AIBA MODULE 1 Expert System
AIBA MODULE 1 Expert System
AIBA MODULE 1 Expert System
The expert system is a part of AI, and the first ES was developed in the year 1970, which
was the first successful approach of artificial intelligence. It solves the most complex
issue as an expert by extracting the knowledge stored in its knowledge base. The
system helps in decision making for compsex problems using both facts and
heuristics like a human expert. It is called so because it contains the expert
knowledge of a specific domain and can solve any complex problem of that particular
domain. These systems are designed for a specific domain, such as medicine,
science, etc.
The performance of an expert system is based on the expert's knowledge stored in its
knowledge base. The more knowledge stored in the KB, the more that system improves
its performance. One of the common examples of an ES is a suggestion of spelling
errors while typing in the Google search box.
Below is the block diagram that represents the working of an expert system:
Note: It is important to remember that an expert system is not used to replace the human
experts; instead, it is used to assist the human in making a complex decision. These
systems do not have human capabilities of thinking and work on the basis of the
knowledge base of the particular domain.
o High Performance: The expert system provides high performance for solving
any type of complex problem of a specific domain with high efficiency and
accuracy.
o Understandable: It responds in a way that can be easily understandable by the
user. It can take input in human language and provides the output in the same
way.
o Reliable: It is much reliable for generating an efficient and accurate output.
o Highly responsive: ES provides the result for any complex query within a very
short period of time.
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.
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.
Inference engine uses the below modes to derive the solutions:
o Forward Chaining: It starts from the known facts and rules, and applies the
inference rules to add their conclusion to the known facts.
o Backward Chaining: It is a backward reasoning method that starts from the
goal and works backward to prove the known facts.
3. Knowledge Base
Here, we will explain the working of an expert system by taking an example of MYCIN
ES. Below are some steps to build an MYCIN:
o Firstly, ES should be fed with expert knowledge. In the case of MYCIN, human
experts specialized in the medical field of bacterial infection, provide
information about the causes, symptoms, and other knowledge in that domain.
o The KB of the MYCIN is updated successfully. In order to test it, the doctor
provides a new problem to it. The problem is to identify the presence of the
bacteria by inputting the details of a patient, including the symptoms, current
condition, and medical history.
o The ES will need a questionnaire to be filled by the patient to know the general
information about the patient, such as gender, age, etc.
o Now the system has collected all the information, so it will find the solution for
the problem by applying if-then rules using the inference engine and using the
facts stored within the KB.
o In the end, it will provide a response to the patient by using the user interface.
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.