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Chapter 11:

Automated Decision Systems and Expert Systems

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

Business Intelligence and Analytics: Systems for Decision Support

(10th Edition)

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Learning Objectives

Understand the concept and applications of automated rule-based decision systems

Understand the importance of knowledge in decision support

Describe the concept and evolution of rule-based expert systems (ES)

Understand the architecture of rule-based ES

Learn the knowledge engineering process used to build ES

(Continued…)

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Learning Objectives

Explain the benefits and limitations of rule-based systems for decision support

Identify proper applications of ES

Learn about tools and technologies for developing rule-based DSS

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Opening Vignette…

InterContinental Hotel Group Uses

Decision Rules for Optimal Hotel Room Rates

Company background

Problem description

Proposed solution

Results

Answer & discuss the case questions...

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Questions for the Opening Vignette

Describe the challenges faced by IHG during development of their retail price optimization system.

Besides the hotel business in the hospitality industry, explain at least three other areas where an optimization model could be used.

What other methods could be used to solve IHG’s price optimization problem?

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Automated Decision Systems

A relatively new approach to supporting decision making

a.k.a. Decision Automation Systems (DAS)

Often a rule-based system that provides a solution in a functional area

“If only 70 percent of the seats on a flight from LA to NY are sold 3 days prior to departure, offer a discount of x to nonbusiness travelers”

Applies to repetitive/structured decisions

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Application Case 11.1

Giant Food Stores Prices the Entire Store

Company background

Problem description

Proposed solution

Results

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Automated Decision-Making Framework

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Architecture of the Airline Revenue Management Systems

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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

Artificial Intelligence (AI)

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Make machines smarter (primary goal)

Understand what intelligence is

Make machines more intelligent & 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

AI Objectives

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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.

- Alan Turing

Test for Intelligence

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The AI Field…

AI provides the scientific foundation for many commercial technologies

The Disciplines and Applications of AI.

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Major…

Expert Systems

Natural Language Processing

Robotics and Sensory Systems

Computer Vision and Scene Recognition

Intelligent Computer-Aided Instruction

Automated Programming, Neural Computing

Additional…

Fuzzy Logic, Genetic Algorithms

Game Playing, Intelligent Software Agents …

AI Areas

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Anti-lock Braking Systems (ABS)

Automatic Transmissions

Video Camcorders

Appliances

Washers, Toasters, Stoves, …

Help Desk Software

Subway Control

…

AI is Often Transparent in Many Commercial Products

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Is a computer program that attempts to imitate expert’s 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

Make their knowledge and experience more widely available, and thus

Permit non-experts to work better

Expert Systems (ES)

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Expert

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

Expertise

The 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

Important Concepts in ES

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Experts / Expertise

Degrees or levels of expertise

Ratio of non-experts to experts  100 to 1

Transferring Expertise

From expert to computer to nonexperts via acquisition, representation, inferencing, transfer

Symbolic Reasoning / Inferencing

Deep Knowledge / Self Knowledge

Features and Concepts in ES

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Conventional vs. Expert Systems

Continued…

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Conventional vs. Expert Systems

…

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Application Case 11.2

Expert System Helps in Identifying Sport Talents

Background

Problem description

Proposed solution

Results

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Applications of Expert Systems

Classical Applications

DENDRAL

Applied knowledge (i.e., rule-based reasoning)

Deduced likely molecular structure of compounds

MYCIN

A rule-based expert system

Used for diagnosing and treating bacterial infections

XCON

A rule-based expert system

Used to determine the optimal information systems configuration

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

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Applications of Expert Systems

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Application Case 11.3

Expert System Aids in Identification of Chemical, Biological, and Radiological Agents

Questions for Discussion

How can CBR Advisor assist in making quick decisions?

What characteristics of CBR Advisor make it an expert system?

What could be other situations where such expert systems can be employed?

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Structure of Expert Systems

Development Environment

Consultation Environment

Major Components

Knowledge acquisition subsystem

Knowledge Engineer

Knowledge Base

Inference Engine

User Interface

Blackboard (workplace)

Explanation subsystem (justifier)

Knowledge-refining system

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Structures of Expert Systems

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Application Case 11.4

Diagnosing Heart Diseases by Signal Processing

Questions for Discussion

List the major components involved in building SIPMES and briefly comment on them.

Do expert systems like SIPMES eliminate the need for human decision making?

