BEE4333 Intelligent Control Hamzah Ahmad Ext: 2055/2141

BEE4333 Intelligent Control

Hamzah AhmadExt: 2055/2141

EXPERT SYSTEM: WHAT IS IT ALL ABOUT?

Chapter 2

Achievements :PO1, CO1, CO5, LO1

Today contents and achievements

• LO1 : Able to understand the concept of expert system

2.1 Knowledge representation technique

Achievements:CO1

Artificial Intelligence

• Early 1960’s problem for AI;– Weak methods; general method to find solution to

a problem (not efficient, weak performance)– Too difficult and broad problem to solve

• Rise needs for specialized problem solving techniquemimicking human expert behavior and decision

• Since 1970’s, the AI is designed to adopt human expertise as a knowledge-based system.

Introduction, or what is knowledge? Rules as a knowledge representation

technique The main players in the development team Structure of a rule-based expert system Characteristics of an expert system Forward chaining and backward chaining Conflict resolution Summary

Rule-based expert systems:Contents

EXPERT SYSTEMS DEFINITION

An Expert System (ES) is a computer-based system (mainly software) that uses knowledge and facts, and apply an appropriate reasoning technique (inferencing) to solve problems in a given field (domain) that normally require the services of human experts.

Keyword: knowledge engineering, knowledge practitioner, knowledge-based system

What is an Expert System…(Cont’d)

• A computer application that performs a task that would otherwise be performed by a human expert.

• 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

In other words..Expert System

• are computer programs which attempts to replicate knowledge and skills of humans expert in some area, and then attempt to solve problems in this area (the way human expert to do)

• Take root from cognitive science – the study of human mind using a combination of AI and psychology

• Comprises of two main parts;– Knowledge– Reasoning or inference

A Human Experts

• Consider several examples:– A medical doctor– carpenter– Engineer– A chef

What is differences between above expertise? Can you say that one is more intelligent that another?

Intelligent Vs Expertise

• Expertise and intelligence are not the same things (although they are related)

• Expertise requires long time to learn• Expertise is a large amount of knowledge (in some

domain)• Expertise is easily recalled• Intelligence allows you to use your expertise

(apply the knowledge)• Expertise enables you to find solution much faster

BRIEF HISTORY

Examples of the early and famous expert systems• DENDRAL - Stanford Univ. (1965)

– Analysis of chemical compunds– Rule-based system

• CADACEUS - Univ. of Pittsburgh (1970)– Diagnosis of human internal diseases

• MYCYSMA - MIT (1971)– Symbolic mathematical analysis

Brief History (Cont’d)

• MYCIN - Stanford Univ. (1972)– Diagnosis and treatment of infectious blood diseases – Rule-based system

• EMYCIN - Stanford Univ. (1978)– Evolved from (Empty) Mycin– First ES Shell

• XCON - DEC (1980)– configures VAX computer system– First ES used commercially

• 1943 Post, E.L proved that any computable problem can be solved using simple IF-THEN rules

• 1962 General Problem Solver (GPS) by A.Newell and H.Simon

Computer Vs Mind

• The whole is more than a sum of its parts

– Computer use pre-programmed algorithms– Obviously, our mind do not work like computers: different

people usually solve problems in their own ways, and you even may solve the same problem differently every time

– Even if we begin to know how neural networks work, it is hard to scale up and understand how they form thoughts

– Some scientists believe that cognition (or intelligence) is somewhat independent of the architectural implementation (i.e. not necessarily should use neural networks)

What is knowledge? Knowledge is a theoretical or practical

understanding of a subject or a domain. Knowledge is also the sum of what is currently known, and apparently knowledge is power. Those who possess knowledge are called experts.

Anyone can be considered a domain expert if he or she has deep knowledge (of both facts and rules) and strong practical experience in a particular domain. The area of the domain may be limited. In general, an expert is a skilful person who can do things other people cannot.

Rules as a knowledge representation technique The term rule in AI, which is the most commonly

used type of knowledge representation, can be defined as an IF-THEN structure that relates given information or facts in the IF part to some action in the THEN part. A rule provides some description of how to solve a problem. Rules are relatively easy to create and understand.

Any rule consists of two parts: the IF part, called the antecedent (premise or condition) and the THEN part called the consequent (conclusion or action).

