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CHAPTER 10

CHAPTER 10. Knowledge-Based Decision Support: Artificial Intelligence and Expert Systems. Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ.

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CHAPTER 10

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  1. CHAPTER 10 Knowledge-Based Decision Support: Artificial Intelligence and Expert Systems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  2. Knowledge-Based Decision Support: Artificial Intelligence and Expert Systems • Managerial Decision Makers are Knowledge Workers • Use Knowledge in Decision Making • Accessibility to Knowledge Issue • Knowledge-Based Decision Support: Applied Artificial Intelligence Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  3. AI Concepts and Definitions • Many Definitions • AI Involves Studying Human Thought Processes • Representing Thought Processes on Machines Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  4. Artificial Intelligence • Behavior by a machine that, if performed by a human being, would be considered intelligent • “…study of how to make computers do things at which, at the moment, people are better” (Rich and Knight [1991]) • Theory of how the human mind works (Mark Fox) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  5. AI Objectives • Make machines smarter (primary goal) • Understand what intelligence is (Nobel Laureate purpose) • Make machines more useful (entrepreneurial purpose) (Winston and Prendergast [1984]) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  6. Signs of Intelligence • Learn or understand from experience • Make sense out of ambiguous or contradictory messages • Respond quickly and successfully to new situations • Use reasoning to solve problems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  7. More Signs of Intelligence • Deal with perplexing situations • Understand and infer in ordinary, rational ways • Apply knowledge to manipulate the environment • Think and reason • Recognize the relative importance of different elements in a situation Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  8. Turing Test for Intelligence A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, can not determine which is which Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  9. AI Represents Knowledge as Sets of Symbols A symbolis a string of characters that stands for some real-world concept Examples • Product • Defendant • 0.8 • Chocolate Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  10. Symbol Structures (Relationships) • (DEFECTIVE product) • (LEASED-BY product defendant) • (EQUAL (LIABILITY defendant) 0.8) • tastes_good (chocolate). Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  11. AI Programs Manipulate Symbols to Solve Problems • Symbols and Symbol Structures Form Knowledge Representation • Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem-Solving Methods Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  12. Characteristics of Artificial Intelligence • Numeric versus Symbolic • Algorithmic versus Nonalgorithmic Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  13. Heuristic Methods for Processing Information • Search • Inferencing “AI is the branch of computer science that deals with ways of representing knowledge using symbols rather than numbers and with rules-of-thumb, or heuristic, methods for processing information”.

  14. AI Advantages Over Natural Intelligence • More permanent • Ease of duplication and dissemination • Less expensive • Consistent and thorough • Can be documented • Can execute certain tasks much faster than a human can • Can perform certain tasks better than many or even most people Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  15. Natural Intelligence Advantages over AI • Natural intelligence is creative • People use sensory experience directly • Can use a widecontext of experience in different situations AI - Very Narrow Focus Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  16. Knowledge in Artificial Intelligence Knowledge encompasses the implicit and explicit restrictions placed upon objects (entities), operations, and relationships along with general and specific heuristics and inference procedures involved in the situation being modeled Of data, information, and knowledge, KNOWLEDGE is most abstract and in the smallest quantity Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  17. Uses of Knowledge • Knowledge consists of facts, concepts, theories, heuristic methods, procedures, and relationships • Knowledge is also information organized and analyzed for understanding and applicable to problem solving or decision making • Knowledge base - the collection of knowledge related to a problem (or opportunity) used in an AI system • Typically limited in some specific, usually narrow, subject area or domain • The narrow domain of knowledge, and that an AI system must involve some qualitative aspects of decision making (critical for AI application success) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  18. Knowledge Bases • Search the Knowledge Base for Relevant Facts and Relationships • Reach One or More Alternative Solutions to a Problem • Augments the User (Typically a Novice) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  19. How Artificial Intelligence Differs from Conventional Computing Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  20. Conventional Computing • Based on an Algorithm(clearly defined, step-by-step procedure) • Mathematical Formula or Sequential Procedure • Converted into a Computer Program • Uses Data (Numbers, Letters, Words) • Limited to Very Structured, Quantitative Applications (Table 10.1)

  21. Table 10.1: How Conventional Computers Process Data • Calculate • Perform Logic • Store • Retrieve • Translate • Sort • Edit • Make Structured Decisions • Monitor • Control Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  22. AI Computing • Based on symbolic representation and manipulation • A symbol is a letter, word, or number represents objects, processes, and their relationships • Objects can be people, things, ideas, concepts, events, or statements of fact • Create a symbolic knowledge base Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  23. AI Computing (cont’d) • Uses various processes to manipulate the symbols to generate advice or a recommendation • AI reasons or infers with the knowledge base by search and pattern matching • Hunts for answers (Algorithms often used in search) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  24. AI Computing (cont’d) • Caution: AI is NOT magic • AI is a unique approach to programming computers (Table 6.2) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  25. Table 6.2: Artificial Intelligence vs. Conventional Programming Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

