ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS
Introduction to the Science of Artificial Intelligence:
Humankind has given itself the scientific name Homo sapiens--man the wise--because
our mental capacities are so important to our everyday lives and our sense of self. If thinking and
intelligence are merely a matter of processing symbolic information, then computers should be
capable of demonstrating such intelligence. One of the big questions of science is "What is
intelligence?" Intelligence can be defined as the collection, analysis, production, dissemination
and exploitation of information and knowledge.
Artificial Intelligence is a branch of Science which deals with helping machines to find
solutions to complex problems in a more human-like fashion. This generally involves borrowing
characteristics from human intelligence, and applying them as algorithms in a computer friendly
The Nature of Intelligence:
Some of the characteristics of intelligent behavior include the ability to do the following:
1 Learns from experience including the ability to learn by trial and error
2 Applies knowledge acquired from experience to another situation
3 Handles complex situations
4 Solves problems when important information is missing is the essence of decision
making when dealing with uncertainty
5 The ability to reason and think
6 Understand and interpret visual images including processing and manipulating symbols
7 Using heuristics, or rules of thumb, or even guesses in making decisions
Comparison of natural and artificial intelligence:
AI Advantages over Natural Intelligence are
1 More permanent
2 Ease of duplication and dissemination
3 Less expensive
4 Consistent and thorough
5 Can be documented
6 Can execute certain tasks much faster than a human can
7 Can perform certain tasks better than many or even most people
Natural Intelligence advantages over AI are
1 Natural intelligence is creative
2 People use sensory experience directly and can use a wide context of experience in
Categories of AI theories:
The four basic categories of AI theories are:
1 Systems that think like humans
2 Systems that act like humans
3 Systems that think rationally
4 Systems that act rationally
Thinking humanly: The cognitive modeling approach
If we are going to say that a given program thinks like a human, we must have some way
of determining how humans think. We need to get inside the actual workings of human minds.
There are two ways to do this: through introspection--trying to catch our own thoughts as they go
by--or through psychological experiments. The interdisciplinary field of cognitive science brings
together computer models from AI and experimental techniques from psychology to try to
construct precise and testable theories of the workings of the human mind.
Acting humanly: The Turing Test approach
The Turing Test, proposed by Alan Turing (Turing, 1950), was designed to provide a
satisfactory operational definition of intelligence. Turing defined intelligent behavior as the
ability to achieve human-level performance in all cognitive tasks, sufficient to fool an
interrogator. Roughly speaking, the test he proposed is that the computer should be interrogated
by a human via a teletype, and passes the test if the interrogator cannot tell if there is a computer
or a human at the other end. The computer would need to possess the following capabilities:
Natural language processing: To enable it to communicate successfully in English or any
Knowledge representation: To store information provided before or during the
Automated reasoning: To use the stored information to answer questions and to draw new
Machine learning: To adapt to new circumstances and to detect and extrapolate patterns.
Thinking rationally: The laws of thought approach
Reasoning should be apt. Given some correct fact we must be able to reason out
correctly, Aristotle was the first to codify “Right Thinking”. His famous syllogisms provided
patterns for argument structures that always gave correct conclusions given correct premises. For
example, ``Socrates is a man; all men are mortal; therefore Socrates is mortal.'' These laws of
thought were supposed to govern the operation of the mind, and initiated the field of logic.
Problems must be expressed in logical notation and then we need to find the solutions to the
problems. Programs that solve the problem using the concept of logic help us to create intelligent
systems. The problem in this approach lies in taking the informal knowledge and stating it in the
formal terms required by logical notation.
Acting rationally: The rational agent approach
Acting rationally means acting so as to achieve one's goals, given one's beliefs. An agent
is just something that perceives and acts. Concluding a correct inference is like being a part of
rational agent, because one way to act rationally is to conclude that a given action will achieve
one’s goal. There may be situations in which acting rationally may not involve inference.
Components of AI:
1 Expert Systems are computer programs that act or behave like a human expert in a field
2 Robotics involves developing mechanical or computer devices controlled by software to
perform tasks that require a high degree of precision or are tedious or hazardous for
3 Natural Language Processing allows the computer to understand and react to statements
and commands made in a "natural" language, such as English.
4 Learning Systems include hardware and software that allow the computer to change how
it functions or reacts to situations based on feedback it receives.
5 Neural Networks are computer systems that act like or simulate the functioning of the
Expert systems are computer programs that are derived from a branch of computer
science research called Artificial Intelligence (AI). Expert Systems are a computer program
intended to embody the knowledge and ability of an expert in a certain domain. The
performances in their specialized domains are often very impressive. Nevertheless, hardly any of
them have certain common sense knowledge and ability possessed by any non-feeble-minded
human. This lack makes them brittle. By this it is meant that they are difficult to extend beyond
the scope originally contemplated by their designers, and they usually don't recognize their own
limitations. Many important applications will require common sense abilities.
Expert Systems can explain their reasoning or suggested decisions, can display intelligent
behavior, can draw conclusions from complex relationships, and can provide portable
Capabilities of Expert Systems:
Expert systems also assure the following capabilities
1 Provide a high potential payoff or significantly reduced downside risk.
2 Capture and preserve irreplaceable human expertise.
3 Provide expertise needed at a number of locations at the same time or in a hostile
environment that is dangerous to human health.
4 Can develop a solution faster than human experts.
5 Provide expertise needed for training and development to share the wisdom of human
experts with a large number of people.