Simple Expert system architecture

How the expert system arrived at certain results

Provide a convenient and efficient means for capturing and storing all components of the knowledge base

stores all relevant information, data, rules, cases, and relationships used by the expert system

A rule can have multiple antecedents joined by the keywords AND (conjunction), OR (disjunction) or a combination of both.

IF <antecedent 1>AND <antecedent 2>

IF <antecedent>THEN <consequent>

AND <antecedent n> THEN <consequent>

. . . . . .

IF <antecedent 1>OR <antecedent 2>

OR < antecedent n>THEN < consequent>

The antecedent of a rule incorporates two parts: an object (linguistic object) and its value. The object and its value are linked by an operator.

The operator identifies the object and assigns the value. Operators such as is, are, is not, are not are used to assign a symbolic value to a linguistic object.

Expert systems can also use mathematical operators to define an object as numerical and assign it to the numerical value.IF ‘age of the customer’ < 18AND ‘cash withdrawal’ > 1000THEN ‘signature of the parent’ is required

The human mental process is internal, and it is too complex to be represented as an algorithm. However, most experts are capable of expressing their knowledge in the form of rules for problem solving.

IF the ‘traffic light’ is greenTHEN the action is go

IF the ‘traffic light’ is redTHEN the action is stop

Relation IF the ‘fuel tank’ is empty THEN the motorcycle cannot start

Recommendation IF the season is autumn AND the sky is cloudy AND the forecast is drizzle THEN

the advice is ‘take an umbrella’ Directive

IF the class is sleepy AND the class is not taking their breakfast THEN the action is ‘………………….’

Rules can represent relations, recommendations, directives, strategies and heuristics:

Strategy IF the car is dead THEN the action is ‘check the fuel tank’;

step1 is complete

IF step1 is complete AND the ‘fuel tank’ is full THEN

the action is ‘check the battery’; step2 is complete

Heuristic IF the spill is liquid AND the ‘spill pH’ < 6 AND

the ‘spill smell’ is vinegar THEN the ‘spill material’ is ‘acetic acid’

Types of Expert System

• Ruled Based Expert System– Represented as a series of rules

• Frame-Based System– Representation of the object-oriented programming approach

• Hybrid System– Include several knowledge representation approach

• Model-Based System– Structured around the model that stimulates the structure and

function of the system under study• Ready-Made (Off-the-shelf) System

– Custom-made, similar to application package such as an accounting general ledger or project management in operation mgmt.

Data -> Information -> Knowledge

• DATA- measurements or records about events. Can be numerical, alphabetical, images, sounds, etc.

• INFORMATION – analyzed and organized data such that we know its characteristics

• KNOWLEDGE – information put into a specific context

Levels of Abstraction

• Data, Information and Knowledge can be classified by levels of abstraction and quantity

• Knowledge is the most abstract and exists in the smallest quantity

• Knowledge itself can have levels of abstraction : concrete (knowledge about the specific problem), domain specific (class of problems) and abstract (many classes of problems)

High

Abstraction

low

knowledge

information

Data

Knowledge Categories

• Symbolic, sub-symbolic & hybrid– Symbolic – represented as discrete unit (explicit)->

expressed in formal language– Sub-symbolic – cannot be represented by single

symbols (implicit), -> stored in a form of information

– Hybrid -> systems involved both types of knowledge representation

Knowledge Categories(cont..)

• Declarative & Procedural – Declarative -> Knowledge are facts (objects, events) that we

observe in the environment. Can describe current state of a problem

e.g. : I' am a human or the sun shining– Procedural -> knowledge represents the skills or rules we use

to operate with and reason using facts e.g. : IF I' am a human THEN I have two legs

• Apparently, there are separate areas in the human brain for storing declarative and procedural knowledge

• Rules (procedural) are usually harder to describe

Basic Concept of ES• Expertise

– Enable experts to make better and faster decisions than non-experts in solving complex problem

• Experts– Degrees or levels of expertise -> mimic human expert

• Transferring expertise– Involves 4 activities -> knowledge acquisition, knowledge representation,

knowledge inference and knowledge transfer to user

• Inference rules– Ability to reason-> component called the inference engine

• Rules– Problem solving procedure -> knowledge stored at database

• Explanation capability– Ability to explain its advice or recommendations -> components/subsystem

called justifier or explanation subsystem

Fundamental Characteristic of an ES

• Narrow specialized domain• High quality performance• Rules of thumb or heuristic• Explanation capability• Employ symbolic reasoning• Do not follow a prescribed sequence of

steps

An expert system is built to perform at a human expert level in a narrow, specialised domain. Thus, the most important characteristic of an expert system is its high-quality performance. No matter how fast the system can solve a problem, the user will not be satisfied if the result is wrong.

