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Slide 1: CHAPTER 10 Knowledge-Based Decision Support: Artificial Intelligence and Expert Systems 1 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 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 2
Slide 3: AI Concepts and Definitions    Encompasses Many Definitions AI Involves Studying Human Thought Processes Representing Thought Processes on Machines 3 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 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) 4 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 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]) 5 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 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 6 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 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 7 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 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 8 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 9: Symbolic Processing  Use Symbols to Represent Problem Concepts Apply Various Strategies and Rules to Manipulate these Concepts  9 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 10: AI Represents Knowledge as Sets of Symbols A symbol is a string of characters that stands for some real-world concept Examples     Product Defendant 0.8 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 10
Slide 11: Symbol Structures (Relationships)     (DEFECTIVE product) (LEASED-BY product defendant) (EQUAL (LIABILITY defendant) 0.8) tastes_good (chocolate). 11 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 12:  AI Programs Manipulate Symbols to Solve Problems Symbols and Symbol Structures Form Knowledge Representation Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem- Solving Methods 12 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ  
Slide 13: Characteristics of Artificial Intelligence   Numeric versus Symbolic Algorithmic versus Nonalgorithmic 13 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 14: Heuristic Methods for Processing Information   Search Inferencing 14 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 15: Reasoning - Inferencing from facts and rules using heuristics or other search approaches Pattern Matching - Attempt to describe objects, events, or processes in terms of their qualitative features and logical and computational relationships 15 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 16: Knowledge Processing - Given facts or other representations Knowledge Bases - Where knowledge is stored Using the Knowledge Base in AI Programs - Inferencing 16 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 17: Using the Knowledge Base Computer Inputs Knowledge Inferencing Base Capability Outputs 17 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 18: Artificial Intelligence versus Natural Intelligence 18 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 19: 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 perform certain tasks better than many or even most people 19 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 20: Natural Intelligence Advantages over AI    Natural intelligence is creative People use sensory experience directly Can use a wide context of experience in different situations AI - Very Narrow Focus 20 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 21: Information Processing   Computers can collect and process information efficiently People instinctively: – Recognize relationships between things – Sense qualities – Spot patterns indicating relationships  BUT, AI technologies can provide significant improvement in 21 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 22: AI Computing     Based on symbolic representation and manipulation A symbol is a letter, word, or number representing objects, processes, and their relationships Objects can be people, things, ideas, concepts, events, or statements of fact Creates a symbolic knowledge base 22 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 23: AI Computing (cont’d)    Manipulates symbols to generate advice AI reasons or infers with the knowledge base by search and pattern matching Hunts for answers (via algorithms) 23 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 24: AI Computing (cont’d)  Caution: AI is NOT magic AI is a unique approach to programming computers  24 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 25: Does a Computer Really Think?   WHY? WHY NOT? Dreyfus and Dreyfus [1988] say NO! The Human Mind is Very Complex Kurzweil says Soon 25 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ   
Slide 26: AI Methods are Valuable      Models of how we think Methods to apply our intelligence Can make computers easier to use Can make more knowledge available Simulate parts of the human mind 26 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 27: The AI Field  Many Different Sciences & Technologies – Linguistics – Psychology – Philosophy – Computer Science – Electrical Engineering – Hardware and Software 27 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 28: (More) – Mechanics – Hydraulics – Physics – Optics – Others  Commercial, Government and Military Organizations 28 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 29: Plus – Management and Organization Theory – Chemistry – Physics – Statistics – Mathematics – Management Science – Management Information Systems 29 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 30: Artificial Intelligence   A Science and a Technology Growing Commercial Technologies 30 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 31: 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 31 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 32: Additional AI Areas      News Summarization Language Translation Fuzzy Logic Genetic Algorithms Intelligent Software Agents 32 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 33:    Anti-lock Braking Systems Video CAMcorders Appliances – Washers – Toasters – Stoves AI Transparent in Commercial Products    Data Mining Software Help Desk Software Subway Control 33 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 34: Expert Systems   Attempt to Imitate Expert Reasoning Processes and Knowledge in Solving Specific Problems Most Popular Applied AI Technology – Enhance Productivity – Augment Work Forces  34 Narrow Problem-Solving Areas or Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 35: 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 35 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 
