expert systems

Application of expert system in road transport

ByASHISH BODHANKAR 2010B4A2594H

VARUN TUMATI 2010B3AB663P

BHARGAV DUTT 2010B2A2304P

Contents

EXPERT SYSTEM INTRODUCTION2

ADVANTAGES OF AN EXPERT SYSTEM5

OUTLINE31

THE DESIGN OF A RULE BASED EXPERT SYSTEM33

DEVELOPMENT OF AN EXPERT SYSTEM4

APPLICATION OF EXPERT SYSTEMS IN NAVATA6

Definition

An expert system is a computer system that emulates the decision making ability of a human expert.

Expert system are designed to solve complex problems by reasoning about knowledge like an expert.

Expert System Introduction

Human experts are able to perform at a successful level because they know a lot about their areas of expertise.

An Expert System use knowledge specific to a problem domain to provide “expert quality” performance in that application area.

As with skilled humans, expert systems tend to be specialists, focusing on a narrow set of problems.


Because of their heuristic, knowledge intensive nature, expert systems generally: Support inspection of their reasoning processes. Allow easy modification in adding and deleting

skills from knowledge base. Reason heuristically, using knowledge to get

useful solutions.


Expert systems are built to solve a wide range of problems in domain such as medicine, math, engineering, chemistry, geology, computer science, business, low, defense and education

These programs address a variety of problems, the following list is a summary of general expert system problem categories:


Interpretation --- forming high-level conclusions from collections of raw data.

Prediction --- projecting probable consequences of given situations.

Diagnosis --- determining the cause of malfunctions based on observable symptoms.


Design --- finding a configuration of system components that meets performance goals while satisfying a set of design constrains.

Planning --- devising a sequence of actions that will achieve a set of goals given starting conditions and runtime constrains.

The Design of Rule-Based Expert System• architecture of a typical expert system for a particular

problem domain.

The Design of Rule-Based Expert SystemThe hear of the expert system is the knowledge base,

which contains the knowledge of a particular application domain.

In a rule-based expert system, this knowledge is most often represented in the form of if…then…

In the figure, the knowledge base contains both general and case-specific information.

The Design of Rule-Based Expert SystemThe inference engine applies the knowledge to the solution of

actual problems.

It is important to maintain this separation of the knowledge and inference engine because: Makes it possible to represent knowledge in a more natural fashion. Expert system builder can focus on capturing and organizing problem-

solving knowledge than the details of code implementation. Allow change to be made easily. Allows the same control and interface software to be used in different

systems.

Development Of An Expert System

Phase 1: Project initialisation Problem definition. Needs assessment. Evaluation of alternative solutions. Verification that an ES approach is

appropriate. Consideration of management issues.


Comment on Phase 1: it's important to discover what problem/problems

the client expects the system to solve for them, and what their real needs are. The problem may very well be that more knowledge is needed in the organisation, but there may be other, better ways to provide it.

'Management issues' include availability of finance, legal constraints, and finding a 'champion' in top management.


Phase 2: System analysis & design Produce conceptual design Decide development strategy Decide sources of knowledge, and ensure

co-operation Select computer resources Perform a feasibility study Perform a cost-benefit analysis


Comment on Phase 2: the 'conceptual design' will describe the

general capabilities of the intended system, and the required resources.


Phase 3: Prototyping Build a small prototype Test, improve and expand it Demonstrate and analyse feasibility Complete the design


Comments on Phase 3:

It's important to establish the feasibility (economic, technical and operational) of the system before too much work has been done, and it's easier to do this if a prototype has been built.


Phase 4: System development Build the knowledge base

Test, evaluate and improve the knowledge base

Plan for integration



The evaluation of an expert system (in terms of validation and verification) is a particularly difficult problem.


Phase 5: Implementation Ensure acceptance by users Install, demonstrate and deploy the system Arrange orientation and training for the

users Ensure security Provide documentation Arrange for integration and field testing



If the system is not accepted by the users, the project has largely been a waste of time.

Field testing (leading to refinement of the system) is essential, but may be quite lengthy.


Phase 6: Post-implementation Operation Maintenance Upgrading Periodic evaluation

Development Of An Expert SystemComments on Phase 6:

A person or group of people must be put in charge of maintenance (and, perhaps, expansion). They are responsible for correcting bugs, and updating the knowledgebase. They must therefore have some knowledge engineering skills.

The system should be evaluated, once or twice a year, in terms of its costs & benefits, its accuracy, its accessibility, and its acceptance.

Rule-Based Expert System

Rule based expert system represent problem-solving knowledge as if…then…

It is one of the oldest techniques for representing domain knowledge in an expert system.

It is also one of the most natural and widely used in practical and experimental expert system.

Rule-Based Expert SystemIn a goal-driven expert system, the goal expression

is initially placed in working memory

The system matches rule conclusions with the goal, selecting one rule and placing its premises in the working memory.

