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.
Expert System Introduction
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 System Introduction
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:
Expert System Introduction
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.
Expert System Introduction
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.
Development Of An Expert System
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.
Development Of An Expert System
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
Development Of An Expert System
Comment on Phase 2: the 'conceptual design' will describe the
general capabilities of the intended system, and the required resources.
Development Of An Expert System
Phase 3: Prototyping Build a small prototype Test, improve and expand it Demonstrate and analyse feasibility Complete the design
Development Of An Expert System
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.
Development Of An Expert System
Phase 4: System development Build the knowledge base
Test, evaluate and improve the knowledge base
Plan for integration
Development Of An Expert System
Comments on Phase 4:
The evaluation of an expert system (in terms of validation and verification) is a particularly difficult problem.
Development Of An Expert System
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
Development Of An Expert System
Comments on Phase 5:
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.
Development Of An Expert System
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.
Advantages of Expert System
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.
Advantages of Expert System
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.
www.themegallery.com
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
www.themegallery.com
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.
www.themegallery.com
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.
www.themegallery.com
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.