1. Running Head: ARTIFICIAL INTELLIGENCE AND EXPERT
SYSTEMS
Artificial Intelligence and Expert System
Artificial Intelligence and Expert systems
ABSTRACT:
· What is artificial intelligence?
· Characteristics of AI
· The field of AI
· How the AI field evolved
· What is Expert System?
KEYWORDS:
· Artificial intelligence Technology
· International Technology Transfers
· Expert Systems
· Applied Artificial Intelligence Human Experts
INTRODUCTION:
· AI to Predict and Adapt
· Making Decisions
· Continuous Learning
· Capability of Motion and Perception
OVERVIEW OF APPLIED ARTIFICIAL INTELLIGENCE:
· Natural language processing
· Robotics
· Computer vision
· Speech Recognition
· Machine learning

2. EXPERIMENT:
APPLICATIONS OF EXPERT SYSTEMS:
· Troubleshooting and Diagnosis
· Financial decision making
· Knowledge Publishing
· Design and Manufacturing
EXPERT SYSTEMS IN ORGANISATION:
BENEFITS AND LIMITATIONS OF AI ALONG WITH
EXPERT SYSTEMS:
LIMITATIONS OF EXPERT SYSTEMS:
NEURAL NETWORKING:
· Artificial neural networking
· Training Data

3. Artificial Intelligence and Expert Systems
Sapana Dahal
CSCI 303
Texas A& M University Commerce
Artificial Intelligence (AI) in the current technological crazed
world has been on the center stage. AI has commendable gained
notable popularity and visibility that is very string in the public
domain, business community, Educations and various other
special fields. Now the most successful business in the world
have not hesitated to fully take advantage of AI to better their
products, through presented AI to the consumer in a cheaper
state. This is the example Amazon Alexa AI, Apples Siri AI and
Microsoft’s Cortana (Kaplan & Haenlein 2019). These are
aperfect example of the use of AI to make things easier for the
consumer, all these considered to be “personal assistants.

4. Expert Systems (ES) on the other hand are a dominant filed in
AI, basically the largest field in AI currently, this is because it
offers scientific, commercial and military application of AI.
This paper is aimed at looking at and explaining the AI concepts
and ES applications that have been able to make the life of
every individual in different field easier. Like ROSS the Ai
attorney, or the Dendral expert system in medicine. The
Implementation of AI technology cannot be ignored in our daily
lives cannot be ignored because of the impact that such a
technology is bringing to our world. Never have machines been
able to mimic the human brain and be able to tackles decision
making or problem-solving instance as better or just as the
human brain. These aspects have caused a major quagmire of
mixed feelings. Mainly because the AI and ESs will make our
lives easier bhut at the same time it will step in the place of
human experts that will mean they will be replaced. The paper
clearly discusses on this fact on how the ESs and Ai are not
here to replace the Human experts, especially the white-collar
jobs but only to make their task much easier.
KEYWORDS
Artificial Intelligence Technology, International Technology
Transfers, Experts Systems, Applied Artificial Intelligence
Human Experts.
INTRODUCTION
Artificial Intelligence (AI) can be described as the creation or

5. the simulation of human intelligence into machines with the aim
of mimicking the actions and thinking like human beings. The
term is broad as it can eb associated with any machine that is
able to display various human thinking traits like problem-
solving and learning. In the current world or the world to come
artificial intelligence will be the most used or preferred
technology as it will make the work easier or rather replace
human beings in tasks that are hard to be completed by human
beings the ideal characteristic of artificial intelligence is taking
actions and at the same rationalization with the aim of
completing a specific goal (Upadhyay & Khandelwal, 2019).
The most perceived idea about artificial intelligence is robots,
the immediate thing that comes to mind when someone mentions
the term artificial intelligence. This is through the novel and the
movies that weave stories depicting AI machines that bring
havoc to the world. But it might not be far from the truth, the
most important aspect to understand is that AI is continuously
developing into new and better versions on a daily basis; with
machines build withing various fields like; psychology,
linguistics, mathematics, computer science and more with the
aim of solving issues in these fields better than human beings.
There are many merits of AI, it is because as a simulation of the
human thinking it is developing and growing every day. These
merits clearly represent the AI and the include: AI can adapt
and predict. The use of algorithms has enabled AI to predict and
adapt, whereby patterns of past data are used for future
prediction and decision making. These patterns give AI the
ability to learn like human beings and put through software
systems that can be used to correct errors or predicting what is
going to be typed and estimation shortest routes to take
(Kietzmann & Pitt, 2019). These are some of the abilities under
this characteristic. AI simulates human intelligence, and
therefore every aspect the human intelligence has is mimicked
like for instance the ability to analyze data, make decisions and
gain new insights to make the best decisions, faster than human
counterparts. Which makes it preferred to human labor by most