How often do you think that the existing expert systems, once built, should be changed?

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Knowledge Engineering (KE)

A set of intensive activities encompassing the acquisition of knowledge from human experts (and other information sources) and converting this knowledge into a repository (commonly called a knowledge base)

The primary goal of KE is to

help experts articulate how they do what they do, and

to document this knowledge in a reusable form

Narrow versus Broad definition of KE?

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The Knowledge Engineering Process

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Difficulties in KE

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Knowledge Engineering Knowledge Validation/Verification

Evaluation is a broad concept - its objective is to assess an ES’s overall value

Validation versus Verification

Validation is the part of evaluation that deals with the performance of the system

Verification is building the system right or substantiating that the system is correctly implemented to its specifications

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Knowledge Representation in ES

Expert knowledge must be represented in a computer-understandable format and organized properly in the knowledge base

The most common/popular way to represent human knowledge:

Production rules

Condition-Action pairs

IF … THEN … ELSE …

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IF premise, THEN conclusion

IF your income is high, THEN your chance of being audited by the IRS is high

Conclusion, IF premise

Your chance of being audited is high, IF your income is high

Inclusion of ELSE

IF your income is high, OR your deductions are unusual, THEN your chance of being audited by the IRS is high, ELSE your chance of being audited is low

More complex rules…

Forms of Production Rules

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Knowledge and Inference Rules

Knowledge rules (declarative rules), state all the facts and relationships about a problem

Knowledge rules are stored in the knowledge base

Inference rules (procedural rules), advise on how to solve a problem, given that certain facts are known

Inference rules contain rules about rules (metarules)

Inference rules become part of the inference engine

Example:

IF needed data is not known THEN ask the user

IF more than one rule applies THEN fire the one with the highest priority value first

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Inferencing in ES

Inference is the process of chaining multiple rules together based on available data

Forward chaining

A data-driven search in a rule-based system.

If the premise clauses match the situation, then the process attempts to assert the conclusion.

Backward chaining

A goal-driven search in a rule-based system.

It begins with the action clause of a rule and works backward through a chain of rules in an attempt to find a verifiable set of condition clauses.

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Inferencing with Rules: Forward and Backward Chaining

Firing a rule

When all of the rule's hypotheses (the “if parts”) are satisfied, a rule said to be FIRED

Inference engine checks every rule in the knowledge base in a forward or backward direction to find rules that can be FIRED

Continues until no more rules can fire, or until a goal is achieved

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Goal-driven: Start from a potential conclusion (hypothesis), then seek evidence that supports (or contradicts with) it

Often involves formulating and testing intermediate hypotheses (or sub-hypotheses)

Inferencing – Backward Chaining

Investment Decision: Variable Definitions

A = Have $10,000

B = Younger than 30

C = Education at college level

D = Annual income > $40,000

E = Invest in securities

F = Invest in growth stocks

G = Invest in IBM stock

Knowledge Base

Rule 1: A & C -> E

Rule 2: D & C -> F

Rule 3: B & E -> F (invest in growth stocks)

Rule 4: B -> C

Rule 5: F -> G (invest in IBM)

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Data-driven: Start from available information as it becomes available, then try to draw conclusions

Which One to Use?

If all facts available up front - forward chaining

Diagnostic problems - backward chaining

Inferencing – Forward Chaining

FACTS:

A is TRUE

B is TRUE

Knowledge Base

Rule 1: A & C -> E

Rule 2: D & C -> F

Rule 3: B & E -> F (invest in growth stocks)

Rule 4: B -> C

Rule 5: F -> G (invest in IBM)

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Inferencing Issues

How do we choose between BC and FC

Follow how a domain expert solves the problem

If the expert first collect data then infer from it

=> Forward Chaining

If the expert starts with a hypothetical solution and then attempts to find facts to prove it => Backward Chaining

How to handle conflicting rules

IF A & B THEN C

IF X THEN C

Establish a goal and stop firing rules when goal is achieved

Fire the rule with the highest priority

Fire the most specific rule

Fire the rule that uses the data most recently entered

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Inferencing with Uncertainty - Theory of Certainty

Certainty Factors and Beliefs

Uncertainty is represented as a Degree of Belief

Express the Measure of Belief

Manipulate degrees of belief while using knowledge-based systems

Certainty Factors (CF) express belief in an event based on evidence (or the expert's assessment)

1.0 or 100 = absolute truth (complete confidence)