This corresponds to a decomposition of the problems’ goal into simpler sub goals.

The process continues in the next iteration of the production system, with these premises becoming the new goals to match.

Advantages of a rule based expert system

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 a rule based expert system

Dealing with incomplete and uncertain knowledge.

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

A Unreal Expert System ExampleRule 1: if

the engine is getting gas, andthe engine will turn over,thenthe problem is spark plugs.

Rule 2: ifthe engine does not turn over, andthe lights do not come onthenthe problem is battery or cables.

Rule 3: ifthe engine does not turn over, andthe lights do come onthen the problem is the starter motor.

Rule 4: ifthere is gas in the fuel tank, andthere is gas in the carburetor.thenthe engine is getting gas.

The production system at the start of a consultation

in the car diagnostic example.

The production system at the start of a consultation in the car diagnostic example.

Three rules match with this expression in working memory: rule 1, 2, and 3.

If we resolve conflicts in favor of the lowest-numbered rule, then rule 1 will fire.

This cause X to be bound to the value spark plugs and the premises of rule 1 to be placed in the working memory.

The production system after Rule 1 has fired.

The production system after Rule 1 has fired.Note that there are two premises to rule 1, both of

which must be satisfied to prove the conclusion true.

So now we need to find out whether The engine is getting gas, and The engine will turn over.

We may then fire rule 4 for whether “The engine is getting gas”.

The system after Rule 4 has fired. Note the stack-based approach to goal reduction.

The and/or graph searched in the car diagnosis example, with the conclusion of Rule 4 matching the

first premise of Rule 1.

Explanation And Transparency In Goal-driven ReasoningThe following dialogue begins with the computer

asking the user about the goals present in the working memory: Gas in fuel tank?

YES Gas in carburetor?

YES Engine will turn over?

WHY

Explanation And Transparency In Goal-driven ReasoningIn general, the two questions answered by rule-based expert

system are WHY? and HOW?

WHY means “why did you ask for that information” The answer is the current rule that the production system is attempting

to fire.

HOW means “How did you get the result” The answer is the sequence of rules that were used to conclude a goal.

Explanation And Transparency In Goal-driven ReasoningThe following dialogue begins with the computer asking the

user about the goals present in the working memory: Gas in fuel tank?YES Gas in carburetor?YES Engine will turn over?WHY

It has been established that:1. The engine is getting gas, 2. The engine will turn over, (we need to know)So that we can make the conclusion that “Then the problem is the spark plugs.”

Explanation And Transparency In Goal-driven ReasoningGas in fuel tank?YesGas in carburettor?YesEngine will turn over?Why It has been established that:1. The engine is getting gas, 2. The engine will turn over,Then the problem is the spark plugs. How the engine is getting gas This follows from rule 4:ifgas in fuel tank, andgas in carburettorthenengine is getting gas.gas in fuel tank was given by the user.gas in carburettor was given by the user .

Data-driven ReasoningThe previous example exhibits goal-driven search.

The search was also depth-first search.

Breadth-first search is more common in Data Driven reasoning.

The algorithm for this category is simple: compare the contents of working memory with the conditions of each rule in the rule base according to the order of the rules.

Data-driven Reasoning

If a piece of information that makes up the premise of a rule is not the conclusion of some other rule,then that fact will be deemed “askable”.

For example: the engine is getting gas is not askable in the premise of rule 1

A Unreal Expert System ExampleRule 1: if

(not askable) the engine is getting gas, andthe engine will turn over,thenthe problem is spark plugs.

Rule 2: ifthe engine does not turn over, andthe lights do not come onthenthe problem is battery or cables.

Rule 3: ifthe engine does not turn over, andthe lights do come onthen the problem is the starter motor.

Rule 4: ifthere is gas in the fuel tank, andthere is gas in the carburettor.thenthe engine is getting gas.

Data-Driven Reasoning

Data-Driven Reasoning

The premise, the engine is getting gas is NOT askable, so rule 1 fails and continue to rule 2.

The engine does not turn over is askable.

Suppose the answer to this query is false, so “the engine will turn over” is placed in working memory.

The production system after evaluating the first premise of Rule 2, which then fails.

The production system after evaluating the first premise of Rule 2, which then fails.Rule 2 fails, since the first of two AND premises is

false, we move to rule 3.

Where rule 3 also fails.

So finally, we move to rule 4.

The data-driven production system after considering Rule 4, beginning its second pass through the rules.

The data-driven production system after considering Rule 4, beginning its second pass through the rules.At this point, all the rules have been considered.

With the new contents of working memory, we consider the rules in order for the second round.

Advantages of Expert System

Permanence - Expert systems do not forget, but human experts may.

Reproducibility - Many copies of an expert system can be made, but training new human experts is time-consuming and expensive.

