Expert Systems

Infsy 540

Dr. Ocker

Expert Systems

computer systems which try to mimic human expertise

produce a decision that

does not require judgment assistants to decision makers rather

than substitutes for them

Expert Systems & AI

artificial intelligence (AI) - group of technologies that attempt to emulate certain aspects of human behavior, such as reasoning and communicating

Expert systems are the most important product of AI research to date

Systems and Types of decisions

Traditional computing systems deal with routine/structured problems

e.g. payroll system is structured - – can write down the formulas– calculations are very repetitive– requires little judgment– no creativity

Systems and Types of decisions

DSS deal with semi-structured problems

– processing less well-defined– no judgment inside system– decision maker applies assumptions to

problem, system calculates results– output must be interpreted by decision

maker

Example Problem

Use DSS to help predict effects of

early retirement plan vary assumptions about plan to forecast

financial impact system produces an answer for each set of

assumptions user judges the validity of the assumptions

and the value of the answer

Systems and Types of decisions

Expert System deals with semi-structured problems

– judgment incorporated into system– system produces a solution– system can “explain” how it reached its

conclusions

Example Problem

Use Expert System to develop an early retirement plan

system contains decision criteria (“rules”) established by decision makers

uses rules to frame a retirement program can trace rules used in developing the

retirement program

What is an expert system?

A knowledge-based system: provides specific knowledge about a

narrow problem domain knowledge stored in the knowledge base system uses knowledge and an

inferencing (reasoning) procedure to solve problems that would otherwise require human competence or expertise.

To use an expert system(1) gather input problem variables and criteria

(2) consult computerized base of knowledge

(3) system reasons out an answer

ES often assistants to decision makers and not substitutes for them

i.e. use ES to help DM with part of a larger problem

Example - Internist/Caduceus one of most knowledge-intensive expert

systems covered 85% of internal medicine - included

information on 500 diseases and more than 100,000 symptomatic associations

user inputs given patient information system uses its knowledge base to identify

a disease and recommend treatment

Components of expert systems

1) knowledge base

2) inference engine

3) knowledge acquisition module

4) explanatory interface

1. knowledge base

structure for saving facts and rules relevant to a specific application (problem domain)

2 types of info: (1) book knowledge about a domain (2) heuristic knowledge - rules of thumb

used by human experts who work in the domain

2. inference engine

that portion of the sw that contains the reasoning methods

expert system asks questions of the user to get info. it needs.

then inference engine, using knowledge base, searches for the sought-after knowledge

returns a decision/ recommendation to user

3. knowledge acquisition module

used by expert to enter rules or facts into the system

4. explanatory interface

system shows the trail of reasoning it used to reach a decision

explains the facts it used what rules it applied and in what order

2 environments of ES

Development Environment Consultation Environment

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Structure of an Expert SystemStructure of an Expert System

Consultation Environment(Use)

Development Environment(Knowledge Acquisition)

User Expert

User Interface

Inference Engine

ExplanationFacility

Working Memory

Facts ofthe Case

Recommendation,Explanation

Facts ofthe Case

KnowledgeEngineer

KnowledgeAcquisition

Facility

KnowledgeBase

Domain Knowledge(Elements ofKnowledge Base)

Knowledge Representation

knowledge represented in expert systems in variety of ways, including:

rules case-based reasoning

Rules

most common way to represent knowledge in expert system

rules called heuristics - obtained from experts number of rules determines complexity of

system rules most appropriate when knowledge can

be generalized into specific statements.

example of heuristic rule

if good customer

and credit requested < $5,000

and loan term < 1 year

then grant credit

Case-based reasoning

system draws inferences by comparing a current problem (case) with hundreds/thousands of similar past cases.

best used when situation involves too many nuances and variations to be generalized into rules

example of case-based reasoning

Sharon , 35 yrs. old, entered hospital with potentially fatal respiratory disease. Her vital stats. and medical history entered into expert system. System drew on records of over 17,000 previous intensive-care patients to predict whether Sharon would live or die.

example of case-based reasoning

First prediction - 15% chance of dying. Stats. entered daily - system compared

her progress to base of previous cases. 2 weeks later - prediction soared to 90%

chance of dying - alerted physicians and nurses to take corrective action.

How expert systems work

knowledge representation method used to organize knowledge production rules - most common

method– consist of an IF part and a THEN part

IF condition THEN action

How expert systems work

Inference Engine controls the order in which the

production rules are applied to solve the problem and

resolves conflicts if more than one rule applies– this is the "reasoning" process

How solution process works

user presents a set of facts describing a situation to the expert system.

inference engine compares facts of the case to the knowledge base

system then gives a recommendation asks for more information if needed

Inferencing strategies for rule-based system Forward chaining

– data driven– inferencing moves from facts of case to a

goal (conclusion) Backward chaining

– inferencing moves from a possible goal state to premises that would satisfy it

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Inferencing StrategiesInferencing Strategies

InputData

Few Items(For Example, UserSpecifications fora Computer System)

Conclusion(Goals)

Many Possibilities(For Example, a ComputerConfiguration)

(a) Forward Chaining: IF - Part Matches Shown

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Inferencing Strategies (Cont.)Inferencing Strategies (Cont.)

InputData

Extensive;Much of the DataObtained by theSystem Queryingthe User (ForExample,Investor’s Profile)

Conclusion(Goals)

Few Possibilities(Known in Advance((For Example, Investment Options)

(b) Backward Chaining: THEN - Part Matches Shown

Expert system shells Most common way to develop ES shell is an expert system without the

knowledge base– includes inference engine, user interface,

explanation and knowledge acquisition pieces– generic shells - used to develop ES in any domain– domain-specific shells - incomplete specific ES;

require much less effort - already includes many rules

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Expert Systems TechnologiesExpert Systems Technologies

Higher-LevelProgramming

Language

Expert SystemDevelopmentEnvironment

Generic Shell

Domain-SpecificShell

Specific ExpertSystem

GreaterFlexibility

GreaterEase of Use

GreaterComplexity ofProblem andEnvironment

Development of ES

Prototype-oriented iterative development

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Development & Maintenance of ESsDevelopment & Maintenance of ESs

Problem Identification andFeasibility Analysis

System Design and ESTechnology Identification

Development ofPrototype

Testing and Refinementof Prototype

Is the PerformanceSatisfactory? Complete and

Field the ES

Maintain ES

No

Yes

ES Ready for Use

Benefits of Expert Systems

Quick consistent low error rate capture scarce expertise

Limitations of Expert Systems

Must have agreement among experts must have a willing expert most only support operational-level

tasks use can weaken human expertise

Appropriate Problem Space for Expert System

1.technical disciplines with large bodies of complex information

2.situations that require decisions

3.an expert can articulate the decision rules s/he uses