0 = certain falsehood

CFs are NOT probabilities

CFs need not sum to 100

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Inferencing with Uncertainty Combining Certainty Factors

Combining Several Certainty Factors in One Rule where parts are combined using AND and OR logical operators

AND

IF inflation is high, CF = 50 percent, (A), AND

unemployment rate is above 7, CF = 70 percent, (B), AND

bond prices decline, CF = 100 percent, (C)

THEN stock prices decline

CF(A, B, and C) = Minimum[CF(A), CF(B), CF(C)]

=> The CF for “stock prices to decline” = 50 percent

The chain is as strong as its weakest link

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Inferencing with Uncertainty Combining Certainty Factors

IF inflation is low, CF = 70 percent, (A), OR

bond prices are high, CF = 85 percent, (B)

THEN stock prices will be high

CF(A, B) = Maximum[CF(A), CF(B)]

=> The CF for “stock prices to be high” = 85 percent

Notice that in OR only one IF premise need to be true

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Combining two or more rules

Example:

R1: IF the inflation rate is less than 5 percent,

THEN stock market prices go up (CF = 0.7)

R2: IF unemployment level is less than 7 percent,

THEN stock market prices go up (CF = 0.6)

Inflation rate = 4 percent and the unemployment level = 6.5 percent

Combined Effect

CF(R1,R2) = CF(R1) + CF(R2)[1 - CF(R1)]; or

CF(R1,R2) = CF(R1) + CF(R2) - CF(R1)  CF(R2)

Inferencing with Uncertainty Combining Certainty Factors

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Explanation

Human experts justify and explain their actions

… so should ES

Explanation: an attempt by an ES to clarify reasoning, recommendations, other actions (asking a question)

Explanation facility = Justifier

Explanation Purposes…

Make the system more intelligible

Uncover shortcomings of the knowledge bases

Explain unanticipated situations

Satisfy users’ psychological and/or social needs, …

Explanation as a Metaknowledge

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Two Basic Explanations

Why Explanations - Why is a fact requested?

How Explanations - To determine how a certain conclusion or recommendation was reached

Some simple systems - only at the final conclusion

Most complex systems provide the chain of rules used to reach the conclusion

Explanation is essential in ES

Used for training and evaluation

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Problem Areas Suitable For Expert Systems

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Development of ES

Defining the nature and scope of the problem

Identifying proper experts

Acquiring knowledge

Knowledge engineer

Selecting the Building Tools

Shells versus Complete Development

Coding the system

Evaluating and Launching the System

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A Popular Expert System Shell

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Application Case 11.5

Clinical Decision Support System for Tendon Injuries

Questions for Discussion

Research other expert systems in other domains and list a few of them.

Why is important to evaluate the expert systems before they are put into use?

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Interpretation systems

Prediction systems

Diagnostic systems

Repair systems

Design systems

Planning systems

Monitoring systems

Debugging systems

Instruction systems

Control systems, …

Problem Areas Addressed by ES

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End of the Chapter

Questions, comments

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All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Printed in the United States of America.

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Questions / Answers

Psychology

Philosophy

Logic

Sociology

Human Cognition

Linguistics

Neurology

Mathematics

Management Science

Information Systems

Statistics

Engineering

Robotics

Biology

Human Behavior

Pattern Recognition

Voice Recognition

Intelligent tutoring

Expert Systems

Neural Networks

Natural Language Processing

Intelligent Agents

Fuzzy Logic

Game Playing

Computer Vision

Automatic Programming

Genetic Algorithms

Machine Learning

Autonomous Robots

Speech Understanding

The AI

Tree

Computer Science

Inference Engine

Working

Memory

(Short Term)

Explanation

Facility

Knowledge

Refinement

Blackboard (Workspace)

External Data

Sources

(via WWW)

Knowledge

Engineer

Human

Expert(s)

Other Knowledge

Sources

Knowledge

Elicitation

Information

Gathering

Knowledge

Base(s)

(Long Term)

User

Interface

Facts

Questions

/ Answers

Rule

Firings

Knowledge

Rules

Inferencing

Rules

Facts

Data /

Information

Refined

Rules

Knowledge

Acquisition

Knowledge

Representation

Knowledge

Validation

Inferencing

(Reasoning)

Explanation &

JustificationFeedback loop (corrections and refinements)

Raw

knowledge

Codified

knowledge

Validated

knowledge