6. Degree of problem structure The Gorry and Scott Morton Grid Management levels Structured Semistructured Unstructured Operational control Management control Strategic planning Accounts receivable Order entry Inventory control Budget analysis-- engineered costs Short-term forecasting Tanker fleet mix Warehouse and factory location Production scheduling Cash management PERT/COST systems Variance analysis-- overall budget Budget preparation Sales and production Mergers and acquisitions New product planning R&D planning 13-

10. Retrieve information elements Analyze entire files Prepare reports from multiple files Estimate decision consequen-ces Propose decisions Degree of problem solving support Degree of complexity of the problem-solving system Little Much Alter’s DSS Types Make decisions 13-

12. GDSS software Mathematical Models Other group members Database GDSS software Environment Individual problem solvers Decision support system Environment Legend : Data Information Communication A DSS Model Report writing software 13-

18. Smaller Larger GROUP SIZE Face-to- face Dispersed Decision Room Local Area Decision Network Legislative Session Computer- Mediated Conference MEMBER PROXIMITY Group Size and Location Determine GDSS Environmental Settings 13-

20. Main Groupware Functions IBM TeamWARE Lotus Novell Function Workgroup Office Notes GroupWise X = standard feature O = optional feature 3 = third party offering 13-

21. Artificial Intelligence (AI) 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. 13-

23. Areas of Artificial Intelligence Expert systems AI hardware Robotics Perceptive systems (vision, hearing) Neural networks Natural language Learning Artificial Intelligence 13-

25. Know- ledge base User User interface Instructions & information Solutions & explanations Knowledge Inference engine Problem Domain Expert and knowledge engineer Development engine Expert system An Expert System Model 13-

29. Evidence Conclusion Conclusion Evidence Evidence Evidence Evidence Evidence Evidence Evidence Conclusion A Rule Set That Produces One Final Conclusion 13-

33. Rule 1 Rule 3 Rule 2 Rule 4 Rule 5 Rule 6 Rule 7 Rule 8 Rule 9 Rule 10 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 The Forward Reasoning Process T T T T T T T T T F T Legend: First pass Second pass Third pass 13-

35. 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 The First Five Problems Are Identified Rule 7 Rule 10 Rule 12 Rule 8 13- T

36. 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 7 Step 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 13-

44. Soma (processor ) Axon Synapse Dendrites (input) Axonal Paths (output) Simple Biological Neurons 13-

47. Single Artificial Neuron 13- y 1 y 2 y 3 y n-1 y w 1 w 2 w 3 w n-1

48. The Multi-Layer Perceptron Y n2 IN n OUT n OUT 1 IN 1 Y 1 Input Layer OutputLayer 13-