This Power Point Presentation can be downloaded viewed from

This Power. Point Presentation can be downloaded/viewed from my homepage at this web address: http: //www. auburn. edu/~fordfn 1/ From there, click Courses, then the link: MNGT 3140 – AI/ES F. Nelson Ford, Ph. D. Coordinator, MIS Programs Department of Management 1

Knowledge-based Decision Support and Artificial Intelligence n n Managerial Decision Makers are Knowledge Workers They Use Knowledge in Decision Making Issue: Accessibility to Knowledge-Based Decision Support Through Applied Artificial Intelligence Tools 2 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

AI Concepts and Definitions AI Involves Studying Human Thought Processes (to Understand What Intelligence Is) and Representing Thought Processes on Machines 3 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Artificial Intelligence n Artificial intelligence is behavior by a machine that, if performed by a human being, would be called intelligent (well-publicized) n Artificial intelligence Deals Primarily with Symbolic, Nonalgorithmic Methods of Problem Solving 4 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Signs of Intelligence n n 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 (Continued on next page) 5 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Signs of Intelligence (cont’d) n n n Deal with perplexing situations Understand Infer in ordinary, rational ways Apply knowledge to manipulate the environment Think and reason Recognize the relative importance of different elements in a situation 6 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Heuristic Methods for Processing Information 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 7 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Commercial Advantages of AI Over Natural Intelligence n n n n AI is more permanent AI offers ease of duplication and dissemination AI can be less expensive AI is consistent and thorough AI can be documented AI can execute certain tasks much faster than a human can AI can perform certain tasks better than many or even most people Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ 8

Natural Intelligence Advantages over AI n n n Natural intelligence is creative People use sensory experience directly Can use a wide context of experience in different situations AI - Very Narrow Focus 9 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

The Artificial Intelligence Field n Involves Many Different Sciences and Technologies – Linguistics – Psychology – Philosophy – Computer Science – Electrical Engineering – Hardware and Software 10 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Major AI Areas n Expert Systems Natural Language Processing Speech Understanding Fuzzy Logic Robotics and Sensory Systems Computer Vision and Scene Recognition Intelligent Computer-Aided Instruction Machine Learning (Neural Computing) n Intelligent Agents n n n n Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ 11

Fundamentals of Expert Systems CATS-1 at General Electric The Problem: General Electric's (GE) Top Locomotive Field Service Engineer was Nearing Retirement 12

Introduction n Expert System: from the term knowledge-based expert 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 13

The Human Element in Expert Systems n n Builder/ Knowledge engineer The Expert – Has the special knowledge, judgment, experience and methods to give advice and solve problems – Provides knowledge about task performance n User/novice 14

Structure of Expert Systems n n Development Environment Consultation Environment Three Major Components: n n n Knowledge Base Inference Engine User Interface 15

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Benefits of Expert Systems n Major Potential ES Benefits – – – – Increased Output and Productivity Decreased Decision Making Time Increased Process(es) and Product Quality Reduced Downtime Capture of Scarce Expertise Flexibility Easier Equipment Operation Elimination of the Need for Expensive Equipment 18

– Operation in Hazardous Environments – Accessibility to Knowledge and Help Desks – Increased Capabilities of Other Computerized Systems – Integration of Several Experts' Opinions – Ability to 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 – Enhancement of Other CBIS (provide intelligent capabilities to large CBIS) 19

Problems and Limitations of Expert Systems n n n 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 Users of expert systems have natural cognitive limits ES work well only in a narrow domain of knowledge 20

Types of Expert Systems n n n n n Expert Systems Versus Knowledge-based Systems Rule-based Expert Systems Frame-based Systems Automated Rule Induction Systems Case-based Systems Hybrid Systems Model-based Systems Ready-made (Off-the-Shelf) Systems Real-time Expert Systems 21

An Overview of Neural Computing n Constructing computer systems that mimic certain processing capabilities of the human brain n Knowledge representations based on – Massive parallel processing – Fast retrieval of large amounts of information – The ability to recognize patterns based on historical cases Neural Computing = Artificial Neural Networks (ANNs) 22 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

The Biology Analogy: Biological Neural Networks n Neurons: Brain Cells – Nucleus (at the center) – Dendrites provide inputs – Axons send outputs n Synapses increase or decrease connection strength and cause excitation or inhibition of subsequent neurons 23 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

24 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright

Artificial Neural Networks (ANN) n n A model that emulates a biological neural network Software simulations of the massively parallel processes that involve processing elements interconnected in a network architecture Originally proposed as a model of the human brain’s activities The human brain is much more complex 25 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Definition of Intelligent Agent n “Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program, with some degree of independence or autonomy and in so doing, employ some knowledge or representation of the user’s goals or desires. ” (“The IBM Agent” [http: //activist. gpl. ibm. com: 81/White. Pa per/ptc 2. htm]) Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ 28

Expert Systems Examples 29

1. XCON (Expert VAX System Configuration and Mass Customization) n n Digital Equipment Corp. (DEC) minicomputer system configuration Manually: Complex task, many errors, not cost effective Cost savings estimated at about $15 million / year Literature: Over $40 million / year later 30

2. MYCIN n n n To aid physicians in diagnosing meningitis and other bacterial blood infections and to prescribe treatment To aid physicians during a critical 24 -48 -hour period after the detection of symptoms, a time when much of the decision making is imprecise Early diagnosis and treatment can save a patient from brain damage or even death Stanford Medical School in the 1970 s by Dr. Edward H. Shortliffe 31

MYCIN Features n n n Rule-based knowledge representation Probabilistic rules Backward chaining method Explanation User-friendly system 32

3. Gate Assignment Display System (GADS) 33

4. Dust. Pro--Environmental Control in Mines 34