Expert Systems and Their Applications

John PaxtonMontana State University

August 14, 2003

Bozeman

Definitions

• A model and associated procedure that exhibits, within a specific domain, a degree of expertise in problem solving that is comparable to that of a human expert. (Ignizio)

• An expert system is a computer system which emulates the decision-making ability of a human expert. (Giarratono)

Characteristics

• Operates in a narrow domain• Separates knowledge from processing• Can explain how a particular conclusion is

reached• Can explain why specific data is needed• Permits inexact reasoning• Can make mistakes• Changes are easy to incorporate

Components

• Program = Algorithm + Data Structure

• Expert System = Inference Engine + Knowledge

Usage (2002)

Area PercentageProduction/Ops Mgmt 48%Finance 17%Information Systems 12%Marketing/Transactions 10%Accounting/Auditing 6%International Business 3%Human Resources 2%Other 2%

Why Use an Expert System?

1. Helps preserve knowledge--builds up the corporate memory of the firm.

2. Helps if expertise is scarce, expensive, or unavailable.

3. Helps if under time and pressure constraints.

4. Helps in training new employees. 5. Helps improve worker productivity.

Architecture

USERINTERFACE

KNOWLEDGEBASE

INFERENCEENGINE

WORKINGMEMORY

Knowledge Base

• Contains facts

antacid (Imodium)

• Contains rules

if traveler (x) and stomach-pains (x)then take (y, antacid (y))

Inference Engine

• Rules that match working memory are identified and then fired.

• This updates working memory and the knowledge base.

• The process is repeated.

Inference Engine

• Conflict Resolution

– fire all matching rules– fire the first matching rule– fire the highest priority matching rule– fire the most specific rule– fire the rule that uses the most recent data

Inference Engine

• Forward Chaining. Starting with the data, a conclusion is reached.

cat (Mulder) cat (x) mammal (x)

• Backward Chaining. Starting with a hypothesis, it works backwards to the data.

Uncertainty Sources

• Weak implications

• Imprecise language (e.g. “often”)

• Unknown data

• Combining views of different experts

Uncertainty

• Certainty Factors.

• Dempster-Shafer Theory.

• Bayesian Networks.

• Fuzzy Logic.

Certainty Factors

IF the light is greenTHEN it is ok to cross the street cf = 0.9

+ easy to compute+ easy to propagate- somewhat ad hoc- all certainty factors are independent

Bayesian Reasoning

Based on Baye’s Theorem and standard probability theory

P(H|E) = P(E|H) * P(H) / P(E)

Birthday Surprise

• What is the probability that 2 people in a room of 30 share a birthday?

• P = 1 – 365/365 * 364/365 * … *336/365 ≈ 0.70

Fuzzy Logic

1. Fuzzification (120 kph = 0.95 fast)2. Inference (IF speed is “fast” THEN

stopping distance is “short”)3. Composition (0.8 “short” and 0.7 “short”

= 0.7 “short”)4. Defuzzification (0.7 “short” = 20 meters)

People Involved

• Domain Expert• Knowledge Engineer• Programmer• Project Manager• End User

Building an Expert System

• Problem assessment determine the problem’s characteristics identify the main participants specify the project’s objectives determine the resources needed

• Data and knowledge acquisition collect and analyze data and knowledge make key concepts of the system design

explicit

Building an Expert System• Development of a prototype system

choose a tool transform data and represent knowledge design and implement prototype test the prototype

• Development of a complete system prepare a detailed design for a full scale system collect additional data and knowledge develop the user interface implement the complete system

Building an Expert System

• Evaluation and revision of a complete system (look for inconsistencies and incompleteness)

• Integration and maintenance of system make arrangements for technology transfer establish an effective maintenance program

Building a Fuzzy Expert System

1. Specify the problem. Define linguistic variables.

2. Determine the fuzzy sets.3. Construct the fuzzy rules.4. Encode the fuzzy sets, fuzzy rules and

fuzzy inference procedures into the expert system.

5. Evaluate and tune the system.

Expert System Shell

• CLIPS is a productive development and delivery expert system tool which provides a complete environment for the construction of rule and/or object based expert systems. Created in 1985, CLIPS is now widely used throughout the government, industry, and academia.

CLIPS features• Allows for many types of knowledge

representation (e.g. rules and procedures)• Portable (written in C)• Extensible• Embeddable• Interactive Development• Verification and Validation support• Fully documented• Public Domain!

Advantages

• Natural Language representation

• Uniform structure

• Separates knowledge from processing

• Can deal with incomplete and uncertain knowledge

Disadvantages

• Opaque relations between rules

• Ineffective search strategy

• Typically can’t learn

Commercial Applications

• National Semiconductor Manufacturing (Singapore) – troubleshoot recurrent equipment breakdowns

• Work and Income New Zealand (a.k.a. Social Welfare Department) - deal with questions of eligibility, allowances and benefit amounts

• GE Capital Global Consumer Finance - help identify risk, retain customers and target prospects

Commercial Applications

• Department of Industry and Fisheries, Tasmania – assist the delivery of information to farmers

• Misselbrook and Weston stores – detect in-store fraud

• Channel 4 TV (UK) – sequence commercial breaks

• Tokyo Nissan - how to increase domestic demand

Commercial Applications

• Rockwell Aerospace and NASA - enables the user to quantify molecular and particulate contamination requirements for solar arrays, thermal control surfaces, or optical sensors

• Meiji Mutual Life Insurance Company - select the most suitable product, along with a reason for the choice, from Meiji's range of 37 individual oriented products

Questions?