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1. ROBOTICS AND EXPERT SYSTEMS.

2. WHAT IS ROBOTIC?
 Is the field of computer science and
engineering conscience with creating robot
 is a branch of AI, which is composed of
Electrical Engineering, Mechanical
Engineering, and Computer Science for
designing, construction, and application of
robots.

3. PARTS OF ROBOTS.
 Sensors
 Control system manipulator .
 Power suppler.
 Software.

4. CHARACTERISTIC OF ROBOTS.
 Movement : move around its environment by
roller, wheels or legs.
 Energy: to power itself solar , battery or
electricity.
 Intelligences: smartness and is done by
programmer.
 Sensors: to senses its surrounding.

5. WHAT IS EXPERT SYSTEMS?
 Is a computer application that performance
task that would otherwise be performed by
human expert

6. PARTS OF EXPERT SYSTEM.
 User interface.
 Knowledge based.
 Inference engine.

7. HOW EXPERT SYSTEM WORKS.
 USER INTERFACE;
 Is the system that allows a none expert user
to quarry all question to the expert system
and to receive advice.

8. HOW EXPERT SYSTEM WORKS CONT:
 KNOWLEDGE BASED.
 It is a collection of facts and rules.
 It is created from the information provide by
human expert.

9. HOW EXPERT SYSTEM WORKS CONT:
 INFERENCE ENGINE.
 It act as search engine which examine the
knowledge based for information that match the
user quarry.
 None expert user quarry the expert system by
asking question or answering question asked by
expert system
 The inference engine uses the quarry to search
the knowledge based and then provides answer
or advice to the user.

10. EXPERT SYSTEM
user
interface
Inference
engine
Knowledge
based
Knowledge
from expert
None
expert
gives
quarryquarry
Advic
e
Expert
system

11. COMPONENT OF KNOWLEDGE BASED
 It is a store for both :-
 factual knowledge based.
 heuristic knowledge based
 rule based knowledge based

12. FACTUAL KNOWLEDGE BASED
 Is the information widely acquainted by the
knowledge engineer and scholars in the task
domain.

13. HEURISTIC KNOWLEDGE BASED.
 Is about practice accurate judgment once a
ability of evaluation and gauzing.

14. KNOWLEDGE REPRESENTATION .
 Is the method used to organized and
formulizing knowledge in the knowledge
based it is in the form of IF-THEN-S RULES

15. KNOWLEDGE ACQUISITION .
 The success of any expert system mainly
depend in the quality, completeness and
accuracy of the information stored in the
knowledge based.
 The knowledge based is formed by reading
from different expert, scholar and knowledge
engineers.

16. WHO IS KNOWLEDGE ENGINEER?
 Is the person with the quality of empathy ,
quick learning and cause analyzing skills.
 He acquires information from subject expert
by recording, interviewing and observation.
 He then categories and organize information
in a meaningful way in the form of IF-THEN –
S RULES to be used by inference engine.
 He also monitor the development of expert
system.

17. INFERENCE ENGINE .
 It acquires and manipulate knowledge from
knowledge based to arrived to a particular
solution.

18. IN CASE OF RULE BASED EXPERT SYSTEM.
 It applies rules repeatedly to the facts which
are obtain from earlier rule application.
 It adds new knowledge to the knowledge
based if required.
 It resolves rule conflict when multiples rules
are applicable to a particular case.

19. STRATEGIES USED BY INFERENCE ENGINE TO
RECOMMEND SOLUTION ARE?
 Foreword chaining.
 Back word chaining.

20. FOREWORD CHAINING
 It is a strategies of expert system to answer
the question what can happen next.
 The inference engine follows the chain of
conditions and directions and finally
deduced/come up with the out come.
 It consider all the fact and rules and sort
them before concluding to a solution as
shown on next slide.

21. FOREWORD CHAINING.
fact1
fact2
fact3
fact4
and
o
r
Decision
1
Decision
2
Decision
3
and

22. BACK WORD CHAINING.
 With this strategies expert system finds out
the answer to the question why this happen.
 On the basic of what has already happened
the inference engine tries to find out which
condition could have happened in the past
for the result.
 This strategies is followed finding out cause
or reason. As shown no next slide.