On the other hand, the speed of reaching a solution is very important. Even the most accurate decision or diagnosis may not be useful if it is too late to apply, for instance, in an emergency, when a patient dies or a nuclear power plant explodes.

Characteristics of an expert system

Expert systems apply heuristics to guide the reasoning and thus reduce the search area for a solution.

A unique feature of an expert system is its explanation capability. It enables the expert system to review its own reasoning and explain its decisions.

Expert systems employ symbolic reasoning when solving a problem. Symbols are used to represent different types of knowledge such as facts, concepts and rules.

QUALIFYING CHARACTERISTICS OF EXPERT SYSTEMS

ES are appropriate in domains when/where:• there are no established theories• human expertise is scarce or in high demand,

but recognized experts exist• the information is fuzzy, inexact or incomplete• the domain is highly specific

QUALIFYING CHARACTERISTICS OF EXPERT SYSTEMS … Cont’d

ES are not suitable when/where:• applications are calculative or deterministic in

nature• a formula or model exists• human experts are plentiful• not integrable into existing systems• end users have to build the knowledge base

Today Summary

• Introduction to Expert system– Definition and concept– Where to apply?– Brief condition of rule based expert system


Achievements:CO1



• LO2 : Able to understand the structure of expert system development team

2.2 Expert System development team2.3 Rule-based expert system structure

Achievements:CO1

Introduction, or what is knowledge? Rules as a knowledge representation

technique The main players in the development team Structure of a rule-based expert system Characteristics of an expert system Forward chaining and backward chaining Conflict resolution Summary

Rule-based expert systems:Contents

Designing ES

The process of building ES is called Knowledge Engineering, consist of three stages :

i. Knowledge acquisition : the process of getting the knowledge from experts

ii. Knowledge representation : selecting the most appropriate structures to represent the knowledge

iii. Knowledge validation :testing that the knowledge of ES is correct and complete

The main players in the development team

There are five members of the expert system development team: the domain expert, the knowledge engineer, the programmer, the project manager and the end-user.

The success of their expert system entirely depends on how well the members work together.

The main players in the development team

Expert System

End-user

Knowledge Engineer ProgrammerDomain Expert

Project Manager

Expert SystemDevelopment Team

Domain expert knowledgeable and skilled person capable of solving problems in

a specific area or domain. has the greatest expertise in a given domain. This expertise is to

be captured in the expert system.

Knowledge engineer capable of designing, building and testing an expert system. establishes what reasoning methods the expert uses to handle

facts and rules and decides how to represent them in the expert system.

chooses some development software or an expert system shell, or looks at programming languages for encoding the knowledge.

responsible for testing, revising and integrating the expert system into the workplace.

Programmer responsible for the actual programming, describing the domain

knowledge in terms that a computer can understand. have skills in symbolic programming in such AI languages and also

some experience in the application of different types of expert system shells.

know conventional programming languages like C, Pascal, FORTRAN and Basic.

Project manager leader of the expert system development team, responsible for

keeping the project on track. makes sure that all deliverables and milestones are met, interacts

with the expert, knowledge engineer, programmer and end-user.

End-user(User) uses the expert system when it is developed. confident in the expert system performance but also feel

comfortable using it. the user interface of the expert system is vital for the project’s

success; the end-user’s contribution here can be crucial.

In the early seventies, Newell and Simon from Carnegie-Mellon University proposed a production system model, the foundation of the modern rule- based expert systems.

The production model is based on the idea that humans solve problems by applying their knowledge (expressed as production rules) to a given problem represented by problem-specific information.

The production rules are stored in the long-term memory and the problem-specific information or facts in the short-term memory.

Structure of a rule-based expert system

Production system model

Conclusion

REASONING

Long-term Memory

ProductionRule

Short-term Memory

Fact

Basic structure of a rule-based expert system

Rule: IF-THEN Fact

The knowledge base contains the domain knowledge useful for problem solving. In a rule- based expert system, the knowledge is represented

as a set of rules. Each rule specifies a relation, recommendation, directive,

strategy or heuristic and has the IF (condition) THEN (action) structure. When the condition part of a rule is satisfied, the rule is said to fire and the action part is executed.

