1 expert system

7/28/2019 1 Expert System

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DMO AI @chintech

Expert system

An expert system is software that attempts toreproduce the performance of one or more

human experts, most commonly in a specific

problem domain.

There are two main methods of reasoning

when using inference rules: backward chainingand forward chaining.


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Forward chaining starts with the data availableand uses the inference rules to conclude more

data until a desired goal is reached.

Backward chaining starts with a list of goals

and works backwards to see if there is data

which will allow it to conclude any of thesegoals.


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DMO AI @chintech

Knowledge Intensive problem solving

In knowledge intensive problem solving, theinformation about the state of its problem

solving and explanations of the choice and

decisions made should be provided. The AI programs can be easily prototyped,

tested, and changed because of this

explanatory nature and also a change in a

production rule will not affect globally.


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DMO AI @chintech

Characteristics of expert system

Support inspection of their reasoningprocesses, both in presenting intermediate

steps and in answering questions about the

solution process.

The system should provide information about

the state of its problem solving andexplanations of the choices and the decisions

that the program is making.


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Allow easy modification in adding anddeleting skills from the knowledge base.

As in production system, modification in a

single rule will not affect the other rules.


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Reason heuristically. We can implement heuristic searching while

implementing expert system.


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DMO AI @chintech

Expert systems are built to form a wide range

of problems in domains such as medicine,engineering, computer, law etc.

The general problems faced in developing

expert system are,

1. Interpretation : forming high level

conclusions from collections of raw data.

2. Prediction : projecting probable

consequences of given situations.


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3. Diagnosis : determining the cause of

malfunctions in complex situations based on

observable symptoms.

4. Design : finding a configuration of systemcomponents that meets perfomance goals while

satisfying a set of design constraints.

5. Planning : devising a sequence of actions

that will achieve a set of goals given certain

conditions and run -time constraints.


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6. Monitoring: Comparing a system's observed

behaviour to its expected behaviour.

7. Instruction : assisting in the education

process in technical domain.

8. Control : governing the behaviour of a

complex environment.


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DMO AI @chintech

User interface

may employ :

Question-and-

answer

OR

menu-driven

OR

Natural

language

ORgraphics

interface

styles

Knowledge-

base editor

Inference

Engine

Explanationsubsystem

General

Knowledge

base

Case-

specific

data

User

Expert System

Architecture


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Interface :

The users interacts with the system through auser interface that simplifies communication.

Expert system interface employ a veriety of

user styles including question-and-answer,

menu-driven, or graphics interface.

The interface type is decided according to theuser needs and the requirements of the

knowledge base and inferencing system.


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DMO AI @chintech

Knowledge-base editor :

The knowledge-base editor helps theprogramer to locate and correct the bugs.

Also used when need to add a new knowledge,

helps to maintain correct rule syntax, and

perform consistency checks on the updated

knowledge-base.


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Inference engine :

It is essentially an interpreter for the

knowledge base.

In the production system inference engineperforms the recognise-act cycle.

The procedures that implement the control

cycle are separate from the production rules.


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Explanation subsystem :

The program explains its reasoning to the user.

Here user gets the informations about why a

particular piece of data is used, justification for

a system's conclusions, etc..


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DMO AI @chintech

The heart of the expert system is the knowledgebase, which contains of the knowledge of the

particular application domain.

In Rule-based expert system knowledge is

represented in the form ofif....then...rules.

Knowledge-base :


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The knowledge base of expert systems contains both factual and

heuristic knowledge.

Factual knowledge is that knowledge of the task domain that is widely

shared, typically found in textbooks or journals, and commonly agreed

upon by those knowledgeable in the particular field.Heuristic knowledge is the less rigorous, more experiential, more

judgmental knowledge of performance.

In contrast to factual knowledge, heuristic knowledge is rarely

discussed, and is largely individualistic. It is the knowledge of good

practice, good judgment, and plausible reasoning in the field. It is the

knowledge that underlies the "art of good guessing."


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Expert systems involve a considerableinvestment of money and human effort.

Attempts to solve a problem that is too

complex, too poorly understood can lead tocostly failures.

These are the guidelines to determinewhether a problem is appropriate for expert

system solution


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DMO AI @chintech

The need for the solution justifies the cost and

effort of building an expert system.

Many expert systems have been built in domains

such as mineral exploration, medicine, defence

etc...

a large potential exists for saving money, time and

human life.


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DMO AI @chintech

Human expertise is not available in allsituations where it is needed.For example in a remote mining and drilling site

if there is a need of an expert he has to travel

wasting his time where we can fulfil the need

with an expert system.


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The problem may be solved using symbolicreasoning:

When a problem can be solved using symbolic

resoning and no need of expertness in manual

acts.


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DMO AI @chintech

The problem domain is well structured and

does not require commonsence.

When a highly technical problem can be well

studied and formalised

also all the terms used are well defined and

domains have a clear conceptual models.


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When the problem may not be solved usingtraditional computing methods

Expert system is used in cases where the

traditional computing methods fails.

If a problem can be solved satisfactorily using

more traditional technoques, then it is not acandidate.


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Cooperative and articulate experts exist.The knowledge used by the expert system

comes from the experience and judgement of

human working in that domain.

So it is important that these experts be both

willing and able to share knowledge.


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DMO AI @chintech

The problem is of proper size and scope.Expert system are used in medical field, it

doesn't mean that a program is made so that all

the expertise of a doctor is captured.

Programming a particular piece of diagonising

equipment or a particular set of diagnose ispossible.


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DMO AI @chintech

The primary people involving in building anexpert system is the knowledge engineer, the

domain expert and the end user.

The knowledge engineer will decide the

representation scheme to be implemented.

Next step is to decide the software andhardware tools to be used for developing the

project.


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DMO AI @chintech

The domain expert will provide the knowledgeof the problem area.

The domain expert will explain the problem

solving techniques such as shortcuts, handling

imprecise data, evaluating partial solutionsand

all the other skills needed for the expert in that

field.


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Next is enduser who is going to decide the

design constrains.

During the design phase the skills and needs of

the user must be kept in mind. Once the knowledge engineer has got the

general overview of the problem domain and

gone through several problem solving sessions

with the expert, he is ready tobegin the design

of the system


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Selecting a way to represent the knowledge Determining the search strategy

Design the user interface

Combining all the above a prototype is made.

The knowledge engineer and expert test and

refine its knowledge.

Begin


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g

Define Problems and goals

Design and construct prototype

Test/Use system

Analyse and correct shortcomings

Ready

for final

evaluation

Are

design assumptions

still correct

Final

evaluation

No

No

Yes

Yes

Passed

Failed


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Advantages

Provides consistent answers for repetitivedecisions, processes and tasks

Holds and maintains significant levels of

information

Encourages organizations to clarify the logic

of their decision-makingNever "forgets" to ask a question, as a human

might


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Disadvantages Lacks common sense needed in some decision making

Cannot make creative responses as human expert would

in unusual circumstances

Domain experts not always able to explain their logicand reasoning

Errors may occur in the knowledge base, and lead to

wrong decisions

Cannot adapt to changing environments, unless

knowledge base is changed

1 expert system

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