This document discusses an assignment submitted on expert system design. It begins with defining an expert system as a computer application that performs tasks done by human experts, such as medical diagnosis. It then outlines the advantages and disadvantages of using expert systems. Key advantages include providing consistent solutions, reasonable explanations, and overcoming human limitations. Disadvantages include lacking common sense, high costs, and difficulties creating inference rules. The document also differentiates expert systems from conventional computer systems and describes knowledge acquisition in expert systems as the process of extracting and organizing knowledge from human experts. It discusses forward and backward chaining and declarative versus procedural knowledge. Finally, it presents problems on analyzing a circuit output and applying laws of equivalence to a logical expression, and defining a

1. ASSIGNMENT
ON
Expert System Design
SUBMITTED TO
Dr. Rudra Banerjee
SUBMITTED BY
Shashwat Shankar
IMB2018017

2. 1. (a) Give a definition of “Expert System”.
A computer application that performs a task that would otherwise be performed by a human
expert. For example, there are expert systems that can diagnose human illnesses, make financial
forecasts, and schedule routes for delivery vehicles. Some expert systems are designed to take
the place of human experts, while others are designed to aid them.
Expert systems are part of a general category of computer applications known as artificial
intelligence. To design an expert system, one needs a knowledge engineer, an individual who
studies how human experts make decisions and translates the rules into terms that a computer
can understand.
An expert system has been reliably used in the business world to gain tactical advantages and
forecast the market’s condition. In this globalization era where every decision made in the
business world is critical for success; the assistance provided from an expert system is
undoubtedly essential and highly reliable for an organization to succeed.
Advantages of Using Expert System:
● Providing consistent solutions:
● Provides reasonable explanations:
● Overcome human limitations:
● Easy to adapt to new conditions:
Although the expert system does provide many significant advantages, it does have its
drawbacks as well.
Disadvantages of Using Expert System:
● Lacks common sense:
● High implementation and maintenance cost:
● Difficulty in creating inference rules:
● May provide wrong solutions:
Figure:- General Structure of Expert System

3. For example, biomedical researchers started creating computer-aided systems for diagnostic
applications in medicine and biology. These early diagnostic systems used patients’ symptoms
and laboratory test results as inputs to generate a diagnostic outcome. These systems were often
described as the early forms of expert systems. However, researchers had realized that there were
significant limitations when using traditional methods such as flow-charts statistical
pattern-matching, or probability theory.
(b) Outline how Expert Systems can be distinguished from more conventional computer
systems.
Expert Systems Conventional computer systems.
1. In the expert system approach all of
the problem-related expertise is
encoded in data structures only, none
is in programs.
2. In expert systems, the use of
knowledge is vital.
3. Expert systems are capable of
explaining how a particular conclusion
is reached and why requested
information is needed during a
process. However, the problems are
solved more efficiently than a
conventional system by an expert
system
4. Generally in an expert system, it uses
the symbolic representations for
knowledge i.e. the rules, different
forms of networks, frames, scripts, etc.
and performs their inference through
symbolic computations.
1. In conventional applications, problem
expertise is encoded in both program
and data structures.
2. Conventional system data is used
more efficiently than knowledge.
3. Conventional systems are not capable
of explaining a particular conclusion
for a problem. These systems try to
solve in a straight forward manner.
4. But conventional systems are unable
to express these terms. They just
simplify the problems in a straight
forward manner and are incapable to
express the “how, why” questions.
Also, the problem-solving tools that
are present in the expert systems are
purely absent in conventional systems.
(c) In the context of Expert Systems, describe what the term “knowledge acquisition”
covers.
Knowledge acquisition is the process to extract structure and organize the knowledge from
various sources of human experts and is also consider as the process of adding new knowledge
and to change which was anonymously acquired to the knowledge base. It is mainly used in
system development. The purpose of knowledge acquisition is to elaborate on the capability of
the system to improve the specific task of the performance consisting of facts, rules, concepts,
procedures, heuristics, formulas, relationships, or other useful information.

4. The knowledge acquisition process facilitates the assimilation of knowledge and experiences of
different specialties. For example, an agricultural diagnostic expert system requires the
integration of specialists in various fields such as nutrition, plant pathology, entomology,
breading, and production. When the problem occurs, the system can help the user more efficient
in identifying the cause of the problem. It then helps in consulting a document that handles a
specific problem
Expert databases are developed through a formal knowledge acquisition process which includes
identification, conceptualization, formalization, implementation, and testing. To extract
information from a human expert, we are using the common means of extracting methods like
interviews, transactional tracking, observation, case study, and self-reporting choices. Expert
Systems put together the investigation and interpretation of data input with specific rules of
actions and facts to arrive at a recommended outcome using programmatic and physical
representation of logic, data, and choice.
The importance of assuring the quality of expert systems is now widely recognized. Quality
assurance is a major issue in the development of expert systems. A consensus has been reached
in the literature that the evaluation of expert systems to ensure their reliability involves two
principal activities, usually called verification and validation. Studies have shown that
verification, can lead to the early detection of errors that otherwise would have remained even
after extensive validation tests. This verification and validation process is molded in the IS
Expert module of the system prototype.
Figure:- Integration of Knowledge acquisition and Expert System

5. 2. Differentiate the following :
(a) Forward and Backward Chaining.
S.No Forward Chaining Backward Chaining
1. Forward chaining starts from
known facts and applies the
inference rule to extract more data
units until it reaches the goal.
Backward chaining starts from the goal and
works backward through inference rules to
find the required facts that support the goal.
2. It is a bottom-up approach It is a top-down approach
3. It is known as a data-driven
inference technique as we reach to
the goal using the available data.
It is known as a goal-driven technique as we
start from the goal and divide it into sub-goal
to extract the facts.
4. Its reasoning applies a breadth-first
search strategy.
Its reasoning applies a depth-first search
strategy.
5. It tests for all the available rules It only tests for a few required rules.
6. It is suitable for the planning,
monitoring, control, and
interpretation application.
It is suitable for diagnostic, prescription, and
debugging applications.
7. It can generate an infinite number
of possible conclusions.
It generates a finite number of possible
conclusions.
8. It operates in the forward direction. It operates in the backward direction.

6. 9. It is aimed at any conclusion. It is only aimed for the required data.
(b) Declarative Knowledge and Procedural Knowledge
S.No Procedural Knowledge Declarative Knowledge
1.
It is also known as
Interpretive knowledge.
It is also known as Descriptive
knowledge.
2.
It means how a particular
thing can be accomplished.
While Declarative Knowledge
means basic knowledge about
something.
3.
It is generally not used means
it is not more popular.
It is more popular.
4. It can’t easily communicate. It can be easily communicated.
5.
It is generally
process-oriented in nature.
It is data-oriented in nature.
6.
In Procedural Knowledge
debugging and validation is
not easy.
In Declarative Knowledge
debugging and validation is easy.

7. 3. (a) What is the output of this circuit?

8. (b) ¬(p ∨ (¬p ∧ q)) ≡ ¬p ∧ ¬q) Using laws of equivalence, show that

10. 4. P be a probability function on a sample space Ω. Let hi ⊆ Ω, i = 1, . . . , n, n > [10]Let 1,
be mutually exclusive hypotheses with P(hi) > 0, such that ∪ n i=1hi = Ω (that is, they are
collectively exhaustive). Furthermore, let e ⊆ Ω such that P (e) > 0.
Then, the following property holds:
P(hi |e) = P(e|hi) · P(hi) Pn j=1 P(e|hj ) · P(hj )