Turban Dss9e Ch12.Ppt

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Decision Support and Business Intelligence Systems th Ed., Prentice Hall) (9 Chapter 12: Artificial Intelligence and Expert Systems

Learning Objectives

Understand the basic concepts and definitions of artificial intelligence (AI) Become familiar with the AI field and its evolution Understand and appreciate the importance of knowledge in decision support Become accounted with the concepts and evolution of rule-based expert systems (ES) Understand the general architecture of rule-based expert systems Learn the knowledge engineering process, a systematic way to build ES

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Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall

Learning Objectives

Learn the benefits, limitations and critical success factors of rule-based expert systems for decision support Become familiar with proper applications of ES Learn the synergy between Web and rule-based expert systems within the context of DSS Learn about tools and technologies for developing rule-based DSS Develop familiarity with an expert system development environment via hands-on exercises

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Opening Vignette:A Web-based Expert System for Wine Selection Company background Problem description Proposed solution Results Answer and discuss the case questions12-4Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall

Artificial Intelligence (AI)

Artificial intelligence (AI)

A subfield of computer science, concerned with symbolic reasoning and problem solving

AI has many definitions

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 Theory of how the human mind works

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AI Objectives

Make machines smarter (primary goal) Understand what intelligence is Make machines more intelligent and useful Signs of intelligence

Learn or understand from experience Make sense out of ambiguous situations Respond quickly to new situations Use reasoning to solve problems Apply knowledge to manipulate the environment

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Test for IntelligenceTuring 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.- Alan Turing

Questions / Answers

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Symbolic Processing

AI represents knowledge as a set of symbols, and uses these symbols to represent problems, and apply various strategies and rules to manipulate symbols to solve problems A symbol is a string of characters that stands for some real-world concept (e.g., Product, consumer,) Examples: (DEFECTIVE product) (LEASED-BY product customer) - LISP Tastes_Good (chocolate)

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AI Concepts

Reasoning

Inferencing from facts and rules using heuristics or other search approaches Attempt to describe and match objects, events, or processes in terms of their qualitative features and logical and computational relationships

Pattern Matching

Knowledge BaseComputer INPUTS (questions, problems, etc.) Knowledge Base Inference Capability OUTPUTS (answers, alternatives, etc.)

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Evolution of artificial intelligenceHighEmbedded Applications

Complexity of the Solutions

Hybrid Solutions

Domain Knowledge

General Methoids

Nave Solutions

Low 1960s 1970s 1980s 1990s 2000+ Time

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Artificial vs. Natural Intelligence

Advantages of AI

More permanent Ease of duplication and dissemination Less expensive Consistent and thorough Can be documented Can execute certain tasks much faster Can perform certain tasks better than many people Is truly creative Can use sensory input directly and creatively Can apply experience in different situations

Advantages of Biological Natural Intelligence

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The AI Field AI is many different sciences and technologies It is a collection of concepts and ideas

Linguistics Psychology Philosophy Computer Science Electrical Engineering Mechanics Hydraulics Physics Optics Management and Organization Theory Chemistry

Chemistry Physics Statistics Mathematics Management Science Management Information Systems Computer hardware and software Commercial, Government and Military Organizations

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The AI FieldIntelligent tutoring

AI provides the scientific foundation for many commercial technologies

Autonomous Robots Speech Understanding Automatic Programming Computer VisionApplications

Intelligent Agents Natural Language Processing Voice Recognition

Machine Learning

Neural Networks Genetic Algorithms

Game Playing Expert Systems The AI Tree

Fuzzy Logic

Philosophy Human BehaviorDisciplines

Mathematics Computer Science Engineering Logic Robotics Management Science Information Systems Statistics Pattern Recognition

Neurology Sociology Psychology Human Cognition

Linguistics

Biology

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AI Areas

Major

Expert Systems Natural Language Processing Speech Understanding Robotics and Sensory Systems Computer Vision and Scene Recognition Intelligent Computer-Aided Instruction Automated Programming Neural Computing Game Playing

Additional

Game Playing, Language Translation Fuzzy Logic, Genetic Algorithms Intelligent Software Agents

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AI is often transparent in many commercial products

Anti-lock Braking Systems (ABS) Automatic Transmissions Video Camcorders Appliances

Washers, Toasters, Stoves

Help Desk Software Subway Control

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Expert Systems (ES)

Is a computer program that attempts to imitate experts reasoning processes and knowledge in solving specific problems Most Popular Applied AI Technology

Enhance Productivity Augment Work Forces

Works best with narrow problem areas/tasks Expert systems do not replace experts, but

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Make their knowledge and experience more widely available, and thus Permit non-experts to work better

Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall

Important Concepts in ES

ExpertA human being who has developed a high level of proficiency in making judgments in a specific domain

ExpertiseThe set of capabilities that underlines the performance of human experts, including

extensive domain knowledge, heuristic rules that simplify and improve approaches to problem solving, meta-knowledge and meta-cognition, and compiled forms of behavior that afford great economy in a skilled performance

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Important Concepts in ES

Experts

Degrees or levels of expertise Nonexperts outnumber experts often by 100 to 1 From expert to computer to nonexperts via acquisition, representation, inferencing, transfer Knowledge = Facts + Procedures (Rules) Reasoning/thinking performed by a computer

Transferring Expertise

Inferencing

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Rules (IF THEN ) Explanation Capability (Why? How?)

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Applications of Expert Systems

DENDRAL

Applied knowledge (i.e., rule-based reasoning) Deduced likely molecular structure of compounds A rule-based expert system Used for diagnosing and treating bacterial infections A rule-based expert system Used to determine the optimal information systems configuration

MYCIN

XCON

New applications: Credit analysis, Marketing,Finance, Manufacturing, Human resources, Science and Engineering, Education,

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D E ev nv e l ir op on m m en en t t

Structures of Expert SystemsC En on v i s ul ro ta nm tio en n t

Human Expert(s)Knowledge Elicitation

Other Knowledge SourcesInformation Gathering

1. 2.

Development Environment Consultation (Runtime) Environment

Knowledge Rules

Knowledge EngineerInferencing Rules

Knowledge Base(s) (Long Term)Rule Firings

Questions / Answers

Inference Engine Explanation Facility Knowledge Refinement

User

User Interface

Refined Rules

Blackboard (Workspace)Facts Data / Information

Facts

Working Memory (Short Term)

External Data Sources (via WWW)

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Conceptual Architecture of a Typical Expert SystemsModeling of Manufacturing Systems Abstract ajshjaskahskaskjhakjshakhska akjsja s askjaskjakskjas

Expert(s)

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