  26. Major AI Areas • Expert Systems • Natural Language Processing • Speech Understanding • Robotics and Sensory Systems • Computer Vision and Scene Recognition • Intelligent Computer-Aided Instruction • Neural Computing Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  27. Additional AI Areas • News Summarization • Language Translation • Fuzzy Logic • Genetic Algorithms • Intelligent Software Agents Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  28. An Expert System Solution General Electric's (GE) : Top Locomotive Field Service Engineer was Nearing Retirement Traditional Solution: Apprenticeship but would like • A more effective and dependable way to disseminate expertise • To prevent valuable knowledge from retiring • To minimize extensive travel or moving the locomotives • To MODEL the way a human troubleshooter works • Months of knowledge acquisition • 3 years of prototyping • A novice engineer or technician can perform at an expert’s level • On a personal computer • Installed at every railroad repair shop served by GE 3

  29. (ES) Introduction • Expert System vs. knowledge-based system • An Expert System is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise • ES imitate the expert’s reasoning processes to solve specific problems 4

  30. History of Expert Systems 1. Early to Mid-1960s • One attempt: the General-purpose Problem Solver (GPS) • General-purpose Problem Solver (GPS) • A procedure developed by Newell and Simon [1973] from their Logic Theory Machine - • Attempted to create an "intelligent" computer • general problem-solving methods applicable across domains • Predecessor to ES • Not successful, but a good start 5

  31. 2. Mid-1960s: Special-purpose ES programs • DENDRAL • MYCIN • Researchers recognized that the problem-solving mechanism is only a small part of a complete, intelligent computer system • General problem solvers cannot be used to build high performance ES • Human problem solvers are good only if they operate in a very narrow domain • Expert systems must be constantly updated with new information • The complexity of problems requires a considerable amount of knowledge about the problem area 6

  32. 3. Mid 1970s • Several Real Expert Systems Emerge • Recognition of the Central Role of Knowledge • AI Scientists Develop • Comprehensive knowledge representation theories • General-purpose, decision-making procedures and inferences • Limited Success Because • Knowledge is Too Broad and Diverse • Efforts to Solve Fairly General Knowledge-Based Problems were Premature 7

  33. BUT • Several knowledge representations worked Key Insight • The power of an ES is derived from the specific knowledge it possesses, not from the particular formalisms and inference schemes it employs 8

  34. 4. Early 1980s • ES Technology Starts to go Commercial • XCON • XSEL • CATS-1 • Programming Tools and Shells Appear • EMYCIN • EXPERT • META-DENDRAL • EURISKO • About 1/3 of These Systems Are Very Successful and Are Still in Use 9

  35. Latest ES Developments • Many tools to expedite the construction of ES at a reduced cost • Dissemination of ES in thousands of organizations • Extensive integration of ES with other CBIS • Increased use of expert systems in many tasks • Use of ES technology to expedite IS construction (ES Shell) 10

  36. The object-oriented programming approach in knowledge representation • Complex systems with multiple knowledge sources, multiple lines of reasoning, and fuzzy information • Use of multiple knowledge bases • Improvements in knowledge acquisition • Larger storage and faster processing computers • The Internet to disseminate software and expertise. 11

  37. Expert Systems • Attempt to Imitate Expert Reasoning Processes and Knowledge in Solving Specific Problems • Most Popular Applied AI Technology • Enhance Productivity • Augment Work Forces • Narrow Problem-Solving Areas or Tasks Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  38. Expert Systems • Provide Direct Application of Expertise • Expert Systems Do Not Replace Experts, But They • Make their Knowledge and Experience More Widely Available • Permit Nonexperts to Work Better Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  39. Expert Systems • Expertise • Transferring Experts • Inferencing • Rules • Explanation Capability Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  40. Expertise • The extensive, task-specific knowledge acquired from training, reading and experience • Theories about the problem area • Hard-and-fast rules and procedures • Rules (heuristics) • Global strategies • Meta-knowledge (knowledge about knowledge) • Facts • Enables experts to be better and faster than nonexperts Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  41. Human Expert Behaviors • Recognize and formulate the problem • Solve problems quickly and properly • Explain the solution • Learn from experience • Restructure knowledge • Break rules • Determine relevance • Degrade gracefully Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  42. Human Experts • Knowledge acquisition from human experts the “paradox of expertise” 17

  43. Transferring Expertise • Objective of an expert system • To transfer expertise from an expert to a computer system and • Then on to other humans (nonexperts) • Activities • Knowledge acquisition • Knowledge representation • Knowledge inferencing • Knowledge transfer to the user • Knowledge is stored in a knowledge base Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  44. Two Knowledge Types • Facts • Procedures (usually rules) Regarding the Problem Domain Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  45. Inferencing • Reasoning (Thinking) • The computer is programmed so that it can make inferences • Performed by the Inference Engine Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  46. Rules • IF-THEN-ELSE • Explanation Capability • By the justifier, or explanation subsystem • ES versus Conventional Systems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  47. Knowledge as Rules • MYCIN rule example: IF the infection is meningitis AND patient has evidence of serious skin or soft tissue infection AND organisms were not seen on the stain of the culture AND type of infection is bacterial THEN There is evidence that the organism (other than those seen on cultures or smears) causin the infection is Staphylococus coagpus. 22

  48. Structure of Expert Systems • Development Environment • Consultation (Runtime) Environment Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  49. Inference Engine Three Major ES Components User Interface Knowledge Base Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

  50. ES Shell Working Memory User Interface Inference Engine Knowledge Base Explanation Facility Knowledge Acquisition Database, Spreadsheets, etc. Basic ES Structure 26

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