6. companies. AI is also known as machine learning, meaning it
can continually learn through of the most notable method know
as deep learning used by Alexa, Siri, Netflix and Google.
Whereby analytical models are built to perform tasks through
countless trial and error. AI is reactive meaning that atypical AI
will perceive a problem and develop a better solution for it; like
the application on our gadgets predict on our actions and
provide recommendations that suit the user. Finally, the rother
major characteristic is the capability of motion and perception
since the development of AI in the last 50 years ago, AI has
evolved into this phenomenon that is able to have auditory,
visual, speech perception and information learning and
processing that is perceptual. Whereby the goof examples
include: Amazon Alexa, Apple’s Siri and the Tesla’s Self
driving Cars.
The first practical application of AI was developed by the
British Mathematician Alan Turing, who was responsible for
conceptualizing machines that had the ability to think in the
1950s. at this point the seeds of AI were set and it picked up
from this moments, whereby followed by an AI lab that was
setup in Massachusetts in the year 1959 by Marvin Minsky
(Garnham 2017). Through this lab their collaboration with
Stanley Kubrick who received radical ideas from Marvin who
developed various literature and movies relating to AI the AI
excitement was further catapulted by the Invention of the
computer in the 1980s (Garnham 2017). whereby this opened
way for the tech corporations who have explored various fields
of AI that are used to bring technology close to the consumer
through affordability and through the various applications.
Today AI is every way, in smartphones, traffic routes, Netflix,
Amazon and Apples and the other various corporations or fields
embracing AI. AI has transformed and is continuing to
transform how things work in the world, through the basics of
machine learning that are instrumental in various instances:
such as advertisement that are based on machine learning
focusing on the data inputs that are received from different

7. channels. But this can be considered as the beginning of AI take
over, the future is envisioned as an economy and industry that is
robot driven. A lot of companies are looking into self-driven
cars, which basically will change the basic characteristics of
private and public transport. Additionally, AI has played an
important part in the management and security sector, through
the analysis of anomalous data patterns. A good example is the
chatbot which is an AI application that is increasingly used by
companies replacing regular customer service agents, which are
not as effective as human. Although creates an all-round
customer engagement through machine learning. Whereby a
good example is the trials of AI genoming, proving that humans
and machines will come to works side by side, and it will be
considered normal as it is growing form the various efforts put
in place at the moment. The future of AI is very clear, looking
at how it has evolved to this moment; although there are various
challenges in the widespread adoption, the world has
overlooked over the advantages that AI brings to the world and
making work much easier, but also ensuring that AI is managed
effectively. There is an increased risk which will affect the
development of AI as it majorly depends on Data, and it will be
challenging as it will cause social unrest and ensure human jobs
are redundant. Whereby it is the responsibility of people
pushing AI to makes sure there is a good balance between the
positives and the negatives of AI which will not destroy but
bring a Positive AI revolution in the Future (Pauschunder
2019).
Experts systems can be described as programs that rely on AI,
in other words these two are tied. There is no way to discuss AI
and without mentioning Expert systems, and vice versa. Experts
systems can use AI to simulate behavior and judgement of an
organization or human that has expert knowledge of a field.
Basically, an expert system brings together an interface of rules
known as engine and accumulated experience known as
knowledge base (Andikos et al. 2016). Whereby the rules engine
applies knowledge based to each situation that is spelled out in