23. BACK WORD CHAINING
fact2
fact1
fact3
fact4
and
or
decision
1
decision
2
and decision3

24. USER INTERFACE.
 It provides the interaction between the user
of the expert system and the expert system
itself.
 It is generally natural natural language
processing so as to be used by the user who
is well vast in the task domain.
 It explain how the expert system has arrived
to a particular outcome.

25. USER INTERFACE CONT:
 The explanations may appear in the following
forms
a) Natural language displayed on screen.
b) Verbal narration in natural language.
c) Listing rule number displayed on the screen.

26. REQUIREMENT FOR EFFICIENT EXPERT SYSTEM
USER INTERFACE.
 It should help user to accomplish their goals
in shortest possible way.
 It should be design to work for user exciting
or desire work practiced.
 Technology should be adoptable to user
requirement, not the other way a round.
 It should make efficient use of user input.

27. LIMITATION OF EXPERT SYSTEM.
 Are difficult to maintain.
 Difficult in knowledge acquisition.
 High development cost.
 Limitation of technology
 Require significant development time and
computer resources.

28. BENEFITS OF EXPERT SYSTEMS
 Availability :- they are easily available due to mass
production.
 Less production cost:- cost is reasonable and
affordable.
 Speed:- offer great speed hence reduce amount of
work.
 Less error rate:- error rate is low as compaired to
human error.
 Reduce risk:- can work in dangers environment to
human.
 Steady response:- work steadily without getting
emotional, tenses and fairtiged .

29. APPLICATION OF EXPERT SYSTEM.
 Medical domain:-are used in diagnostic
system to deduced cost of disease from
observation data.
 Mortaring system :- it is used for comparing
data continues with observed system or with
prescribe behavior e.g. mortaring leakage
along petroleum pipeline.
 Process control system

30. EXPERT SYSTEM TECHNOLOGY
 Expert system development environment
 Tools
 Shell.

31. EXPERT SYSTEM DEVELOPMENT ENVIRONMENT
 Includes:- hard wares and tools they are
working stations
 High level symbolic programming language
such as LISP program and PROLOG.
 Large data bases.

32. TOOLS.
 Includes:-powerful editors and multiple
windows.
 They provides rapid prototyping.
 They have end bit definition of model
knowledge representation and inference.

33. SHELLS
 Is an expert system without knowledge based.
 It provide the developer with knowledge acquiring,
inference engine, user interface and explanation
facilities
 Example of shells are:- JAVA expert system
shell(JESS) which provide a fully developed java
API(application programming interface) for creating
an expert system.
 Vidwan this is a shell developed is developed at
national centre for software technology in Mumbai in
1993 it enable knowledge encoding in the form of IF
THEN- S RULES

34. STEPS IN THE DEVELOPMENT OF EXPERT
SYSTEM.
 Identify the problem domain:- the problem must
be suitable for an expert system to solve it. fine
the expert in task domain for the expert system
project. Establish cost effectiveness of the
system.
 Design the systems:- identify the expert system
technology. Know and establish the degree of
integration with other system and data bases.
Realize how the concept can represent the
domain knowledge best.

35. STEPS IN THE DEVELOPMENT OF EXPERT
SYSTEM CONT.
 Develop the prototype :- the knowledge engineer uses
sample cause to test the prototype for any defenses in the
performance. End user also test the prototype of the
expert system.
 Develop and complete expert system:-test and ensure the
interaction of the expert system with all elements of its
environment including the end user data bases and other
information system. Document the expert system well.
Train the user to use the expert system.
 Maintained the expert system:-keep the knowledge based
up to date by regular review and up dates. Carter for new
interface with other information system as those system
evolves .

36. ASPECTS OF ROBOTICS.
 The robots has mechanical construction form
or shape design to accomplish a particular
task.
 They have electrical components which
power and control the machinery.
 They contained some level of computer
program that determine what when and how
a robot does somethings.

