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ESSENTIALS OF EXPERT SYSTEM AND ITS APPLICATIONS
Dr. Ashwini Kodipalli
Assistant Professor, Dept. of I.S.E, New Horizon College of Engineering, Bangalore, Karnataka, India
Abstract: The principal objective of this paper is to understand the basic essential components of the
expert system and to understand the working of the expert system. The system presented in this paper
consists of rule-base providing the required knowledge, inference engine performs the reasoning, a
knowledge-based compiler that optimizes the reasoning process. Expert systems were one of the first
applications to emerge from initial research in artificial intelligence. This paper is structured as follows.
In section 2, it explains the introduction to AI and importance of expert system. In section 3, the
essential components needed to build an expert system are explained. In section 3, the basic architecture
and functionality of expert system is covered. In section 4, Advantages and disadvantages of using
expert system is mentioned.
Keywords: Artificial Intelligence, Expert System, Expert system applications, Expert system
components.
I. INTRODUCTION
The Artificial Intelligence is concerned with the development of intelligent system. Here system
imitates the human capabilities of thinking and sensing. AI has systems the replicates the human
decision making capabilities for well-defined problems.
In robotics the controlling software uses AI. The devices in robotics perform a task that requires
high degree of precision to humans. Applications of robotics are many in the areas of medical surgeries,
agriculture, mechanical industries and etc. Interaction of machines with humans is the most critical
aspects of AI. Natural language processing has been developed for this. The statements and commands
made in the human language such as English, natural language processing allows the computer to
understand and react. The major component of an artificial intelligence system is learning system. This
system uses the combination of hardware and software to allow the device to change how it function or
reacts based on the feedback. Many computer based games has learning system built-in. Due to this
element, suppose if the computer does not win, it remembers not to make the same mistake twice.
Neural network,an growing aspect of AI system. In neural network, the device tries to simulate some of
the functionality of human brain.
One of the major research fields in the area of artificial intelligence is expert system. Expert
system is introduced by the Edward Feigenbaum, prominent researcher of computer science department,
university of Stanford. In 1950, the AI field evolved into a machine which performs intelligently if an
interrogate using remote terminals cannot distinguish its responses from those of humans which is
tuning test. Thus resulting in general problem solving method. In 1960, AI is considered to be well
research field, thus resulting into knowledge-based expert systems.
Expert systems are mainly used t solve major complex problems in a specific domain. Domain is
an area within which the task is being performed. The major capabilities of an expert system are its
performance is high as it performs at a level of human experts. The system is highly responsive in the
reasonable amount of time and reliable from not being crash.
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II. COMPONENT OF AN EXPERT SYSTEM
In order to design an expert system, the following are the primary components needed as shown
in the figure 1:
1. Knowledge base.
2. Inference Engine.
3. User interface.
Knowledge refers to data, information and experiences from the past. Data is the collection of
facts which is collected from the expert engineer. Information is data and facts about the task domain.
Therefore knowledge base consists of high quality and domain specific knowledge. This component is
very much required to exhibit intelligence. More accurate and highly precise knowledge is the success of
any expert system.
Figure 1: Components of expert system
The knowledge-based expert system plays a key role in the functioning of expert system. The
knowledge base is used to store both factual and heuristic knowledge.
1. Factual Knowledge: It is the information widely accepted by the knowledge engineers and
scholars in the task domain. The factual knowledge comes from human expert, textbooks,
magazines and internet.
2. Heuristic knowledge: This knowledge refers to art of good guessing. It is the knowledge of good
practice, good judgment and plausible reasoning in the field. The
The knowledge base is formed by the various experts, scholars and knowledge engineers. These
three plays a fundamental role in building the expert systems.
1. Expert: Many experts are required in building the large expert system. The success of any expert
system depends on the how person’s experience and the knowledge of the application can bring
to it during its development.
2. Knowledge engineer: Knowledge engineer plays a dual task. This person should be able to draw
knowledge from the expert, gradually understand the area of expertise. The qualities some of the
knowledge engineer possesses is intelligence, tact, empathy and proficiency. Knowledge
engineer uses a knowledge acquisition technique which includes conducting interviews with
varying degrees of structures, observation of the experts while doing work, analysis of cases.
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3. User: Larger expert systems are built in an organized development effort. A prototype-oriented
interactive development strategy is commonly used. He end user is one who uses the expert
system.