The database includes a set of facts used to match against the IF (condition) parts of rules stored in the knowledge base.

The inference engine carries out the reasoning whereby the expert system reaches a solution. It links the rules given in the knowledge base with the facts provided in the database.

The explanation facilities enable the user to ask the expert system how a particular conclusion is reached and why a specific fact is needed. An expert system must be able to explain its reasoning and justify its advice, analysis or conclusion.

The user interface is the means of communication between a user seeking a solution to the problem and an expert system.

Complete structure of a rule-based expert system

User

ExternalDatabase External Program

Inference Engine

Knowledge Base

Rule: IF-THEN

Database

Fact

Explanation Facilities

User Interface DeveloperInterface

Expert System

Expert

Knowledge Engineer

Can expert systems make mistakes?

Even a brilliant expert like a human make mistakes. This suggests that an expert system built to perform at a human expert level also should be allowed to make mistakes. If human make mistakes, Then machines are also allowed making mistakes (with

accuracy)

We still trust experts, even we recognise that their judgements are sometimes wrong.

We relies to expert system with consideration of probability of mistake.

In expert systems, knowledge is separated from its processing (the knowledge base and the inference engine are split up). A conventional program is a mixture of knowledge and the control structure to process this knowledge. This mixing leads to difficulties in understanding and reviewing the program code, as any change to the code affects both the knowledge and its processing.

When an expert system shell is used, a knowledge engineer or an expert simply enters rules in the knowledge base. Each new rule adds some new knowledge and makes the expert system smarter.

An expert system is built to perform at a human expert level in a narrow, specialised domain. Thus, the most important characteristic of an expert system is its high-quality performance. No matter how fast the system can solve a problem, the user will not be satisfied if the result is wrong.

On the other hand, the speed of reaching a solution is very important. Even the most accurate decision or diagnosis may not be useful if it is too late to apply, for instance, in an emergency, when a patient dies or a nuclear power plant explodes.

Characteristics of an expert system

Expert systems apply heuristics to guide the reasoning and thus reduce the search area for a solution.

A unique feature of an expert system is its explanation capability. It enables the expert system to review its own reasoning and explain its decisions.

Expert systems employ symbolic reasoning when solving a problem. Symbols are used to represent different types of knowledge such as facts, concepts and rules.

QUALIFYING CHARACTERISTICS OF EXPERT SYSTEMS

ES are appropriate in domains when/where:• there are no established theories• human expertise is scarce or in high demand,

but recognized experts exist• the information is fuzzy, inexact or incomplete• the domain is highly specific

QUALIFYING CHARACTERISTICS OF EXPERT SYSTEMS … Cont’d

ES are not suitable when/where:• applications are calculative or deterministic in

nature• a formula or model exists• human experts are plentiful• not integrable into existing systems• end users have to build the knowledge base

Comparison of expert systems with conventional systems and human experts

Human Experts Expert Systems Conventional Programs

Use knowledge in theform of rules of thumb orheuristics to solveproblems in a narrowdomain.

Process knowledgeexpressed in the form ofrules and use symbolicreasoning to solveproblems in a narrowdomain.

Process data and usealgorithms, a series ofwell-defined operations,to solve general numericalproblems.

In a human brain,knowledge exists in acompiled form.

Provide a clearseparation of knowledgefrom its processing.

Do not separateknowledge from thecontrol structure toprocess this knowledge.

Capable of explaining aline of reasoning andproviding the details.

Trace the rules firedduring a problem-solvingsession and explain how aparticular conclusion wasreached and why specificdata was needed.

Do not explain how aparticular result wasobtained and why inputdata was needed.

Comparison of expert systems with conventional systems and human experts

(Continued)Human Experts Expert Systems Conventional Programs

Use inexact reasoning andcan deal with incomplete,uncertain and fuzzyinformation.

Permit inexact reasoningand can deal withincomplete, uncertain andfuzzy data.

Work only on problemswhere data is completeand exact.

Can make mistakes wheninformation is incompleteor fuzzy.

Can make mistakes whendata is incomplete orfuzzy.

Provide no solution at all,or a wrong one, when datais incomplete or fuzzy.

Enhance the quality ofproblem solving via yearsof learning and practicaltraining. This process isslow, inefficient andexpensive.