37. DIFFERENT BETWEEN ROBOTS AND ARTIFICIAL
INTELLIGENT .
ARTIFICIAL INTELLIGENT ROBOTS
They usual operates in computer
simulated world.
They operate in real physical world.
The input to an AI program is in
symbols and rules
Input to robot is analogs signal in the
form of speech waves form or images.
They need general purpose computers
to operate on
They need special hardware with
sensor and effectors .

38. ROBOTS LOCOMOTION.
 Locomotion is the mechanism that make the
robot capable of moving in its environment.
 They are various types of locomotion which
include:-legged
wheeled
combined legged and wheeled

39. LEGGED LOCOMOTION.
 These type of locomotion consumes more
power while demonstrating walking
 It requires more number of motors to a
accomplish a movement.
 It is suited for rough as well as smooth
surface makes it consumes more power for a
wheel locomotion.
 It is little difficult to implement due to stability
issues.

40. LEGGED CONT:
 The total number of possible gaits a robot
can travel depends upon the number of its
leg.
 If a robot has k legs then the number of
possible events is
N=(2K-1)!
K=number of leg
! =factious.

41. CALCULATION OF EVENTS
 In case of a two legged robot (k-2) the
number of possible events is lifting left leg.
N=(2K-1)! Release left leg.
=(2*2-1)! Lifting right leg.
=(4-1)! Release right leg
=3! Lifting both legs
togeth.
=3*2*1 release both legs.
=6 ans.

42. WHEELED LOCOMOTION
 Requires fewer number of motors to a
accomplish a movement
 It is little easy to implement as there are less
stability issues in case of more number of
wheels.
 It is power efficient as to legged locomotion.

43. WHEELED LOCOMOTION CAN BE IMPLEMENTED
IN THE FOLLOWING FORM
 Standard wheel
It rotate around the wheel axis and around the
contact.
 Caster wheel
It rotate around the wheel axis and the off set
staring joint.
 Swidish 45 degree and 90 degree wheel
They are owni wheel and rotate around the
contact point around the wheel axis and around
the roles.

44. WHEELED LOCOMOTION CAN BE IMPLEMENTED
IN THE FOLLOWING FORM CONT:
 Boll or spiral wheel.
The are owni directional wheel and are
technical difficult to impliment

45. TRACKED SLIP/SKID LOCOMOTION
 In this type of locomotion the vechcal use
tracks as in a trunk.
 The robot is stirred by moving the trunk with
different speed in same or opposite direction
 It offer stability due to large contract area and
the ground.

46. COMPONENTS OF A ROBOT
 Robots are constructed with the following:-
a) Power supply the robots are powered by batteries, solar power,
hydraulic or pneumatic power sources
b) Electric motors(AC/DC) they are required for rotational
movement.
c) Actuators they converts energy into movement.
d) Pneumatic air muscles they contract almost 40%when air is
sacked in them.
e) Muscle wires they contract by 5% when electric current is
passed through them.
f) Sensors they provide knowledge of real time information on the
task environment . Robots are equipped with vision sensors and
a tactile sensor which imitates the mechanical properties of
touch of human fingertips

47. COMPUTER VISION.
 Is the technology with which the robots can see.
The computer vision plays a vital role in the
domains of safety, security, health, access and
entertainment.
 A computer vision automatically extracts,
analysis and comprehends useful information
from a single image or an array of images.
 This process involves development of
algorithms to accomplish automatic vision
comprehension.

48. THE HARDWARE OF COMPUTER VISION SYSTEM.
 This involves:-
i. Image acquisition device eg camera
ii. A processor
iii. A software
iv. A display device for monitoring the system
v. Accessories such as camera stands, cables
and connectors.