Components of Expert Systems:
Every expert system consists of two principal parts
1 Knowledge base
2 Reasoning and interference engine.
The knowledge base of expert systems contains both factual and heuristic knowledge.
Factual knowledge: It is that knowledge of the task domain that is widely shared,
typically found in textbooks or journals, and commonly agreed upon by those knowledgeable in
the particular field.
Heuristic knowledge: It is the less rigorous, more experiential, more judgmental
knowledge of performance. In contrast to factual knowledge, heuristic knowledge is rarely
discussed, and is largely individualistic. It is the knowledge of good practice, good judgment,
and plausible reasoning in the field. It is the knowledge that underlies the "art of good guessing."
Rule based systems: Rule is a conditional statement that links given conditions to actions
or outcomes. Knowledge representation formalizes and organizes the knowledge. One widely
used representation is the production rule, or simply rule. A rule consists of an IF part and a
THEN part (also called a condition and an action). The piece of knowledge represented by the
production rule is relevant to the line of reasoning being developed if the IF part of the rule is
satisfied. Consequently the THEN part can be concluded, or its problem-solving action taken.
Expert systems whose knowledge is represented in rule form are called rule-based systems.
Another widely used representation, called the unit (also known as frame, schema, or list
structure) is based upon a more passive view of knowledge. The unit is an assemblage of
associated symbolic knowledge about an entity to be represented. Typically, a unit consists of a
list of properties of the entity and associated values for those properties.
The problem-solving model, or paradigm, organizes and controls the steps taken to solve
the problem. One common but powerful paradigm involves chaining of IF-THEN rules to form a
line of reasoning.
Forward Chaining: If the chaining starts from a set of conditions and moves toward
some conclusion, the method is called forward chaining.
Backward Chaining: If the conclusion is known (for example, a goal to be achieved) but
the path to that conclusion is not known, then reasoning backwards is called for, and the method
is backward chaining.
These problem-solving methods are built into program modules called inference engines
or inference procedures that manipulate and use knowledge in the knowledge base to form a line
Though an expert system consists primarily of a knowledge base and an inference engine, a
couple of other features are worth mentioning: reasoning with uncertainty, and explanation of the
line of reasoning. Knowledge is almost always incomplete and uncertain. To deal with uncertain
knowledge, a rule may have associated with it a confidence factor or a weight.
Reasoning with Uncertainty: The set of methods for using uncertain knowledge in combination
with uncertain data in the reasoning process is called reasoning with uncertainty. An important
subclass of methods for reasoning with uncertainty is called "fuzzy logic" and the systems that
use them are known as "fuzzy systems".
The most important ingredient in any expert system is knowledge. The power of expert
systems resides in the specific, high-quality knowledge they contain about task domains.
Expert Systems Development:
There are basically 4 steps to building an expert system.
The spiral model is normally used to implement this approach. Expert system development can
be modeled as a spiral, where each circuit adds more capabilities to the system.
The purpose of analysis is to identify a potential application. Possible applications
include diagnostics, a controller, etc. During analysis the developer must also assess the
suitability of knowledge-engineering technology
for this application.
The specification step is where the developer defines what the expert system will do. The
expert is a human who is identified as being the domain expert in a particular field. The
developer must familiarize himself with the problem so that system development can be
performed. The experts may envision a different functionality for the system than the developer,
who better understands the limitations of the software.
The development step consists of several important tasks. Here, the developer must learn
how the expert performs the task (knowledge acquisition) in a variety of cases. There are
basically three kinds of cases the developer should discuss with the expert: current, historical,
and hypothetical. Current cases can be covered by watching the expert perform a task. Historical
cases can be discussed by discussing with the expert a task that was performed in the past. And,
hypothetical cases can be covered by having the expert describe how a task should be performed
in a hypothetical situation.
Next, a conceptual model of the expert system must be developed. This conceptual
model is a framework which consists of high-level descriptions of the tasks and situations. In this
framework, the developer must:
Decide how the inference, representation, and control structure can be used to implement.
Build the knowledge base.
Verify and validate.
In the deployment phase the developer installs the system for routine use. He also fixes bugs,
updates, and enhances the expert system.
Participants in Expert Systems:
The area of human intellectual endeavor to be captured in an expert system is called the
task domain. Task refers to some goal-oriented, problem-solving activity. Domain refers to the
area within which the task is being performed. Typical tasks are diagnosis, planning, scheduling,
configuration and design
Domain expert is the individual or group whose expertise and knowledge is captured for
use in an expert system. Knowledge user is the individual or group who uses and benefits from
an expert system is called a knowledge user. Knowledge Engineering is the development,
implementation, and maintenance of an expert system.
Advantages of Expert Systems:
Permanence - Expert systems do not forget, but human experts may.
Reproducibility - Many copies of an expert system can be made, but training new human
experts is time-consuming and expensive
Efficiency - can increase throughput and decrease personnel costs. Although expert
systems are expensive to build and maintain, they are inexpensive to operate.
Documentation - An expert system can provide permanent documentation of the decision
Completeness - An expert system can review all the transactions, a human expert can
only review a sample
Timeliness - Fraud and/or errors can be prevented. Information is available sooner for
So, where is the AI? It is in the size, in the ideas, and in the understanding of what is
significant that contributes to the behavior of intelligent beings. Some of the application of
Artificial Intelligence and Expert Systems are credit granting, information management and
retrieval, loan analysis, virus detection, marketing, warehouse optimization and in many other
It is hoped that this paper will incite the reader to probe deeper into this fascinating field,
which has helped us gain a deeper insight into ourselves.