Completeness - An expert system can review all the transactions, a human expert can only review a sample.


Completeness - An expert system can review all the transactions, a human expert can only review a sample.

Breadth - The knowledge of multiple human experts can be combined to give a system more breadth that a single person is likely to achieve.

Timeliness - Fraud and/or errors can be prevented. Information is available sooner for decision making.


Efficiency - can increase throughput and decrease personnel costs Although expert systems are expensive to build and

maintain, they are inexpensive to operate. Development and maintenance costs can be spread over

many users. The overall cost can be quite reasonable when compared

to expensive and scarce human experts. Cost-savings:

Wages - (elimination of a room full of clerks)

When to Use Expert Systems

Develop an expert system if it can do any of the following: Provide a high potential payoff or significantly

reduce downside risk. Capture and preserve irreplaceable human

expertise. Solve a problem that is not easily solved using

traditional programming techniques. Develop a system more consistent than human

experts.

When to Use Expert Systems

Provide expertise needed at a number of locations at the same time or in a hostile environment that is dangerous to human health.

Provide expertise that is expensive or rare. Develop a solution faster than human experts can Provide expertise needed for training and.

development to share the wisdom and experience of human experts with a large number of people.

The Application Of Expert Systems

Its applications spread in a wide range i.e. in industrial and commercial problems etc.Diagnosis and troubleshooting of devices and system

of all kindsPlanning and schedulingConfiguration of manufactured objectsFinancial decision makingKnowledge publishingProcess monitoring and control

Application Of Expert System In NavataExpert system has many applications at navata:

i. Helpful for new recruitments.ii. Fast response in solving problems.iii. Assists in decision making.iv. Increased reliability.v. Multiple expertise.

Transshipment Section At Navata

The list of departments under the transshipment section-Loading & Unloading sectionAccounts section.Dispatch section.Invoice section.

www.themegallery.comTransshipment section

Loading & Unloading section

Accounts Section

Dispatch Section

Invoice section

Loading & Unloading Section

Goods are loaded/unloaded in this section.

Load sheets and unload sheets are prepared.

The lorry driver is given an invoice and a

waybill(Lorry Receipt) that he has to carry with him.This data is entered into the waybill and invoice.

www.themegallery.com

Article damage

Damage could have been done while

loading/unloading

Damage could have been done during

transport

The good will be replaced and the hammali will be

charged.

The good will be replaced,company

pays the price.


Excess/shortage of articles

If any two parties have same type of article then

due to the mistake of hamalis excess/shortage

takes place

The customer produces the consignment copy

and the company delivers the good to

correct party


Delay in delivery

Due to misplacement of goods

Due to bandhs and riots

Due to vehicle breakdown

The vehicle is halted and regular process starts after

the bandh

The vehicle is repaired and then

the goods are delivered

www.themegallery.comMisplacement of goods

Short loading

The customer contacts the excess articles section and

produces the consignment copy

Discrepancy in LR

The company verifies the LR and contacts the customer

Good loaded in wrong vehicle

The supervisor checks the loading sheet and the good

is loaded in the correct vehicle

Dispatch Section

This section receives the waybills and receipts from the load/unload section and passes to the transshipment computer section.

It receives the receipts from the drivers and monitor their work.


Problems in Dispatch section

Less staff

Excess shift for

the working

staff

Less number of

vehicles

Vehicles with

repairs are used

LR mistake

Excess kilometers run by the vehicle due

to the mistake is credited into the personal account

Invoice Section

This section receives the invoice from the lorry drivers.

Invoice sheets are entered here.

All the offline information regarding invoice is made online.


Discrepency in the invoice

Driver and the agent are contacted

If the reason is justifiable nothing is done

If proper reason is not given driver/agent should pay the penalty

Cons of Expert System

Every system has it’s pros and cons, coming to the expert system :

Common sense - In addition to a great deal of technical knowledge, human experts have common sense. It is not yet known how to give expert systems common sense.

Creativity - Human experts can respond creatively to unusual situations, expert systems cannot.

Cons of Expert System

Degradation - Expert systems are not good at recognizing when no answer exists or when the problem is outside their area of expertise.

Sensory Experience - Human experts have available to them a wide range of sensory experience; expert systems are currently dependent on symbolic input.

Learning - Human experts automatically adapt to changing environments; expert systems must be explicitly updated.

Conclusion

Expert will retire in a few years taking his expertise with him. So, the company needs to develop an expert system to diagnose the difficult problems.

The system can also be used to provide training to the new recruitments

Conclusion

It fit the needs of the individual learner by guiding him in various prospects.

Today's powerful PCs are starting to put such trainers, called ICAI (Intelligent Computer Assisted Instruction) systems, within everybody's reach.

Date post:	13-Nov-2014
Category:	Technology
Upload:	varun-tumati
View:	1,907 times
Download:	0 times

expert systems

Technology