49. USES/TASKS OF COMPUTER VISION
 Face detection:-many state of the art cameras come with
this feature which enables the computer to read the face
and take the picture of that perfect expression. it is used
to let a user access the software on a correct match
 Object recognition:-are installed in supermarkets,
cameras and high-end cars such as BMW, GM and
VOLVO.
 Estimating position:-it is used in estimating the position of
an object with respect to camera i.e the position of tumor
in human’s body.
 Optical character reader:-is a software that converts
scanned documents into editable texts which
accompanies scanner

50. ARTIFICIAL NEURAL NETWORKS.
 This is a computing system made up of a
number of simple highly interconnected
processing elements which process
information by their dynamic state exchange
to external inputs.

51. STRUCTURE OF ARTIFICIAL NEURAL NETWORKS
(ANN)
 The idea of artificial neural networks is based
on the belief that, the working of the human
brain by making the right connections can be
imitated using silicon and wires as living
neurons and dendrites .
 The human brain is composed of 100 billion
nerve cells called neurons.
 They are connected to other 1000 cells by
Axons.

52. STRUCTURE OF ARTIFICIAL NEURAL NETWORKS
CONT;
 Stimuli from the external environment or inputs from sensory
organs are accepted by dendrites. This inputs create electric
impulses which quickly travel through the neural network.
 A neuron can then sent message to other neuron to handle the
issue.
 Artificial neuron network are composed of multiple neurons which
imitates biological neurons of human brain.
 The neurons are connected by links and they interact with each
other. The nodes can take input data and perform simple
operations on the data.
 The results of this operations is passed to other neurons the
output at each node is called its activation or node value.
 Each link is associated with weight and A.N.N are capable of
learning which takes place by altering weight values.

53. THE FOLLOWING ILLUSTRATION SHOWS A
SIMPLE ARTIFICIAL.
Input hidden
output

54. TYPES OF ARTIFICIAL NEURAL NETWORK
 They are two types of artificial neural network
topologies
i. Feedforword artificial neural network.
ii. Feedback artificial neural network.

55. FEED FORWARD ARTIFICIAL NEURAL NETWORK.
 The information flow is uni-directional. A unit
sends information to other unit from which it
does not receive any information.
 There are no feedback loops.
 They are used in pattern
generation/recognition/classification.
 Have fixed inputs and outputs.

56. FEED FORWORD ARTIFICIAL NEURAL NETWORK

57. FEEDBACK ARTIFICIAL NEURAL NETWORK
 Here feedback loops are allowed. They are used in
content addressable memories
 The diagrams shown
above each arrow represents a connection between two
neurons and indicates the pathway for the flow of
information

58. CONT:
 Each connection has a weight i.e an integer
number that controls the signal between the
neurons
 If the network generates a good or desired
output, then there is no need to adjust the
weight however if the network generates a
poor on a desired output or error, then the
system alters the weights in order to improve
subsequent results.

59. MACHINE LEARNING IN ARTIFICIAL NEURAL
NETWORKS.
 Artificial neuron network are capable of
learning and they to be trained.
TYPES OF LEARNING.
1. Supervised learning.
2. Unsupervised learning.
3. Reinforcement learning

60. SUPERVISED LEARNING
 It involves a teacher that is a scholar than the
artificial neuron network itself.eg teacher feeds
some example data about which the teacher
already knows the answer.
 This type of learning is used in partner
recognition and artificial neuron network comes
up with guess while recognizing then the
teacher provide the artificial neuron network
with answer, artificial neuron network then
compare its guess with the teacher correct
answer and make adjustment according to error.

61. UNSUPERVISED LEARNING.
 It is required when there is no example data
set with known answer.eg searching a
hidden partner
 In this type of learning clustering is applied ie
dividing a set of element into group
according some unknown partner based on
the existing data set present.

62. REINFORCEMENT LEARNING
 This type of learning is built on observation.
 The artificial neuron network make a decision
by observing it environment.
 If the observation is negative the artificial
neuron network adjust its weight to make
required decision.

63. APPLICATION OF ARTIFICIAL NEURON
NETWORKS.
 Military :- they are used for weapons
 Electronics :-they are used in cording sequence
prediction
 Financial :-loan a devisor .
 Industrial:- used in manufacturing process
control.
 Transportation:- for routing system.
 Signal processing ;can be trained to process an
audio signal and filter a propriety .
 Time service prediction .