Enhance the quality ofproblem solving byadding new rules oradjusting old ones in theknowledge base. Whennew knowledge isacquired, changes areeasy to accomplish.

Enhance the quality ofproblem solving bychanging the programcode, which affects boththe knowledge and itsprocessing, makingchanges difficult.

Expert System Application areas

Application areas include classification, diagnosis, monitoring, process control, design, scheduling and planning, and generation of options.• Classification - identify an object based on stated characteristics• Diagnosis Systems - infer malfunction or disease from observable

data• Monitoring - compare data from a continually observed system to

prescribe behaviour• Process Control - control a physical process based on monitoring• Design - configure a system according to specifications• Scheduling & Planning - develop or modify a plan of action• Generation of Options - generate alternative solutions to a problem

Examples of ES• Ali wants to go from KL to Sydney, Australia. His is member of the frequent flyer

programme of MAS – Departure: 1.04.11 Return: 10.05.11

• Possible solution– Solution

• Flight MH434 KLIA – Indonesia- Sydney 1.04.11 9 H 40 • Flight MH121 KLIA – Singapore – Sydney 1.04.11 11 H 45 • Flight MH101 KLIA – Sydney 2.04.94 9 H 30 • Flight MH123 KLIA- New Zealand 2.04.94 16 H 20

• Rule 1– IF the local flight is done by a company THEN plan the international flight with this

company• Rule 2

– IF the customer is part of the frequent flyer programme of a company and if this company has a flight to the customer's destination THEN select a flight with this company.

