Unit -4
Artificial Intelligence Assignment
Q-1) What is an expert system?
Ans) In artificial intelligence, an expert system is a computer
system that emulates the decision-making ability of a human
expert. Expert systems are designed to solve complex problems
by reasoning through bodies of knowledge, represented mainly as
if-then rules rather than through conventional procedural code.
Expert systems have specific knowledge to one problem domain,
e.g., medicine, science, engineering, etc. The expert’s knowledge
is called a knowledge base, and it contains accumulated
experience that has been loaded and tested in the system. Much
like other artificial intelligence systems, expert system’s
knowledge may be enhanced with add-ons to the knowledge
base, or additions to the rules. The more experience entered into
the expert system, the more the system can improve its
performance.
Characteristics of expert systems:
Highly responsive
Reliable
Understandable
High performance
Q-2) Why is Expert System needed?
Ans) Before using any technology, we must have an idea about
why to use that technology and hence the same for the ES.
Although we have human experts in every field, then what is the
need to develop a computer-based system. So below are the
points that are describing the need of the ES:
1. No memory Limitations: It can store as much data as
required and can memorize it at the time of its application.
But for human experts, there are some limitations to
memorize all things at every time.
2. High Efficiency: If the knowledge base is updated with the
correct knowledge, then it provides a highly efficient output,
which may not be possible for a human.
3. Expertise in a domain: There are lots of human experts in
each domain, and they all have different skills, different
experiences, and different skills, so it is not easy to get a
final output for the query. But if we put the knowledge gained
from human experts into the expert system, then it provides
an efficient output by mixing all the facts and knowledge
4. Not affected by emotions: These systems are not affected
by human emotions such as fatigue, anger, depression,
anxiety, etc.. Hence the performance remains constant.
5. High security: These systems provide high security to
resolve any query.
6. Considers all the facts: To respond to any query, it checks
and considers all the available facts and provides the result
accordingly. But it is possible that a human expert may not
consider some facts due to any reason.
7. Regular updates improve the performance: If there is an
issue in the result provided by the expert systems, we can
improve the performance of the system by updating the
knowledge base.
Q-3) Write a Brief about expert system architecture
Ans) There are 5 Components of expert systems:
Knowledge Base
Inference Engine
Knowledge acquisition and learning module
User Interface
Explanation module
Knowledge base: The knowledge base in an expert
system represents facts and rules. It contains knowledge
in specific domains along with rules in order to solve
problems, and form procedures that are relevant to the
domain.
Inference engine: The most basic function of the inference
engine is to acquire relevant data from the knowledge
base, interpret it, and to find a solution as per the user’s
problem. Inference engines also have explanationatory
and debugging abilities.
Knowledge acquisition and learning module: This
component functions to allow the expert systems to
acquire more data from various sources and store it in the
knowledge base.
User interface: This component is essential for a non-
expert user to interact with the expert system and find
solutions.
Explanation module: As the name suggests, this module
helps in providing the user with an explanation of the
achieved conclusion
Q-4) What are the components of expert system?
Ans) An expert system mainly consists of three components:
o User Interface
o Inference Engine
o Knowledge Base
1. User Interface
With the help of a user interface, the expert system interacts with
the user, takes queries as an input in a readable format, and
passes it to the inference engine. After getting the response from
the inference engine, it displays the output to the user. In other
words, it is an interface that helps a non-expert user to
communicate with the expert system to find a solution.
2. Inference Engine(Rules of Engine)
o The inference engine is known as the brain of the expert
system as it is the main processing unit of the system. It
applies inference rules to the knowledge base to derive a
conclusion or deduce new information. It helps in deriving an
error-free solution of queries asked by the user.
o With the help of an inference engine, the system extracts the
knowledge from the knowledge base.
o There are two types of inference engine:
o Deterministic Inference engine: The conclusions drawn
from this type of inference engine are assumed to be true. It
is based on facts and rules.
o Probabilistic Inference engine: This type of inference
engine contains uncertainty in conclusions, and based on
the probability.
3. Knowledge Base
o The knowledgebase is a type of storage that stores
knowledge acquired from the different experts of the
particular domain. It is considered as big storage of
knowledge. The more the knowledge base, the more precise
will be the Expert System.
o It is similar to a database that contains information and rules
of a particular domain or subject.
o One can also view the knowledge base as collections of
objects and their attributes. Such as a Lion is an object and
its attributes are it is a mammal, it is not a domestic animal,
etc.
Components of Knowledge Base
o Factual Knowledge: The knowledge which is based on
facts and accepted by knowledge engineers comes under
factual knowledge.
o Heuristic Knowledge: This knowledge is based on practice,
the ability to guess, evaluation, and experiences.
Q-5) Give some examples of Expert System.
