1. Knowledge-Based Systems

2. Artificial Intelligence n Definition: The activity of providing such machines as computers with the ability to display behavior that would be regarded as intelligent if it were observed in humans.

3. History n 1956, Dartmouth College. John McCarthy coined term. Same year, Logic Theorist (first AI program. Herbert Simon played a part) n Past 20 or so years, DOD and NSF have funded AI research at top schools (Stanford, Carnegie Mellon, etc.) n Desert Storm opened up new funding (smart bombs, night vision)

4. Areas of Artificial Intelligence Expertsystems AIhardware Robotics Perceptive systems systems (vision, (vision, hearing) hearing) Neural networks Natural language language Learning Artificial Intelligence

5. The Appeal of Expert Systems A computer program that attempts to code the knowledge of human experts in the form of heuristics (i.e. a rule of thumb) Two distinctions from DSS 1. has the potential to extend the manager’s problem-solving ability beyond his or her normal capabilities 2. the ability to explain how the solution was reached

6. Know- ledge base User interface Instructions & information Solutions & explanations Knowledge Inference engine Problem Domain Expert and knowledge engineer Development engine Expertsystem An Expert System Model

7. Expert system model - main parts: n User interface n Knowledge base n Inference engine n Development engine

8. User Interface n User enters: –Instructions –Information n Expert system provides: –Solutions –Explanations of »Questions »Problem solutions } Menus, commands, natural language, GUI

9. Knowledge Base n Description of problem domain n Rules: A knowledge representation technique –such as ‘IF:THEN’ logic –networks of rules »Lowest levels provide evidence »Top levels produce 1 or more conclusions »A conclusion is called a Goal variable.

10. Evidence Conclusion Evidence Conclusion A Rule Set That Produces One Final Conclusion

11. Cheetah TigerGiraffe ZebraOstrichPenguin Albatross And Tawny color Dark spots legs LongBlack strips Long neck Can’t fly Black& White Swims Flies Well UngulateBird Mammal Carnivore Or And Feathers And Or Hair milk Hoofs Flies Lays eggs Chews Gives And teeth PointedForward Eyes LEGEND: Rules Conditions Action (conclusions) Claws Eats milk cud R1R2R5R6 R9R10R11R12R14R13R15 R7R8R3R4 A Rule Set That Can Produce More Than One Final Conclusion

12. Rule Selection n Selecting rules to efficiently solve a problem is difficult n Some goals can be reached with only a few rules; rules 3 and 4 identify bird

13. Inference Engine n Two basic approaches to using rules 1. Forward reasoning (data driven) 2. Reverse reasoning (goal driven)

14. Forward Reasoning (forward chaining) n Rule is evaluated as: –(1) true, (2) false, (3) unknown n Rule evaluation is an iterative process n When no more rules can fire, the reasoning process stops even if a goal has not been reached

15. Rule 1 Rule 3 Rule 3 Rule 2 Rule 2 Rule 4 Rule 4 Rule 5 Rule 5 Rule 6 Rule 6 Rule 7 Rule 7 Rule 8 Rule 8 Rule 9 Rule 9 Rule 10 Rule 10 Rule 11 Rule 11 Rule 12 IF A THEN B IF C THEN D IF M THEN E IF K THEN F IF G THEN H IF I THEN J IF B OR D THEN K IF E THEN L IF K AND L THEN N IF M THEN O IF N OR O THEN P F IF (F AND H) OR J THEN M IF (F AND H) OR J THEN M The Forward ReasoningProcess T T T T T T T T T F T Legend: First pass Second pass Third pass

16. Reverse Reasoning (backward chaining) n Divide problem into subproblems n Try to solve one subproblem n Then try another

17. Rule 10 IF K AND L THEN N Rule 11 IF M THEN O Rule 12 IF N OR O THEN P Legend: Problem Subproblem A Problem and Its Subproblems

18. Rule 7 Rule 8 Rule 10 Subproblem Legend: Problem IF B OR D THEN K IF E THEN L IF K AND LTHEN N A Subproblem Becomes the New Problem Rule 12 IF N OR O THEN P

