions

15. 15 Development of Expert 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

16. 16 Development of Expert Systems Figure 13.7 Components of an expert system; numbers indicate the order of the processes

17. 17 Knowledge Representation Methods IF-THEN Rules Most popular method of knowledge representation Also called production rules Systems hold facts in the form of IF-THEN statements

18. 18 Figure 13.8 In IF-THEN rules, different combinations of conditions lead to different conclusions. Knowledge Representation Methods

19. 19 Frames Knowledge organized as facts in tables Figure 13.9 A frame describing a dog Knowledge Representation Methods

20. 20 Semantic Nets Knowledge stored as objects and the relationships among them Figure 13.10 A semantic net that determines if an animal is a cat or a dog Knowledge Representation Methods

21. 21 Knowledge Engineering Asking experts appropriate questions and translating into a knowledge base Some ESs take years Knowledge engineer: programmer who specializes in developing ESs Knowledge Representation Methods

22. 22 Figure 13.11 A knowledge engineer must know what to ask, how to ask, and how to organize the answers into a knowledge base. Knowledge Representation Methods

23. 23 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 Knowledge Representation Methods

24. 24 Figure 13.12 Forward chaining and backward chaining in an expert system Knowledge Representation Methods

25. 25 Factors Justifying the Acquisition of Expert Systems Figure 13.13 What justifies the acquisition of an ES? Knowledge Representation Methods

26. 26 Expert Systems in Action Business areas using ESs Telephone network maintenance Credit evaluation Tax planning Detection of insider securities trading Mineral exploration

27. 27 Expert Systems in Action Irrigation and pest management Predicting failure of diesel engines Medical diagnosis Class selection for students

28. 28 Limitations of Expert Systems Three limitations of ESs Can handle only narrow domains Do not possess common sense Have a limited ability to learn

29. 29 Ethical and Societal Issues Too Sophisticated Technology Increasing dependence on machine intelligence raises legal and ethical issues. Who is legally responsible for advice provided by a program? Is expert judgment needed to interpret program output? Does machine expertise replace or complement the ‘real thing’? How do we know if the experts behind expert systems are expert at all?

30. 30 Ethical and Societal Issues Too Sophisticated Technology Malfunctions of an ES can be caused by anyone involved in the development. Experts who contribute knowledge Knowledge engineer who builds the system Professional who uses the ES The person who is affected by the decision