Ans) Below are some popular examples of the Expert
System:
o DENDRAL: It was an artificial intelligence project that was
made as a chemical analysis expert system. It was used in
organic chemistry to detect unknown organic molecules with
the help of their mass spectra and knowledge base of
chemistry.
o MYCIN: It was one of the earliest backward chaining expert
systems that was designed to find the bacteria causing
infections like bacteraemia and meningitis. It was also used
for the recommendation of antibiotics and the diagnosis of
blood clotting diseases.
o PXDES: It is an expert system that is used to determine the
type and level of lung cancer. To determine the disease, it
takes a picture from the upper body, which looks like the
shadow. This shadow identifies the type and degree of harm.
o CaDeT: The CaDet expert system is a diagnostic support
system that can detect cancer at early stages.
Q-6) What are the Advantages and Limitation of expert System?
Ans) Advantages of Expert System
o These systems are highly reproducible.
o They can be used for risky places where the human
presence is not safe.
o Error possibilities are less if the KB contains correct
knowledge.
o The performance of these systems remains steady as it is
not affected by emotions, tension, or fatigue.
o They provide a very high speed to respond to a particular
query.
Limitations of Expert System
o The response of the expert system may get wrong if the
knowledge base contains the wrong information.
o Like a human being, it cannot produce a creative output for
different scenarios.
o Its maintenance and development costs are very high.
o Knowledge acquisition for designing is much difficult.
o For each domain, we require a specific ES, which is one of
the big limitations.
o It cannot learn from itself and hence requires manual
updates
Q-7) What are the application of Expert system?
Ans) Applications of Expert System
o In designing and manufacturing domain
It can be broadly used for designing and manufacturing
physical devices such as camera lenses and automobiles.
o In the knowledge domain
These systems are primarily used for publishing the relevant
knowledge to the users. The two popular ES used for this
domain is an advisor and a tax advisor.
o In the finance domain
In the finance industries, it is used to detect any type of
possible fraud, suspicious activity, and advise bankers that if
they should provide loans for business or not.
o In the diagnosis and troubleshooting of devices
In medical diagnosis, the ES system is used, and it was the
first area where these systems were used.
o Planning and Scheduling
The expert systems can also be used for planning and
scheduling some particular tasks for achieving the goal of
that task.
Q-8) What is the need of expert system while we human expert in
domains?
Ans) Expert systems can automate specific knowledge based
tasks such as credit approval, fraud detection, loan application
scoring, and so forth. Knowledge from a human expert can be
translated into rules or cases and applied to datasets collected
from information gathering systems. Information jobs relying upon
human expertise for decision making are the best candidates to
be automated by expert systems. The cost of maintaining expert
systems may be high due to the level of conflicting rules within a
given system. Standard cost benefit tradeoff analysis is typically
performed before an expert system project is undertaken.
Q-9) What are the capabilities of an Expert system?
Ans) Below are some capabilities of an Expert System:
o Advising: It is capable of advising the human being for the
query of any domain from the particular ES.
o Provide decision-making capabilities: It provides the
capability of decision making in any domain, such as for
making any financial decision, decisions in medical science,
etc.
o Demonstrate a device: It is capable of demonstrating any
new products such as its features, specifications, how to use
that product, etc.
o Problem-solving: It has problem-solving capabilities.
o Explaining a problem: It is also capable of providing a
detailed description of an input problem.
o Interpreting the input: It is capable of interpreting the input
given by the user.
o Predicting results: It can be used for the prediction of a
result.
o Diagnosis: An ES designed for the medical field is capable
of diagnosing a disease without using multiple components
as it already contains various inbuilt medical tools.
Q-10) What is LISP?
Ans) It is particularly suitable for Artificial Intelligence programs,
as it processes symbolic information effectively.
Common Lisp originated, during the 1980s and 1990s, in an
attempt to unify the work of several implementation groups that
were successors to Maclisp, like ZetaLisp and NIL (New
Implementation of Lisp) etc.
It serves as a common language, which can be easily extended
for specific implementation.
Programs written in Common LISP do not depend on machine-
specific characteristics, such as word length etc.
Features of Common LISP
It is machine-independent
It uses iterative design methodology, and easy extensibility.
It allows updating the programs dynamically.
It provides high level debugging.
It provides advanced object-oriented programming.
It provides a convenient macro system.
It provides wide-ranging data types like, objects, structures,
lists, vectors, adjustable arrays, hash-tables, and symbols.
It is expression-based.
It provides an object-oriented condition system.
It provides a complete I/O library.
It provides extensive control structures.
Applications Built in LISP
Large successful applications built in Lisp.
Emacs
G2
AutoCad
Igor Engraver
Yahoo Store
Q-11) WAP to find the sum & average of 3 numbers in LISP.
Ans) (princ "Enter how many numbers to read: ")
(defparameter a (read))
(defun num ()
(loop repeat a
sum (progn
(format *query-io* "Enter a number: ")
(finish-output)
(parse-integer (read-line *query-io*)))))
(defun stats ()
(let ((sum (num)))
(format t "Sum: ~d ~%" sum)
(format t "Average: ~d ~%" (/ sum a))))
Q-12) What are the various AI languages?
Ans)
Python. Python is the ideal coding language used for
machine learning, NLP, and neural network connections. ...
Prolog. ...
LISP. ...
Java. ...
C++ ...
Conclusion
