Rule Based Systems Rule based systems / Knowledge based systems/ Expert Systems have played and plays an important role in the AI industry. A report from.

Rule Based Systems

Rule based systems / Knowledge based systems/ Expert Systems

have played and plays an important role in the AI industry.

A report from from 1993 by John Durkin:

Reports on Over 2500 Developed Expert Systems

Application areas:

Agriculture, Business, Chemistry, Communications, Computer Systems, Education, Electronics, Engineering, Environment, Geology, Image processing, Information Management, Law, Manufacturing, Mathematics, Medicine, Meteorology, Military, Mining, Power Systems, Science, Space Technology, Transportation

Types of systems:

Rule Based, Frame Based, Fuzzy Logic, Case Based, Neural Network

Architecture of a typical expert system

User

User interface:

Question-and-answer

Menu driven

Natural language

Graphic

inteface

Explanation subsystem

Inference engine

Knowledge- base editor

General knowledge- base

Case-specific

data

Knowledge base

Expert system shell

AI in Medicine (USA 1970)• Stanford

MYCIN - blood infections

• Rutgers

CASNET - casual reasoning

• MIT

PIP - renal disease

• Stanford

• Pittsburgh

Internist – internal medicine

- ”the primary goal of this field is to develop computer programs that perform efficiently and are able to explain their reasoning and conclusions to their users”

MYCIN’S knowledge base

•About 400 diagnostic rules

•About 5 therapy rules

Why Mycin?•Diagnose likely infecting organisms in blood and meningitis infections

•Use test results and information about patient supplied by doctor

•Prescribe an effective antibiotic treatment

•Do this early in the course of the disease, before all possible information is available

•To counteract:

- overuse of antibiotics

- irrational use of antibiotics

-maldistribution of expertise

Mycin system for diagnosis og meningitis and

bacteremia (bacterial infections) IF

the site of the culture is blood, and

the identity of the organism is

not known with certainty, and

the stain of the organism is gramneg, and

the morphology of the organism is rod, and

the patient has been seriously burned

THEN

there is weakly suggestive evidence (0.4) that

the identity of the organism is pseudomonas

MYCIN diagnosis rule (2)

IF

the site of the culture is blood, and

the identity of the organism is gramneg, and

the morphology of the organism is rod, and

the patient is a compromised host

THEN

there is suggestive evidence (0.6) that

the identity of the organism is pseudomonas-Aeruginosa


Rule 3

IF (1) stain of organism is gram-positive and

(2) morphology of organism is coccus and

(3) growth-conformation of the organism is clumps

THEN there is suggestive evidence (0.7) that

identity of organism is staphylococcus.


(CEFAX notation )

rule 3

if stain of organism is gram_positive and

morphology of organism is coccus and

growth_conformation of organism is clumps

then 0.7 certainty


MYCIN: Therapy Selection Rule

IF You are considering giving chloramphenicol, and

the patient is less than 1 week old

THEN it is definite (1.0) that chlorampericol is contraindicated for this patient

[Justification: Newborn infants may develop vasomusculular collapse due to an immaturity of the liver and kidney functions resulting in decreased metabolism of chloramphenicol]

How does MYCIN create confidence in the user

Answering ”Why?” (Why did you ask that?)

Answering ”How” (How did you arraive at that conclusion?)

Answering ”Why not X?” (Why did you not consider X?)

Mycin’s simple rule format and friendly explanations in ”English” are the key.

MYCIN Explanation

User: Why didn’t you consider Streptococcus as a possiblity

for Organism- 1

MYCIN: The following rule could have been used to determine that the identoty of Organism-1 was streptococcus:

Rule 33

But Clause 2 (”the morphology of the organism is Coccus”) was already known to be false for Organism-1, so the rule was never tried.

How MYCIN looks to the user:Therapy recommendation

[REC-1] My preferred therapy recommendation is as follows:

In order to cover for items <1 2 3 4 5Z:

Give the following in combination

1: Kanamycin

Dose 750 mg (7.5 mg(kg)q12h IM (or IV)

for 28 days

Comments: Modify dose in renal failure

2: Penicillin

Dose: 2,500,000 units (2500 units/kg) q4h IV for 28 days

Emycin and expert system shell

MYCIN has later been developed, and separated into to parts:

An expert system shell EMYCIN (empty MYCIN)

A knowledg base

The expert system shell EMYCIN is ”the mother of all expert system shells”.