Slide 36: Expert Systems      Expertise Transferring Experts Inferencing Rules Explanation Capability 36 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 37: 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 37
Slide 38:      Expertise is usually associated with a high degree of intelligence, but not always with the smartest person Expertise is usually associated with a vast quantity of knowledge Experts learn from past successes and mistakes Expert knowledge is well-stored, organized and retrievable quickly from an expert 38 Experts have excellent recall Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ Some Facts about Expertise
Slide 39: Experts   Degrees or levels of expertise Nonexperts outnumber experts often by 100 to 1 39 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 40: 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 40 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 41: 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 41 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 42: Two Knowledge Types   Facts Procedures (usually rules) Regarding the Problem Domain 42 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 43: Inferencing    Reasoning (Thinking) The computer is programmed so that it can make inferences Performed by the Inference Engine 43 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 44: Rules  IF-THEN-ELSE Explanation Capability – By the justifier, or explanation subsystem ES versus Conventional Systems 44 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ  
Slide 45: Structure of Expert Systems   Development Environment Consultation (Runtime) Environment 45 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 46: Three Major ES Components    Knowledge Base Inference Engine User Interface 46 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 47: Three Major ES Components User Interface Inference Engine Knowledge Base 47 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 48: All ES Components         Knowledge Acquisition Subsystem Knowledge Base Inference Engine User Interface Blackboard (Workplace) Explanation Subsystem (Justifier) Knowledge Refining System User Most ES do not have a Knowledge Refinement Component (See Figure 10.3) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ  48
Slide 49: Knowledge Acquisition Subsystem   Knowledge acquisition is the accumulation, transfer and transformation of problemsolving expertise from experts and/or documented knowledge sources to a computer program for constructing or expanding the knowledge base Requires a knowledge engineer 49 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 50: Knowledge Base  The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems Two Basic Knowledge Base Elements – Facts – Special heuristics, or rules that direct the use of knowledge – Knowledge is the primary raw material of ES Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ  50
Slide 51: Inference Engine    The brain of the ES The control structure (rule interpreter) Provides methodology for reasoning 51 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 52: Inference Engine Major Elements    Interpreter Scheduler Consistency Enforcer 52 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 53: User Interface   Language processor for friendly, problem-oriented communication NLP, or menus and graphics 53 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 54: Blackboard (Workplace)   Area of working memory to – Describe the current problem – Record Intermediate results Records Intermediate Hypotheses and Decisions 1. Plan 2. Agenda 3. Solution 54 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 55: Explanation Subsystem (Justifier)   Traces responsibility and explains the ES behavior by interactively answering questions -Why? -How? -What? -(Where? When? Who?) Knowledge Refining System – Learning for improving performance55 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 56: The Human Element in Expert Systems     Expert Knowledge Engineer User Others 56 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 57: The Expert   Has the special knowledge, judgment, experience and methods to give advice and solve problems Provides knowledge about task performance 57 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 58: The Knowledge Engineer  Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counterexamples, and bringing to light conceptual difficulties Usually also the System Builder 58 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 