8. the program. The capabilities of the system can be improved
with various addition to the set of rules or the knowledge base.
Current expert systems are typically equipped with capabilities
of machine learning which makes it easy for them to perform
better as humans through learning from experience. Developed
in the 1970s the expert systems have been significant in many
industries like telecommunications, financial, customer
services, healthcare, video games, transportation,
manufacturing, written communication and aviation. A good
example of an expert system was the groundbreaking medical
system which helped in the diagnosis. Deandra Expert System
assisted chemists to understand organic molecules that led to
the easy identification of various bacteria such as the meningitis
and bacteremia and recommend the nest dosage and antibiotics
(Tan 2017).
The expert systems therefore are useful in the current
technological world in the sense that it can eb used to resolve a
lot of issues, that would generally require human expertise.
Interestingly enough AI and expert systems can eb sued to
reason and expression in different domains of knowledge.
Basically, it is safe to say that expert systems are the
predecessors of the modern-day AI, machine learning and deep
learning systems. There are different examples of Expert
systems: MYCIN, tis was developed earlier in the medical field
and it was designed around backward chaining, that gave it the
ability to identify various bacteria that could cause infections
and at the same time recommend drugs to patients. Similar
systems are DENDRAL in the chemical engineering field,
PXDES in the medical systems specifically for lung cancer and
CaDet, in the medical filed for cancer identification. These
systems are in every filed even the legal system, which will
revolutionize labor in the world as it was previously identified.
APPLICATIONS
Overview of Applied Artificial Intelligence
AI is rapidly growing and soon enough it will be covering

9. almost all areas of the world, whereby it is quickly moving from
the lad to the consumer and business applications (Bravo et al.
2016). The result creates a significant on how software is built
now as compared to previous versions. Now applied AI is one of
the powerful technologies powering the most successful
business currently, Apple, Google, Facebook and Amazon.
There are a few areas where AI has been applied which are:
Natural Language Processing (NLP) a branch of AI that mainly
deals with interactions between humans and computers using the
natural language, with the aim of reading, deciphering,
understanding and making sense of human language in a way
that is valuable. Robotics, AI in this field helps in saving
certain motions in the systems of robotics, these motions are
constantly refined that makes moving and installing robotic
systems easy to achieve; the result is robots being used in the
customer service sector globally. Machine Learning, as
mentioned before it is the way in machines learn new concepts
on its own without explicit programming, which is an applied
AI that enables automatic improvement and learning form
experience. These are some of the Application of AI in addition
to more others like Speech recognition and computer vision
(Hengstler et al. 2016).
Capabilities of Expert Systems
In AI, expert systems emulate the decision-making ability of
humans to solve various issues which is just a simple
description. Experts systems are built to have knowledge to one
domain like, engineering, science and medicine. This knowledge
as mentioned before is known as a knowledge base and has
gathered experiences that are tested after being loaded in the
system. The rules and adds-on to the knowledge base maybe add
to give the expert systems to create a better system. The system
is described as reliable, highly responsive, understandable and
high performance. Expert system typically will come into the
world to replace the individuals in the white collar and
analytical jobs. Because expert systems are excellent in pattern
matching, configuration, classification, reasoning, planning and

10. diagnosis (Bogdanova et al. 2016).
The Inference engine is the basis for the capabilities of ESs. To
have a solution the Inference engine manipulates the knowledge
that as acquired.
For instance; when an Expert system is rule-based in AI, there
is the need to add knowledge if require, the rules obtained from
earlier application need to be applied to the facts and this will
resolve rules conflict, especially when multiple rules are
presented in an instance. The following strategies are used to
recommend a solution, the forward chaining and the backwards
chaining.
Forward chaining: this is a strategy used by an Experts System.
This strategy helps to answer the question what happens next.
Basically, the chain of derivations and conditions are followed
in this strategy and at the same time reduce the outcome. All the
rules and facts are considered and are sorted before the
conclusion is made to recommend a solution. A good example is
predicting the share market status, based on the change in
interest rates.
Table 1. Prediction of share market status as an effect of
changes in interest rates.
Image Source: Data Flair Team (Data Flair, 2018)
Backward Chaining: This strategy is sued by the Expert System
to answer the question “why this happened?” normally in this
case what had already happened will be very significant matter
to the organization or the individual. Therefore, this strategy
will focus on finding out the conditions that led to the particular
outcome in place in the pat to have this result. Therefore, is a
strategy that finds reason or cause. For example, human blood
cancer.
Table 2. Diagnosis of blood cancer in humans.

