EXPERT SYSTEMS AND TELECOMMUNICATIONS by Tim Riley Lawrence Rowell L. Michael Guard

1. EXPERT SYSTEMS AND TELECOMMUNICATIONS by Tim Riley Lawrence Rowell L. Michael Guard

2. Why Knowledge Management? • Art of Creating value from an organizations intangible assets, namely knowledge. • Considers • Creation of knowledge • Management of knowledge • Dissemination of knowledge • Transference of knowledge

3. How ‘BIG’ is KM? • “Knowledge is power” (Sir Francis Bacon) • “We’re drowning in information and starving for knowledge” • creation of the Chief Knowledge Officer “CKO” positions (Coca-Cola, HP, Ernst & Young) $ 600,000 - 1,200,000

4. How ‘BIG’ is KM? • 1996 Survey of 150 CEO found that it is expected that knowledge-based information networks will dominate the technology environment of at least 2/3s of respondent by the year 2000. • Delphi survey - companies investing in KM will increase by 79% in the next 2 years • META Group - data mining software market potential $800 million industry by the year 2000.

6. S-Curve for Number of Developed ES Per Year

8. Knowledge Management Components • Applied Artificial Intelligence (tools and techniques for intelligent systems) • Information Technology (delivery system) • Organizational Theory (Dr. Eastman’s Class)

9. Intelligent (Decision Support) Systems Overview • Expert systems (rule-based) • Machine/Inductive Learning systems • Case-based Reasoning systems • Neural Network systems • Data Mining (AI + statistics + database)

10. Expert Systems • http://www.multilogic.com/software/software.cfm • Typically rule-based with knowledge acquisition required: IF weather is nice AND Meinhart is teaching THEN skip class AND go play golf

11. Case-based Reasoning • Concept - people reason by analogy • Old cases used to determine how a new situation should be handled using case similarities • http://www.aic.nrl.navy.mil/~aha/

12. Neural Networks • Emulate how we believe the human brain to work on a computer • Cases + ‘magic’ = system ‘trained’ to perform intelligent task • ‘Magic’ can be done using EXCEL!! • http://www.wardsystems.com/cando.htm

13. SO WHAT!?! • Companies that leverage their expertise or transform information into knowledge - $$$$$$ • People that understand these tools and techniques - $$$$$$ • Wide range of applicability for all industries

14. What is Intelligent Behavior? • learning • problem solving/decision making • goal-directed behavior • reasoning and language ability • creativity • memory • ability to generalize

15. AI vs. Natural Intelligence (NI) - Positives • AI permanent, NI perishable • AI easy to duplicate • AI less expensive • AI consistent, thorough, never tires; people get tired • AI documentable, NI not always

16. AI vs. Natural Intelligence (NI) - Negatives • NI creative, AI uninspired • NI is conscious and perceptive, AI only symbolic • NI (people) use wide area of context; AI requires (typically) a narrow domain

17. APPLIED ARTIFICIAL INTELLIGENCE • Behavior of a machine that, if performed by a human within an organization, would be called intelligent and would add value to the products and services offered by the organization. • Study of how to make computers do organizational value-added activities that, at present, people are better.

18. Artificial Intelligence “Areas” • Game Playing • Natural Language Processing • Speech Recognition Systems • Speech Generation Systems • Visual Recognition Systems • Biometrics • Robotics • Real-time Control Systems

19. Key Dates - AI • 1943 - McCullough and Pitts • neural activity modeled with Boolean algebra • 1950 - Shannon - Chess Playing Computers • Late 1950’s - Rosenblatt’s perceptron • 1956 - Dartmouth Conference which coined the term “Artificial Intelligence”

20. Key Dates - AI • Early-Classical AI • problem solving via exhaustive search • game playing/ puzzle solving • 1960 Newell/Simon’s General Problem Solver • Dark ages of AI (1965-70’s) • oversold benefits of AI and Neural Networks • disenchantment with field (Minsky, et al.)

21. Key Dates - AI • 1970’s shift away from game playing and search techniques • Human chess expert experiments (paraphrasing discussion)

22. Key Dates - AI • Mid 70’s -Early 80’s - Expert systems • 1982 - Hopfield led resurgent interest in neural networks • Present work - all kinds of advanced technologies • May 1997 - IBM beats Kasparov - applying specific knowledge of his playing style

23. AI Impact on Decision Making • Basic Steps in Problem Solving • Define the Problem • Define Evaluation Criteria • Generate Alternatives • Search for Solution and Evaluate • Choice and Recommendation • Implementation AI has had direct impact on steps 3, 4 and 5

24. Expert System Characteristics • Manipulates symbols rather than numbers • Makes inferences and deductions from information provided by the user • KNOWLEDGE is applied in solving the problem, used to guide and constrain the search for solutions • Problem area is narrow and well defined

25. An Expert, by definition ... • identifies issues relevant to the problem • solves complex problems fairly quick • explains the results and how they were arrived at • learns continuously (restructures knowledge) • applies the spirit of rules, NOT the absolute letter of the rules (knows when to apply exceptions) • degrades gracefully • is human!