One simplified version is called CEFAX is implemented at NTNU in Prolog

Rule based system as a reasoning systemIf we look aside from the uncertainties in MYCIN, the system can be regarded as logical inference system, where the explanation is the proof tree of the reasoning.

A bank clerk shall approve loans for customers. He collects the basic information about the customer, which is represented as a set of variables:

Basic variablesAPP (the appraisal on the collateral is greater than the loan amount)RATING (The applicant has a good credit rating)INC (The applicant’s income exceeds his expenses)BAL (The applicant has an excellent balance sheet) Derived variablesOK (The loan should be approved)COLLAT(The collateral for the loan is satisfactory)PYMT (The applicant is able to make the loan payments)REP (The applicant has a good financial reputation)

The loan approval rule base

He follows the banks guidelines, which can be stated as follows

(1) COLLAT PYMT REP OK(2) APP COLLAT(3) RATING REP(4) INC PYMT(5) BAL REP OK

A loan application case

Mr John Smith has applied for a loan. His case is:

APP is true, INC is true, RATING is true, BAL is true

That his loan can be approved follows from the following proof tree

(Rule numbers in parentheses)

approval

OK OK

(1) (5)

COLLAT PYMT REP BAL REP

(2) (4) (3) (3)

APP INC RATING RATING

The loan approval as a rule based system

rule 1

if collat is true and

pymt is true and

rep is true

then 1.0 certainty

ok is true.

rule 2

if app is true

then 1.0 certainty

collat is true.

rule 3

if rating is true

then 1.0 certainty

rep is true.

rule 4

if inc is true

then 1.0 certainty

pymt is true.

rule 5

if bal is true and

rep is true

then 1.0 certainty

ok is true.

A loan approval “real” dialog

User: Why do you believe that the loan should be approved

System: Because the collateral for the loan is satisfactory, and the applicant is able to make the loan payments, and the applicant has a good financial reputation.

User: Why do you believe that the applicant has a good financial reputation ?

System: Because the credit-rating service said that the applicant has a good credit rating.

User: Why did you ask me about whether or not the applicant’s income exceeds his expenses ?

System: Because I was trying to establish whether or not the applicant is able to make the loan payments.

User: Why are you trying to establish whether or not the applicant is able to make loan payments.

System: Because that is one of the criteria whether or not the loan should be approved.

User: Why are you trying to establish whether or not the loan should be approved ?

System: Because you asked me to.

An expert system loan approval dialog?- eval ok.

What is the certainty of app is true --> 1.0.

What is the certainty of inc is true --> why.

I ask because

rule 4 if inc is true

then 1.00 certainty pymt is true .

I ask because

rule 1 if collat is true and pymt is true and rep is true

then 1.00 certainty ok is true .

The last conclusion was your original problem .

What is the certainty of inc is true --> 1.0.

What is the certainty of rating is true --> 1.0.

What is the certainty of bal is true --> 1.0.

ok is true IS PROVED ( 1.00)

(help./ how./ ok./ quit./ ) ?

Loan approval proof

Dialog and proof of Loan approval are shown here

PRO/loanapproval.txt

PRO/loanproof.txt


(help./ how./ ok./ quit./ ) ?

|: how.

BECAUSE

contributions

by rule 1 :


BECAUSE

collat is true IS PROVED ( 1.00)

BECAUSE

by rule 2 :

collat is true IS PROVED ( 1.00)

BECAUSE

app is true IS PROVED ( 1.00)

BECAUSE

app is true is given AND

pymt is true IS PROVED ( 1.00)

BECAUSE

by rule 4 :

pymt is true IS PROVED ( 1.00)

BECAUSE

inc is true IS PROVED ( 1.00)

BECAUSE

inc is true is given AND

rep is true IS PROVED ( 1.00)

BECAUSE

by rule 3 :


BECAUSE

rating is true IS PROVED ( 1.00)

BECAUSE

rating is true is given

by rule 5 :


BECAUSE

bal is true IS PROVED ( 1.00)

BECAUSE

bal is true is given

AND


BECAUSE

by rule 3 :


BECAUSE

rating is true IS PROVED ( 1.00)

BECAUSE

rating is true is given

The Certainty Factor model for uncertainty handling

Rule 3

IF (1) stain of organism is gram-positive and

(2) morphology of organism is coccus and

(3) growth_conformation of the organism is clumps

THEN there is suggestive evidence (0.7) that


The uncertainty model is based on certainties which are numbers between –1 and +1. The example 0.7 is a rule parameter that modifies the certainty of the conclusion.