11. Image source: Data Flair Team (Data Flair, 2018)
Applications of Expert Systems
There are various application of Expert Systems, but the most
important things to consider when choosing and Expert System
ES is that the tool that has been chosen needs to match the
qualification the project team possess, and the tool selected for
the projected has to go hand in hand with the sophistication and
capability of the projected ES. The first application is the
troubleshooting and diagnosis of Devices of all kinds, basically
discovering faults and suggest remedies for these faults for a
device or process that is malfunctioning. A good example is
medical diagnosis the first ever application of ES. Planning and
Scheduling, systems in this case analyses interacting and
potentially complex goals and determine set of actions that can
be sued to achieve the same goals. At the same temporal
ordering of the actions can be initiated with the ES considering
material, account personnel and various constraints. Financial
Decisions Making, the financial service industry has a common
use of ES techniques some programs have been created for the
sole reason of advising bankers to whether give loans to
individuals and businesses (Wagner 2017). Knowledge
Publishing, this is a new application that has not been there for
some years but potentially an area that is rapidly growing. The
main function of the ES in this case is deliver relevant
knowledge to the problems the user presented. A good example
of ES in this sector is the advisor that guides the user on the use
of grammar in a text and an ES that guides the user on
individual tax policy and tax strategy, whereby the ES is known
as the tax advisor. Process Monitoring and Control, ES in this
application analyze real-time data obtained from physical
devices with the objective of predicting trends, establishing
anomalies, controlling failure correction and optimality. Design
and Manufacturing, Expert Systems in falling field assist in the
design of physical processes and devices. Which can be factory
floor configuration of processes in manufacturing or can be
level conceptual design of entities considered to be abstract.

12. Development of AI and Expert Systems
Experts Systems ES have been identified to be systems that
utilize AI to provide decisions making and problem techniques
that as close as to human intelligence working. Which means
that ES really needs Ai to function but at the same time it is an
independent entity on its own. As mentioned before expert
systems are hand in hand, AI as one grows the other follows the
pattern. As AI is taking over the world, ES follows the same
path are they are being deployed and developed in a various part
of the world in applications considered to be myriad. This is
because of the explanation capabilities and symbolic reasoning
of the ES. A good example is the recently developed Expert
System known as ROSS, which is an attorney AI that is built
under the principles of pattern recognition, deep learning, self-
learning systems and natural language processing that utilizes
data mining and at the same copy the working of the human
brain (Semmler & Rose, 2017). Whereby this qualifies to be an
exciting development yet controversial. It may not be in every
enterprise, but the development of AI and Experts Systems has
taken over the world.
EXPERT SYSTEMS (ESs) IN ORGANIZATION
ES provide both important and tangible value to various
organizations. As mentioned before, the most successful
business in the world have benefitted from the use AI and
Experts systems majorly through introducing them to their
consumers. The benefits should be measured against the
exploitation and development costs of an ES, that are basically
very expensive especially for ESs that are considered to
organizationally important and large (Masuch 2018). At this
moment it seems that Experts systems are the substitution for an
individual’s work but that is not the case as the ES can
substitute overall performance for a knowledge worker or the
white-collar jobs, mainly in analytics and problem-solving
tasks. But this is not true, yes, the systems can significantly
reduce the individual’s work dramatically required for the
knowledge worker to solve the problem but leave the innovative

13. and creative aspect of problem solving to the people. Basically,
ESs are tools that are developed to make work easier. The issue
is that it will reduce the work that two people would have
performed to be performed by one person, which is good for the
company, but it will create societal unrest of unemployment.
Therefore, looking at the benefits and limitation will help in
establishing where the ESs stand in organizations.
Benefits of AI along with Expert Systems
· The benefits of ESs are straight forward and are used to
reduce the tine taken, workload or resources used to complete a
task. This is only possible when AI is incorporated to the ESs.
The benefits include:
· Es are designed to be able to complete task presented to them
faster than how long a human expert would take in the same
task.
· The systems that are successful presents a low error rate,
whereby at times the error rate is much lower than when a
human expert is performing the same task.
· At the same when the ESs are given task, or problems to solve
they will come up with various recommendations, which is only
possible to a human expert who has experience in the field.
· At some point there is the need for difficult-to-use sources of
knowledge in an application, ESs are used and turn out to be
convenient to make sure bring9ng the application to this point.
· ESs are instrumental because through machine they can eb
able to capture scarce expertise, maybe there is only a few
experts in a field, let’s say finance who are the only ones who
can perform the specific task. Through Experience the uniquely
qualified expert can be accurately mimicked by ESs.
· Currently ES are known used differently, basically
organizations know use ESs to build up organizational
knowledge, as compared to the organization’s knowledge of
individuals.
· For novices, wen training vehicles are used ESs produce
higher and faster learning curves.