Slide 59: The User   Possible Classes of Users – A non-expert client seeking direct advice (ES acts as a Consultant or Advisor) – A student who wants to learn (Instructor) – An ES builder improving or increasing the knowledge base (Partner) – An expert (Colleague or Assistant) The Expert and the Knowledge Engineer Should Anticipate Users' Needs and 59 Limitations When Designing ES Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 60: Other Participants       System Builder Systems Analyst Tool Builder Vendors Support Staff Network Expert 60 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 61: How Expert Systems Work Major Activities of ES Construction and Use    Development Consultation Improvement 61 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 62: ES Development     Knowledge base development Knowledge separated into – Declarative (factual) knowledge and – Procedural knowledge Development (or Acquisition) of an inference engine, blackboard, explanation facility, or any other software Determine knowledge representations Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 62
Slide 63: Participants    Domain Expert Knowledge Engineer and (Possibly) Information System Analysts and Programmers 63 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 64: ES Shell   Includes All Generic ES Components But No Knowledge – EMYCIN from MYCIN – (E=Empty) 64 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 65: Expert Systems Shells Software Development Packages     Exsys InstantTea K-Vision KnowledgePro 65 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 66: Consultation   Deploy ES to Users (Typically Novices) ES Must be Very Easy to Use ES Improvement – By Rapid Prototyping 66 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 
Slide 67: An Expert System at Work Exsys Demo - Section 10.10 67 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 68: Problem Areas Addressed by Expert Systems           Interpretation systems Prediction systems Diagnostic systems Design systems Planning systems Monitoring systems Debugging systems Repair systems Instruction systems Control systems Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 68
Slide 69: Expert Systems Benefits         Increased Output and Productivity Decreased Decision Making Time Increased Process(es) and Product Quality Reduced Downtime Capture Scarce Expertise Flexibility Easier Equipment Operation Elimination of Expensive Equipment Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 69
Slide 70:            Operation in Hazardous Environments Accessibility to Knowledge and Help Desks Integration of Several Experts' Opinions Can Work with Incomplete or Uncertain Information Provide Training Enhancement of Problem Solving and Decision Making Improved Decision Making Processes Improved Decision Quality Ability to Solve Complex Problems Knowledge Transfer to Remote Locations 70 Enhancement of Other MIS Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 71: Lead to    Improved decision making Improved products and customer service Sustainable strategic advantage May enhance organization’s image 71 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 
Slide 72: Problems and Limitations of Expert Systems       Knowledge is not always readily available Expertise can be hard to extract from humans Each expert’s approach may be different, yet correct Hard, even for a highly skilled expert, to work under time pressure Expert system users have natural cognitive limits ES work well only in a narrow domain of72 knowledge Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 73:        Most experts have no independent means to validate their conclusions Experts’ vocabulary often limited and highly technical Knowledge engineers are rare and expensive Lack of trust by end-users Knowledge transfer subject to a host of perceptual and judgmental biases ES may not be able to arrive at valid conclusions ES sometimes produce incorrect 73 recommendations Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 74: Expert System Success Factors   Most Critical Factors – Champion in Management – User Involvement and Training Plus – The level of knowledge must be sufficiently high – There must be (at least) one cooperative expert – The problem to be solved must be qualitative (fuzzy), not quantitative – The problem must be sufficiently narrow in scope 74 – The ES shelland Intelligent Systems,high quality, and must be Efraim Turban and Jay E. Aronson Decision Support Systems 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 75: – A friendly user interface – The problem must be important and difficult enough – Need knowledgeable and high quality system developers with good people skills – The impact of ES as a source of endusers’ job improvement must be favorable. End user attitudes and expectations must be considered – Management support must be cultivated.   Need end-user training programs 75 Organizational environment should favor Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 76: For Success 1. Business applications justified by strategic impact (competitive advantage) 2. Well-defined and structured applications 76 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 77: Longevity of Commercial Expert Systems    Only about one-third survived five years Generally ES Failed Due to Managerial Issues – Lack of system acceptance by users – Inability to retain developers – Problems in transitioning from development to maintenance – Shifts in organizational priorities Proper management of ES development and deployment could resolve most 77 (Gill [1995]) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 78: Expert Systems Types        Expert Systems Versus Knowledgebased Systems Rule-based Expert Systems Frame-based Systems Hybrid Systems Model-based Systems Ready-made (Off-the-Shelf) Systems Real-time Expert Systems 78 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 79: Expert Systems and the Web/Internet/Intranets 1. Use of ES on the Net 2. Support ES (and other AI methods) 79 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Slide 80: Using ES on the Web     Provide knowledge and advice Help desks Knowledge acquisition Spread of multimedia-based expert systems (Intelimedia systems) Support ES and other AI technologies provided to the Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ  80