UNIT-V

EXPERT SYSTEM AND APPLICATIONS

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INTRODUCTION PHASES IN BUILDING EXPERT
SYSTEMS

 An expert system is a computer program that is designed

to solve complex problems and to provide decision-
making ability like a human expert. It performs this by
extracting knowledge from its knowledge base using the
reasoning and inference rules according to the user
queries.

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 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 complex 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.

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 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.

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CHARACTERISTICS OF EXPERT
SYSTEM

 High Performance: The expert system provides high

performance for solving any type of complex problem of
a specific domain with high efficiency and accuracy.
 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.
 Reliable: It is much reliable for generating an efficient
and accurate output.
 Highly responsive: ES provides the result for any
complex query within a very short period of time.
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COMPONENTS OF EXPERT SYSTEM

 An expert system mainly consists of three components:

 User Interface

 Inference Engine

 Knowledge Base

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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.

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2. INFERENCE ENGINE(RULES OF
ENGINE)

 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.
 With the help of an inference engine, the system extracts
the knowledge from the knowledge base.

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THERE ARE TWO TYPES OF INFERENCE ENGINE:

 Deterministic Inference engine: The conclusions drawn

from this type of inference engine are assumed to be
true. It is based on facts and rules.
 Probabilistic Inference engine: This type of inference
engine contains uncertainty in conclusions, and based on
the probability.

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INFERENCE ENGINE USES THE BELOW MODES TO
DERIVE THE SOLUTIONS:

 Forward Chaining: It starts from the known facts and

rules, and applies the inference rules to add their
conclusion to the known facts.
 Backward Chaining: It is a backward reasoning method
that starts from the goal and works backward to prove
the known facts.

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3. KNOWLEDGE BASE

 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.
 It is similar to a database that contains information and
rules of a particular domain or subject.
 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.
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EXPERT SYSTEM VERSUS
TRADITIONAL SYSTEMS
 AI manages more comprehensive issues of automating a
system. This computerization should be possible by
utilizing any field such as image processing, cognitive
science, neural systems, machine learning etc. AI
manages the making of machines, frameworks and
different gadgets savvy by enabling them to think and do
errands as all people generally do.

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 An expert system is an AI software that uses knowledge
stored in a knowledge base to solve problems that would
usually require a human expert thus preserving a human
expert’s knowledge in its knowledge base. They can
advise users as well as provide explanations to them
about how they reached a particular conclusion or
advice.

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RULE BASED EXPERT SYSTEMS

 A rule-based expert system is the simplest form of

artificial intelligence and uses prescribed knowledge-based
rules to solve a problem The aim of the expert system is to
take knowledge from a human expert and convert this into
a number of hardcoded rules to apply to the input data. In
their most basic form, the rules are commonly conditional
statements (if a, then do x, else if b, then do y). These
systems should be applied to smaller problems, as the more
complex a system is, the more rules that are required to
describe it, and thus increased difficulty to model for all
possible outcomes.
 Note: with problems related to radiological images, often
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preprocessing of the images is required prior to the expert
system being applied
 BLACKBOARD SYSTEMS TRUTH MAINTENANCE
SYSTEMS
 A blackboard-system application consists of three major
components
 1.The software specialist modules, which are called knowledge
sources (KSs). Like the human experts at a blackboard, each
knowledge source provides specific expertise needed by the
application.
 2.The blackboard, a shared repository of problems, partial
solutions, suggestions, and contributed information. The blackboard
can be thought of as a dynamic "library" of contributions to the
current problem that have been recently "published" by other
knowledge sources.
 3.The control shell, which controls the flow of problem-solving
activity in the system. Just as the eager human specialists need a
moderator to prevent them from trampling each other in a mad dash 16
to grab the chalk, KSs need a mechanism to organize their use in the
most effective and coherent fashion. In a blackboard system, this is
 Famous examples of early academic blackboard systems are
the Hearsay II speech recognition system and
Douglas Hofstadter's Copycat and Numbo projects.
 More recent examples include deployed real-world
applications, such as the PLAN component of the Mission
Control System for RADARSAT-1,[10] an Earth observation
satellite developed by Canada to monitor environmental
changes and Earth's natural resources.
 GTXImage CAD software by GTX Corporation was developed
in the early 1990s using a set of rulebases and neural networks
as specialists operating on a blackboard system.
 Adobe Acrobat Capture (now discontinued) used a Blackboard
system to decompose and recognize image pages to understand
the objects, text, and fonts on the page. This function is
currently built into the retail version of Adobe Acrobat as
"OCR Text Recognition". Details of a similar OCR blackboard
for Farsi text are in the public domain.[11] 17

 Blackboard systems are used routinely in many military

C4ISTAR systems for detecting and tracking objects.
APPLICATION OF EXPERT SYSTEMS
 The following table shows where ES can be applied:

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BENEFITS OF EXPERT SYSTEMS

 Availability − They are easily available due to mass

production of software.
 Less Production Cost − Production cost is reasonable.
This makes them affordable.
 Speed − They offer great speed. They reduce the amount
of work an individual puts in.
 Less Error Rate − Error rate is low as compared to
human errors.
 Reducing Risk − They can work in the environment
dangerous to humans.
 Steady response − They work steadily without getting
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motional, tensed or fatigued.
EXPERT SYSTEMS LIMITATIONS

 No technology can offer easy and complete solution.

Large systems are costly, require significant
development time, and computer resources. ESs have
their limitations which include −
 Limitations of the technology

 Difficult knowledge acquisition

 ES are difficult to maintain

 High development costs

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LIST OF SHELLS AND TOOL

 The generic components of a shell : the knowledge

acquisition, the knowledge Base, the reasoning, the
explanation and the user interface are shown below. The
knowledge base and reasoning engine are the core
components.

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KNOWLEDGE BASE

 A store of factual and heuristic knowledge. Expert

system tool provides one or more knowledge
representation schemes for expressing knowledge about
the application domain. Some tools use both Frames
(objects) and IF-THEN rules. In PROLOG the
knowledge is represented as logical statements.

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REASONING ENGINE

 Inference mechanisms for manipulating the symbolic

information and knowledge in the knowledge base form
a line of reasoning in solving a problem. The inference
mechanism can range from simple modus ponens
backward chaining of IF-THEN rules to Case-Based
reasoning.

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KNOWLEDGE ACQUISITION
SUBSYSTEM

 A subsystem to help experts in build knowledge bases.

However, collecting knowledge, needed to solve
problems and build the knowledge base, is the biggest
bottleneck in building expert systems.

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USER INTERFACE

 A means of communication with the user. The user

interface is generally not a part of the expert system
technology. It was not given much attention in the past.
However, the user interface can make a critical
difference in the peeived utility of an Expert system.

**********THE END***********

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