26. What is expertise? • extensive, task-specific knowledge acquired from training, reading, experience, etc. • What is knowledge? • Data + processing = information • Information + processing (experience, training, etc.) = knowledge

27. An Expert is also ... • expensive • scarce • busy • inconsistent • emotional • mortal All good reasons to consider capturing his/her expertise.

28. Expert System Generic Types of Uses • Diagnosis • inferring system malfunctions from observations • prescribing remedies for malfunctions • Selection • what is the best widget given requirements (a,b,c,d,e)? • Planning and Scheduling • configuring objects under constraints • how best to meet goals

29. Expert System Components (Traditional View) User User Interface Know. Acquisition Sub-system Expert Explanation sub-system optional, automated acquisition Knowledge Base Inference Engine inputs knowledge ‘reasons’ knowledge acquisition techniques modify parameters Knowledge Engineer

30. “USES” of ES - Users • In place of expert - ES = Consultant • Novice/Trainee - ES = Instructor • Org. Knowledge? - ES = Mentor (**) • Another Expert - ES = Colleague/Partner

31. Potential Benefits of Expert Systems • Increased Productivity • Increased Quality and Consistency • Capture of scarce expertise • individual • corporate • Flexibility (Design systems) • Increased reliability • Training capabilities • Novices about an activity • People about their organization

32. Problems and Limitations • Knowledge may not be readily available (or accessible - i.e., the hesitant expert) • Difficulty in representing knowledge • Multiple Experts - Different Approaches? • Work well in only narrow domain • AI vs. NI • do not ‘degrade gracefully’

33. Problems and Limitations • Knowledge Acquisition is very difficult • Expensive knowledge engineering • Lack of User Trust (“AI”) • Expert System can • make mistakes • be stumped just like a human can!!!

34. Purpose/Motivation for ES • Capture/Disseminate Scarce Expertise • Free Up experts for ‘harder’ tasks • Educate workers • Help novice workers (supplemental tool) • Increase quality and task uniformity • Improve customer service, efficiency

35. Preferable Attributes for ES Development - Basic Req’s • Domain characterized by use of expert knowledge, judgment, experience • Narrow, self-contained domain • Experts exist today are are better than non-experts • Expertise is not or will not be available on a reliable or continuing basis

36. Preferable Attributes for ES Development - Domain Personnel • Personnel have realistic expectations • Personnel realize the expert system may not always be correct • There exists support in the organization for use of the system

37. Preferable Attributes for ES Development - Experts • One exists who is credible and has lots of experience • Expert can communicate his/her expertise • Expert will commit the time • Expert is cooperative and/or easy to work with • Multiple Experts??

38. System Design - Logical • KEY: Knowledge Engineering (Acquisition), and the management of KE process. • Knowledge Engineer needs • communication and interview skills • organizational and group dynamics skills • willingness to learn new domain • “S” on his/her chest • Knowledge sources (other than expert)

39. Expert System Tools • The market for expert system tools has grown approximately 17% per year since 1988 • There are several different programming languages and platforms used for expert systems

40. Sales of expert system development tools per year

41. Programming languages • LISP • PROLOG • OPS • C & C++ gaining popularity • Other including Pascal, LOOPS, Fortran, Smalltalk, and Basic

42. Sales of LISP tools per year

43. Expert System Shells • Greatly reduces development time by having modules already programmed • All that is needed is to add expertise • Allows for updated knowledge without interfering with other modules • Majority of ES are developed with a shell

44. Software used in ES development

45. ES tools for various platforms • Expert systems are built on PCs, workstations, minocomputers, and mainframes • PC and Mac tool sales decreased in 1991 but this is seen as positive • Workstations have steadily increased • As fear of the technology declines companies are stepping up to larger platforms and want more features

46. Platforms used in ES development

47. Sales of PC and Mac tools per year

48. Sales of workstation tools per year

49. Sales of mainframe tools per year

50. Future of ES systems • Integration with other software and computer systems such as a databases • Domain specific tools • The Internet and information retrieval