Groupsgroup 1 group 2

MOHD AZWAN BIN RAMLAN NORFADILAH BT CHE YUSOFF

MUHAMMAD AIMAN BIN AZIZUDDIN NOOR AMIRA SYUHADA BINTI MAHAMAD SALLEH

AMIRULLAH BIN ARIES ZALIDA WATI BINTI AHMAD

MUHAMMAD FADLI BIN MOHAMAD NUR FARAHANA BT MAT KHAIRI

ZUL RASYIED BIN AB RASAT MYRZA FASHA BINTI MD HAMYRUDIN

group 3 group 4NURUL AKMAL BINTI CHE ZAHARI NUR SYAZREEN BINTI ARDNAN

NORLAILAH BINTI LANISA NUR AIN BT ABD WAHAB

SITI NOR AZRA BT MOHD ANUAR NURUL HUDA BINTI BAHARUN

WAN NUR DIYANA BINTI WAN MUSTAFA NUR ATHIRAH BINTI A GHANI

NUR HAMIZAH BINTI ABDUL HAMID NOR SUAIDA BINTI ABD WAHAB

group 5 group 6

SITI KHADIJAH BINTI MOHAMAD FATIN IZZWANI BINTI AZMAN

ENTI NASHILA BINTI RIAZUDDIN NURUL ASYIKIN BT HUSSIN

SALINA BT BIDIN NIK NUR FARAH AYU BINTI CHE HAMID

NUR SYAKIRAH BINTI SAPIE SITI NUR AISHAH RODZIAH

NUR AIZAT BINTI OTHMAN WAN NUR FARHANAH BINTI WAN ZAKARIA

group 7 group 8

CHONG SOON WOEI WONG CHUN YUAN

MOHAMAD NORFALIHIN BIN HARUN AHMAD FATHI BIN ABD MALIK

SHAHIRAN IZNAN B MOHD YUSOF @ HAMID MUHAMMAD REZDUAN BIN OTHMAN

AMIR SYAHIR BIN YAAKOB SYED MOHD MOKHLIS BIN SAYED MAHADZIR

MOHD BUKHARI BIN SALLEH MOHD SHAFIZAL BIN ISA

group 9CHOU YONG LEE

MOHD 'IZZAT BIN MAT HASSAN

MOHD NORHAFIZZI BIN SIDEK

AHMAD ZULHILMI BIN ZAINAL ABIDIN

MOHD KHAIRUL HAFEZEE BIN MOHD ASRI

group 10

MUHAMMAD RAZIF BIN NOOR AZAM

AHMAD SAIFUDDIN BIN JAMALUDDIN

MOHD HAFIS AIKAL BIN AMRAN

MOHD AFIQ B NORDIN

MUHAMMAD AZLAN BIN AHMAD @ ABDUL RAHIM

group 11

GOH SWEE TIAN

ISKANDAR RIZAN BIN MUHAMAD

DEVANDRAN A/L KRISNAN

MOHD HAFIZIE BIN MUHAMAD

group 12NURUL JANNAH BINTI MOHAMAD JUNUS

RAWAIDA BINTI JAAFAR

NURUL FARAH AIN BT ABDUL HAMID

NORHIDAYAH MAT YUSOFFNUR AZIA AZWANI BINTI ISMAIL

group 13NURUL FADZLINA BINTI JAMIN

NUR NAJIAH BINTI MOHD NASIR

THOIEBAH BINTI AHMAD MUKHTA

NORHASLINDA MOHD ROSLI

Discussion in Group

• Given the following fields, select an area that your group is most expert with.– Industrial application/manufacturing– Robotics– Oil & gas– Troubleshooting in Electrical & Electronics

• Identify your problem domain and objectives• Construct a flowchart to describe your system.• The expert system must have at least 10 rules and 2

conclusion.




2.2 Expert System development team2.3 Rule-based expert system structure

Achievements:CO1

Last week contents and achievements



2.2 Expert System development team2.3 Rule-based expert system structure2.4 Expert system characteristics

Achievements:CO1


• LO1 : Able to determine the forward and backward chaining for a specific problem

2.5 Forward and backward chaining2.6 Conflict resolution

Achievements:CO1, CO5

Forward chaining and backward chaining

Rule-based expert system domain knowledge is represented by a

set of IF-THEN production rules data : a set of facts about the current

situation. The inference engine compares each

rule stored in the knowledge base with facts contained in the database.

When the IF (condition) part of the rule matches a fact, the rule is fired and its THEN (action) part is executed.

IF

THEN

Inference chainindicates how an

expert system applies the rules to reach a conclusion.

Forward Chaining

Available Data

Inference Rule

MATCHING

GOAL!

Data driven

Inference engine cycles via a match-fire procedure

Knowledge Base

Database

Fact: A is x

Match Fire

Fact: B is y

Rule: IF A is x THEN B is y

An example of an inference chain

Rule1: IF Y is trueAND D is trueTHEN Z is true

Rule2: IF X is trueAND B is trueAND E is trueTHEN Y is true

Rule3: IF A is trueTHEN X is true

A X

B

E

Y

D

Z

Forward chaining Forward chaining is the data-driven reasoning.

The reasoning starts from the known data and proceeds forward with that data.

Each time only the topmost rule is executed.

When fired, the rule adds a new fact in the database.

Any rule can be executed only once.

The match-fire cycle stops when no further rules can be fired.

Forward chaining

Match Fire Match Fire Match Fire Match Fire

Knowledge Base

Database

A C E

X

Database

A C E

L

Database

A D

YL

B

X

Database

A D

ZY

B

LX

Cycle 1 Cycle 2 Cycle 3

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

X & B & E

ZY & D

LC

L & M

A X

N

Knowledge Base Knowledge Base Knowledge Base

X

C E C EB D B D

Y Y Y Y

Forward chaining

Forward chaining is a technique for gathering information and then inferring from it whatever can be inferred.

However, in forward chaining, many rules may be executed that have nothing to do with the established goal.

Therefore, if our goal is to infer only one particular fact, the forward chaining inference technique would not be efficient.

Backward chaining Backward chaining is the goal-driven

reasoning. Characteristics; an expert system has the goal (a hypothetical solution)

and the inference engine attempts to find the evidence to prove it.

First, the knowledge base is explored to find rules that might have the desired solution. Such rules must have the goal in their THEN (action) parts.

If such a rule is found and its IF (condition) part matches data in the database, then the rule is fired and the goal is proved.

However, this is rarely the case.

Backward chaining

Thus the inference engine puts aside the rule it is working with (the rule is said to stack) and sets up a new goal, a subgoal, to prove the IF part of this rule.

Then the knowledge base is searched again for rules that can prove the subgoal.

The inference engine repeats the process of stacking the rules until no rules are found in the knowledge base to prove the current subgoal.

Backward chaining

Match Fire

KnowledgeBase

Database

AB CD E

X

Match Fire

KnowledgeBase

Database

AC DE

YX

B

Sub-Goal: X Sub-Goal: Y

Knowledge Base

Database

AC DE

ZY

B

X

Match Fire

Goal: Z

Pass 2

KnowledgeBase

Goal: Z

Knowledge Base

Sub-Goal: Y

KnowledgeBase

Sub-Goal: X

Pass 1 Pass 3

Pass 5Pass 4 Pass 6

Database

AB CD E

Database

AB CD E

Database

BC DEA

YZ

?

