1. Application of expert
system in road
transport
By
ASHISH BODHANKAR 2010B4A2594H
VARUN TUMATI 2010B3AB663P
BHARGAV DUTT 2010B2A2304P

2. Contents
OUTLINE
1
3
EXPERT SYSTEM INTRODUCTION
2
THE DESIGN OF A RULE BASED EXPERT
3 SYSTEM
DEVELOPMENT OF AN EXPERT SYSTEM
4
ADVANTAGES OF AN EXPERT SYSTEM
5
APPLICATION OF EXPERT SYSTEMS IN
6 NAVATA

3. 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.

4. 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.

5. 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.

6. 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:

7. 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.

8. 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.

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

10. The Design of Rule-Based Expert
System
The 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.

11. The Design of Rule-Based Expert
System
 The 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.

12. 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.

13. 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.

14. 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

15. 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.

16. Development Of An Expert System
Phase 3: Prototyping
 Build a small prototype
 Test, improve and expand it
 Demonstrate and analyse feasibility
 Complete the design

17. 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.

18. Development Of An Expert System
Phase 4: System development
 Build the knowledge base
 Test, evaluate and improve the knowledge base
 Plan for integration

19. 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.

20. 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

21. 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.

22. Development Of An Expert System
Phase 6: Post-implementation
 Operation
 Maintenance
 Upgrading
 Periodic evaluation

23. Development Of An Expert System
 Comments 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.

24. 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.

25. Rule-Based Expert System
In 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.

26. 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.

27. 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.

28. A Unreal Expert System Example
Rule 1: if
the engine is getting gas, and
the engine will turn over,
then
the problem is spark plugs.
Rule 2: if
the engine does not turn over, and
the lights do not come on
then
the problem is battery or cables.
Rule 3: if
the engine does not turn over, and
the lights do come on
then
the problem is the starter motor.
Rule 4: if
there is gas in the fuel tank, and
there is gas in the carburetor.
then
the engine is getting gas.

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

30. 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.

31. The production system after Rule 1
has fired.

32. 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”.

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

34. The and/or graph searched in the car diagnosis
example, with the conclusion of Rule 4 matching the
first premise of Rule 1.

35. Explanation And Transparency In
Goal-driven Reasoning
The 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

36. Explanation And Transparency In
Goal-driven Reasoning
 In 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.

37. Explanation And Transparency In
Goal-driven Reasoning
 The 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.”

38. Explanation And Transparency In
Goal-driven Reasoning
Gas in fuel tank?
Yes
Gas in carburettor?
Yes
Engine 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:
if
gas in fuel tank, and
gas in carburettor
then
engine is getting gas.
gas in fuel tank was given by the user.
gas in carburettor was given by the user
 .

39. Data-driven Reasoning
The 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.

40. 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

41. A Unreal Expert System Example
Rule 1: if
(not askable) the engine is getting gas, and
the engine will turn over,
then
the problem is spark plugs.
Rule 2: if
the engine does not turn over, and
the lights do not come on
then
the problem is battery or cables.
Rule 3: if
the engine does not turn over, and
the lights do come on
then
the problem is the starter motor.
Rule 4: if
there is gas in the fuel tank, and
there is gas in the carburettor.
then
the engine is getting gas.

42. Data-Driven Reasoning

43. 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.

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

45. 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.

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

47. 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.

48. 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.

49. 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.

50. 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)

51. 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.

52. 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.

53. 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 kinds
Planning and scheduling
Configuration of manufactured objects
Financial decision making
Knowledge publishing
Process monitoring and control

54. Application Of Expert System In
Navata
Expert 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.

55. Transshipment Section At Navata
The list of departments under the transshipment
section-
Loading & Unloading section
Accounts section.
Dispatch section.
Invoice section.

56. www.themegall
Transshipment section ery.com
Loading & Unloading section
Accounts Section
Dispatch Section
Invoice section

57. 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.

58. www.themegallery.com
Article damage
Damage could have Damage could have
been done while been done during
loading/unloading transport
The good will be
The good will be
replaced and the
replaced,company
hammali will be
pays the price.
charged.

59. 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

60. www.themegallery.com
Delay in
delivery
Due to misplacement Due to bandhs and Due to vehicle
of goods riots breakdown
The vehicle is The vehicle is
halted and regular repaired and then
process starts after the goods are
the bandh delivered

61. www.themegall
ery.com
Misplacement
of goods
Short Discrepancy Good loaded in
loading in LR wrong vehicle
The customer contacts The supervisor checks
The company verifies
the excess articles the loading sheet and
the LR and contacts
section and produces the good is loaded in
the customer
the consignment copy the correct vehicle

62. 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.

63. www.themegall
ery.com
Problems in
Dispatch
section
Less number
Less staff of vehicles LR mistake
Excess kilometers
Excess shift Vehicles with run by the vehicle
for the repairs are due to the mistake is
credited into the
working staff used personal account

64. 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.

65. www.themegall
ery.com
If the reason is
justifiable nothing
is done
Driver and the
Discrepency in the
invoice agent are
contacted If proper reason is
not given
driver/agent should
pay the penalty

66. 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.

67. 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.

68. 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

69. 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.