Uncertainty vs Ignorance

The origin is based on belief intervals.

Measure of Belief [0.0 -- 1.0]

Measure of Disbelief [0.0 -- 1.0]

Certainty Factor = MB – MD = [ - 1.0 -- + 1.0]

Measurements of Ignorance 1.0 – (MB+MD)

Measurement of inconsistency MB+MD –1.0 (= -MI)

Uncertainty: Is it raining in Trondheim tomorrow ?

Ignorance : Is it raining in Kuala Lumpur tomorrow ?

00 0

00 1

MB MI MD

Statistical interpretationsCharacteristics Values

Ranges 0 <= MB <= 1

0 <= MD <= 1

-1 <= CF <= 1

Certain True Hypothesis MB=1

P(H|E) =1 MD=0

CF =1

Certain False Hypothesis MB=0

P(-H|E) =1 MD=1

CF = -1

Lack of evidence MB=0

P(H|E) =P(H) MD=0

CF = 0

Contradictory evidence MB=1

MD=1

CF = 0

Manipulation of CF-values

Usually, we use only one CF value, so we don’t distinguish between ignorance and inconsistency.

CF rule principle

if P then CF certainty Q

CF(P) parameter CF(Q)

computed defined computed

CF(Q) =

CF(P) *CF CF(P) >0

0 otherwise

Antecedent Combination Rule

If A and B then CF C

(CF = 0.6)

A CF(A) (0.5)

B CF(B) (0,7)

CF(A and B) = min(CF(A),CF(B)) = (0.5)

CF (C ) = CF * CF(A and B) = (0.3)

Similarily

CF(A or B) = max (CF(A),CF(B))

CF(not B) = - CF(B)

The CF values of the premise is computed together

Serial Combination Rule

IF AA THEN CF1 B

CF(AA)=0.5 (0.7) => CF(B)=0.35

IF B THEN CF2 C

(0.35) (0.3) => CF(C) = 0.105

The CF-values are chained together with the rule applications

Paralell combination ruleAccumulation of CF-values, contribution from several rules

(1) IF AA1 then xx R ( CF(R1)= P)

(2) IF AA2 then yy R ( CF(R1)= Q)

CF(R) = P + Q – P*Q (in the simple case)

QP

R

Motivation for Parallel rule

Supppose B1 and B2 are two independent stochastic variables, and that B = B1 or B2

Then

P(B) = P(B1 or B2)

= P(B1) + P(B2) – P(B1 and B2)

= P(B1) + P(B2) – P(B1)*P(B2)

which corresponds to the rule

CF(R) = CF(R1) + CF(R2) – CF(R1)*CF(R2)

The complete parallel rule

CF1 + CF2 – CF1*CF2 (CF1,CF2 >0)

CFparallel(CF1,CF2) = CF1 + CF2 – CF1*CF2 (CF1,CF2 < 0)

(CF1 + CF2) (CF1*CF2 <0) _ _________________

(1 – min(|CF1|,|CF2|))

Motivation for complete parallel ruleHistorically, the parallel rule for CF values of opposite sign was just

CF = CF1 + CF2

e.g.

CF1=0.999 (damn sure)

CF2= - 0.799

=> CF = 0.2 which is unreasonably low

The revised rule gives

CF = 0.995 (almost damn sure)

BUT the old rule also had the defect that

it was not associative and not commutative

Mathematical properties of the revised parallel rule

The parallel rule has some good and obviously required properties

The CF parallel combination rule has some very nice (and obviously required) mathematical properties:

- it is associative, i.e. evidence may be grouped arbitrarily

- it is commutative, i.e. the sequence of evidence is irrelevant

- it has a zero element, (CF = 0) that has no effect

- it is symmetric, i.e. equal but opposite evidence cancel out

However, the CF parallel combination rule is not idempotent:

C + C - C*C > C (if C >0)

(If you repeat the same weakly supported postulate sufficiently often,

it will be regarded as certain after a while . :-)

Rule Based Systems Rule based systems / Knowledge based systems/ Expert Systems have played and plays an important role in the AI industry. A report from.

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