14. · Finally, a point that will change the current narrative that
taints ESs existence is that, organizations can utilize AI and
ESs in environments that are hazardous for regular Human
Experts. A good example can be the exploration of Mars; the
planet is not safe for human habitation but AI and ESs can be
used to come up a better environment that will pave way for
human experts.
Limitation of Expert Systems
Given the various advantages or benefits that the ESs present,
the human expert remains the most intelligent and efficient
system on the planet. This is because there is no total and easy
solution that technology can offer. ESs are large, take a lot of
time to develop, need a lot of computer resources and overall,
very costly. This makes ESs have their own limitations which
are:
· Technological limitations, such as the dependence on data.
· There are also issues with the acquisition of knowledge.
· The principle area of ES application is operational domains.
· Affect the maintenance of Human Expertise in an
organization, whereby it is important as ESs are not 100%
reliable at some point they will need upgrading or break down.
· Expert systems also do not have common sense to apply when
making a decision. Takin for an example; an expert system in
the Judiciary or any legal enterprise, in such a field most
decisions are based on common sense something that will not be
possible.
· The expert systems will require upgrades just any other
computer systems, meaning it lacks the ability to adapt to
changing environments.
· The Expert systems cannot explain the reasoning and logic
behind a decision made.
NEURAL NETWORKING
Neural Networks are described as computer systems that are
modelled on the brain of a human being. These systems look
like interconnected mesh network of processing elements known

15. as neurons. The neural network has around 100 billion neuron
brain cells making them simpler that the brain (Shakya et al.
2018). However, just as the brain, these networks can process
various information pieces at the same time and learn how to
understand patterns and program to resolve problems
independently. A neural network “is an array of interconnected
processing elements, each of which can accept inputs, process
them, and produce a single output with the objective of
imitating the operation of the human brain.” Information or
knowledge that is given out in a neural network in form of
pattern connections that is within the elements of processing
and through the adjustment of the weights of the same
connections. Therefore, the neural networks get strength from
applications that need sophisticated recognition of patterns. The
weakness of neural networks is that they do not sugar coat an
explanation to the conclusion or answers they give out.
But what has not yet been mentioned is that Deep Learning is
the name of this new approach that involves neural networks.
This approach in artificial intelligence has been there more than
70 years as they were first developed by Walter Pitts and
Warren McCullough in 1944 by these two University of Chicago
researcher who later transferred to MIT in 1952. These are the
founding members of neural networking or alternatively
identified as the first cognitive science department (Pospichal &
Kvasnicka, 2015). In both neuroscience and computer science
neural network was a major filed of research. Therefore, the
ability of google to have applications such as the speech
recognizers, google translator on smartphones have only
resulted from this technique known as the “deep learning,” that
will be further discussed below.
In other words, it is seen that artificial neural networks use
various layers of mathematical processing to make sense of
what information is needed to be fed. Furthermore, an artificial
neural network has millions of artificial neurons which are
known as units. These are arranged in a series of layers
(Mohapatra, Dandapat, & Thatoi, 2017). With the development

16. of the brain, various cognitive neuroscientists have learned
about the effective functioning of the human brain. It is seen
that the human brain is responsible for processing different
aspects of information and these aspects are arranged in an
order that shows hierarchy.
This means that input comes from the brain and each neuron
level provides and insight on the information which gets passed
on to the next complex level. This is the mechanism that
artificial neural systems try to develop and replicate. In
addition, it is seen that artificial neural systems need a huge
amount of data for them to learn. This information needed is
referred to as training data. In the event the training data has
reached a significant amount, it will then classify future
information based on what it sees through different units
(Prakashkumar, Murugan, Thiagarajan, Krishnaveni, & Babby,
2019). There are different uses of artificial neural networks.
These are, the network classified information, predict outcomes,
and clusters data. For example, it is seen that Google used a
neural network (30 layered) to power Google Photo. On the
other hand, Facebook also uses this aspect for DeepFace
algorithm. This is important in that it can give 97% accuracy.
Additionally, Skype’s ability to translate information in real
time is associated to the effectiveness of artificial neural
network.
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