X

?

X & B & E

LCL & M

A X

N

ZY & DX & B & EY

ZY & D

LCL & M

A X

NLC

L & M N

X & B & EYZY & D

A X

X & B & EYZY & D

LC

L & M

A X

N

X & B & E

LCL & M

A X

N

ZY & DX & B & E

ZY & D

LC

L & M

A X

N

Y

YY

Backward chaining

How do we choose between forward and backward chaining?

If an expert first needs to gather some information and then tries to infer from it whatever can be inferred, choose the forward chaining inference engine.

However, if your expert begins with a hypothetical solution and then attempts to find facts to prove it, choose the backward chaining inference engine.

Look to the problem

So how to choose between methods?

Identify rule

Determine facts

More condition

than conlusion?

ForwardChaining

Facts leads to many facts?

BackwardChaining

Finding where

information leads

Finding if one conclusion is true when no information

Look into working knowledge/facts

Conflict resolution

Earlier we considered two simple rules for crossing a road. Let us now add third rule:

Rule 1: IF the ‘traffic light’ is green THEN the action is go

Rule 2: IF the ‘traffic light’ is red THEN the action is stop

Rule 3: IF the ‘traffic light’ is red THEN the action is go

We have two rules, Rule 2 and Rule 3, with the same IF part. Thus both of them can be set fired when the condition part is satisfied. These rules represent a conflict set. The inference engine must determine which rule to fire from such a set. A method for choosing a rule to fire when more than one rule can be fired in a given cycle is called conflict resolution.

In forward chaining, BOTH rules would be fired. Rule 2 is fired first as the topmost one, and as a result, its THEN part is executed and linguistic object action obtains value stop. However, Rule 3 is also fired because the condition part of this rule matches the fact ‘traffic light’ is red, which is still in the database. As a consequence, object action takes new value go.

Methods used for conflict resolution

Fire the rule with the highest priority. In simple applications, the priority can be established by placing the rules in an appropriate order in the knowledge base. Usually this strategy works well for expert systems with around 100 rules.

Fire the most specific rule. This method is also known as the longest matching strategy. It is based on the assumption that a specific rule processes more information than a general one.

Fire the rule that uses the data most recently entered in the database. This method relies on time tags attached to each fact in the database. In the conflict set, the expert system first fires the rule whose antecedent uses the data most recently added to the database.

Advantages of rule-based expert systems Natural knowledge representation. An expert

usually explains the problem-solving procedure with such expressions as this: “In such-and-such situation, I do so-and-so”. These expressions can be represented quite naturally as IF-THEN production rules.

Uniform structure. Production rules have the uniform IF-THEN structure. Each rule is an independent piece of knowledge. The very syntax of production rules enables them to be self-documented.

Advantages of rule-based expert systems

Separation of knowledge from its processing. The structure of a rule-based expert system provides an effective separation of the knowledge base from the inference engine. This makes it possible to develop different applications using the same expert system shell.

Dealing with incomplete and uncertain knowledge. Most rule-based expert systems are capable of representing and reasoning with incomplete and uncertain knowledge.

Disadvantages of rule-based expert systems Opaque relations between rules. Although the

individual production rules are relatively simple and self-documented, their logical interactions within the large set of rules may be opaque. Rule-based systems make it difficult to observe how individual rules serve the overall strategy.

Ineffective search strategy. The inference engine applies an exhaustive search through all the production rules during each cycle. Expert systems with a large set of rules (over 100 rules) can be slow, and thus large rule-based systems can be unsuitable for real-time applications.

Disadvantages of rule-based expert systems Inability to learn. In general, rule-based expert

systems do not have an ability to learn from the experience. Unlike a human expert, who knows when to “break the rules”, an expert system cannot automatically modify its knowledge base, or adjust existing rules or add new ones. The knowledge engineer is still responsible for revising and maintaining the system.


• LO1 : Able to determine the forward and backward chaining for a specific problem

2.5 Forward and backward chaining2.6 Conflict resolution

Achievements:CO1, CO5

Documents

BEE4333 Intelligent Control Hamzah Ahmad Ext: 2055/2141