Inference Engine: This acquires and manipulates the knowledge from the knowledge base to
arrive at a particular solution. Inference engine combines the facts of a specific care with a
knowledge contained in the knowledge base to come up with a recommendation. In a rule-based
expert system, inference engine controls the order in which production rules are applied and resolves
conflicts if more than one rule is applicable at a given time. Inference engine query the user for
information needed in order to process.
All the facts about a particular case are stored in the working memory. Inference engine
repeatedly applies the rules to the working memory, adding new information to it until goal state is
produced. To reach the conclusion, one of the strategies is applied by the inference engine.
1. Forward chaining.
2. Backward chaining.
Forward chaining: This strategy is used to answer the question “what can happen next”? The
process moves from the facts of the case to a goal (conclusion).
The inference engine matches the condition (IF) part of each rule in the knowledge base with the
facts currently available in the working memory. If several rule matches, conflict resolution procedure is
invoked. Inference engine follows the chain of conditions and derivations and finally deduces the
outcome. It considers all the facts and rules and sorts them before concluding to a solution. Forward
chaining systems are commonly used to solve more open-ended problems.
Backward chaining: This strategy is used to answer the question “why this happening”? On the
basis of what has already happened, the inference engine tries to find out which conditions could have
happened in the past for this result. This strategy is used to find out the causes or reasons.
User Interface: This provides interaction between user of the expert system and the expert system itself.
It explains how the expert system has arrived at a particular recommendation.
III. EXPERT SYSTEM ARCHITECTURE
The user interacts with the system through a user interface using menus, natural language or any
other style of interaction).Inference engine is then used to reason with both the expert knowledge and
data specific to the particular problem being solved. The expert knowledge will be in the form of a set of
IF-THEN rules. The case specific data includes both data provided by the user and partial conclusions
based on this data. In a simple forward chaining rule-based system the case specific data will be the
elements in working memory as shown in figure 2.
Almost all expert systems also have an explanation subsystem, which allows the program to
explain its reasoning to the user. Some systems also have a knowledge base editor which helps the
expert or knowledge engineer to easily update and check the knowledge base.
Figure 2: Expert system architecture
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One important feature of expert systems is the way they (usually) separate domain specific
knowledge from more general purpose reasoning and representation techniques. The general purpose bit
(in the dotted box in the figure) is referred to as an expert system shell. As we see in the figure, the shell
will provide the inference engine (and knowledge representation scheme), a user interface, an
explanation system and sometimes a knowledge base editor. Given a new kind of problem to solve (say,
car design), we can usually find a shell that provides the right sort of support for that problem, so all we
need to do is provide the expert knowledge. There are numerous commercial expert system shells, each
one appropriate for a slightly different range of problems. Using shells to write expert systems generally
greatly reduces the cost and time of development.
IV. APPLICATIONS OF AN EXPERT SYSTEM
Expert system has its wide scope across many domains such as medical, monitoring, process
control, knowledge, finance and commerce and etc.. In the medical domain expert system is used as
diagnosis systems to deduce cause of disease from observed data, conduction medical operations on
humans. In monitoring systems, expert system is used for comparing data continuously with observed
system or with prescribed behavior such as leakage monitoring in long petroleum pipeline. In process
control systems, expert system controls a physical process based on monitoring. In the knowledge
domain, expert system helps in finding out faults in vehicles, computers. In Finance and Commerce,
expert system detects the possible fraud, suspicious transactions, stock market trading, Airline
scheduling, cargo scheduling.
V. ADVANTAGES OF EXPERT SYSTEM
1. Expert system performs the task much faster than the human beings.
2. The error rate of successful completion of the task is much lower than the human error rate for
the same task.
3. Expert systems are available easily due to mass production software.
4. The cost of providing expertise is not expensive.
5. They can be used in any risky environments where humans cannot work with. Thus reduces the
danger to the human life.
6. The knowledge will last long indefinitely.
7. They are capable of explaining in detail the reasoning that led to a conclusion.
8. They can respond at great speed due to the inherent advantages of computers over humans.
9. Unlike humans, they do not get tense, fatigue or panic and work steadily during emergency
situations.
VI. DISADVANTAGES OF EXPERT SYSTEM
A problem with knowledge acquisition and maintenance is difficult.
VII. CONCLUSION
An expert system is a computer program that uses knowledge and inference procedures to solve
that are ordinarily solved through human expertise. A knowledge engineer obtains the necessary
knowledge from a human expert and puts it into a knowledge base. This knowledge together with the
inference procedures used can be thought of as a model of an expert in a specialized field or domain of
interest.
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