19. T Rule 1 Rule 2 Rule 3 Rule 9 Rule 11 Legend: Problems to be solved Step 4 Step 3 Step 2 Step 1 Step 5 IF A THEN B IF B OR D THEN K IF K AND L THEN N IF N OR O THEN P IF C THEN D IF M THEN E IF E THEN L IF (F AND H) OR J THEN M IF M THEN O IF M THEN O T The First Five Problems Are Identified Rule 7 Rule 10 Rule 12

20. If K Then F Legend: Problems to be solved If G Then H If I Then J If M Then O Step 8 Step 9 Step 7Step 6 Rule 4 Rule 5 Rule 11 Rule 6 T IF (F And H) Or J Then M T Rule 9 T T Rule 12 T If N Or O Then P The Next Four Problems Are Identified

21. Forward Versus Reverse Reasoning n Reverse reasoning is faster than forward reasoning n Reverse reasoning works best when –there are multiple goal variables –there are many rules –all or most rules do not have to be examined in the process of reaching a solution

22. Handling Uncertainty n Two types of uncertainty –Rules –Conditions n Certainty factors (CFs) range from 0.00 to 1.00

23. Development Engine n Programming languages Lisp, Prolog, and recently C++ n Expert system shells

24. Role of the Systems Analyst n Knowledge engineers work with the expert in designing expert systems n Beyond traditional analyst skills, the following skills are needed –understand how the expert applies his or her knowledge –be able to extract the description of the knowledge (rules as well as facts)

25. System Development Process n Initiate the development process n Develop the expert system prototype n User participation n Expert system maintenance

26. Prototyping Approach n A new player: the expert n Delayed user involvement n Need for maintenance

27. Systems analyst Study the Problem domain Study the problem problemdomain Define the problem Specify the rule set step 1 step 2 step 3 step 4 step 5 Test the prototype system Construct the interface Maintain the system ExpertUser Conduct user tests Use the system step 6 step 7 Prototyping Is Incorporated in the Development of an Expert System step 8 Need to redesign

28. Example: Financial Expert System n Credit approval n Knowledge base for the example consists of rules and a mathematical model n User interface n Five decision categories; credit amount influences weightings

29. Weightings of the Information Categories Financial strength 0.650.70 Payment record0.180.20 Customer background0.100.05 Geographical location0.050.03 Business potential0.020.02 Total1.001.00 $5,000 to $20,000 to Category $20,000 $50,000

30. Expert System Advantages n To managers –Consider more alternatives –Apply high level of logic –Have more time to evaluate decision rules –Consistent logic n To the firm –Better performance from management team –Retain firm’s knowledge resource

31. Expert System Disadvantages n Can’t handle inconsistent knowledge n Can’t apply judgment or intuition

32. Neural Networks n Expert systems should be able to learn, and improve their performance n Neural net design -- a bottom-up approach to modeling human intuition

33. The Human Brain n Neuron -- the information processor –Input -- dendrites –Processing -- soma –Output -- axon n Neurons are connected by the synapse

34. Soma (processor ) Axon Synapse Dendrites (input) Axonal Paths (output) Simple Biological Neurons

35. Artificial Neural Systems (ANS) n McCulloch-Pitts mathematical neuron function (late 1930s) n Hebb’s learning law (early 1940s) n Neurocomputers –Marvin Minsky’s Snark (early 1950s) –Rosenblatt’s Perceptron (mid 1950s)

36. Current Methodology n Mathematical models n Complex networks n Repetitious training -- the ANS “learns” by example. An ANS can learn; an expert system cannot.

37. y1y1 y2y2 y3y3 y n-1 y w1w1 w2w2 w3w3 w n-1 Single Artificial Neuron

38. The Multi-Layer Perceptron Y n2 IN n OUT n OUT 1 IN 1 Y1Y1Y1Y1 Input Layer OutputL ayer

39. Prerequisite Activities for the EIS Informationneeds Informationtechnologystandards Information systems plan Corporate data model Production and performance systems EIS Analysis of organization