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Artificial Intelligence: A Brief
Introduction and application
examples
Ling & Lam
2009

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Universiti Tunku Abdul Rahman
Table of Contents
Lists of Figures and Tables........................................................................................................ 2
Figures ................................................................................................................................... 2
Tables .................................................................................................................................... 2
Chapter 1 Introduction............................................................................................................... 3
Chapter 2 Applications of AI .................................................................................................... 4
2.1 Games .............................................................................................................................. 4
2.2 Expert System.................................................................................................................. 5
2.3 Intelligent Agent .............................................................................................................. 6
2.4 Simulations ...................................................................................................................... 7
2.5 Computer Vision ............................................................................................................. 8
2.6 Natural Language Processing ........................................................................................ 10
2.7 Machine Learning.......................................................................................................... 12
2.8 Interfaces ....................................................................................................................... 12
2.9 Robotics ......................................................................................................................... 13
2.10 Theorem Proving ......................................................................................................... 14
Chapter 3 Expert System ......................................................................................................... 16
3.1 Introduction ................................................................................................................... 16
3.2 Basic Concepts of Expert Systems ................................................................................ 17
3.4 How Expert Systems Work ........................................................................................... 18
3.5 Expert Systems in Medical Field ................................................................................... 21
3.6 Pros and Cons of Expert Systems .................................................................................. 22
3.7 Conclusion ..................................................................................................................... 22
Chapter 4 Conclusion .............................................................................................................. 24
Reference ................................................................................................................................. 24
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Lists of Figures and Tables
Figures
Figure 1 Deep Blue…………………………………………………….………………………5
Figure 2 & Figure 3 Flight Simulation and Auto Racing Simulation………………………….8
Figure 4 Video surveillance system software………………………………………………...10
Figure 5 A map of ALICE's "brain" plots all the words she knows….………………………11
Figure 6 & Figure 7 Android Aiko (Left) and Lisa (Right)…………………………………..14
Figure 8 Process of transferring expertise……………………………………………………18
Figure 9 Main components of expert systems and their interrelationship……………………20
Tables
Table 1 Types of expert systems……………………………………………………………...16
Table 2 Lists of well-known expert systems in medical field………………………………..21
Table 3 Application example in different categories of medical expert systems…………….21
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Chapter 1 Introduction
Artificial Intelligence (AI), a term that coined by John McCarthy in his 1955
proposal for the 1956 Dartmouth Conference, is the branch of computer science
concerned with making computers behave like humans, or defined by John McCarthy
himself as “the science and engineering of making intelligent machines, especially
intelligent computer programs.”
After Second World War, a number of people started to work on intelligent
machines independently, where it is believed that that was the time AI research
started. The English mathematician Alan Turing may have been the first where he
gave a lecture on it in 1947. He also may have been the first to decide that AI was best
researched by programming computers rather than by building machines. By the late
1950s, there were many researchers on AI, and most of them were basing their work
on programming computers. Although it has been around 60 years in AI research
where most of the researcher aimed to simulate full human behavior in various
applications, however, up until today, there is still no computers exhibit full artificial
intelligence. Nevertheless, research in AI never stopped where there are still a lot of
different research projects focusing on AI improvement. One of the noticeable
projects is the Artificial Intelligence System (AIS), which is a large scale distributed
computing project by Intelligence Realm, Inc. that involving over 10,000 computers
with the initial goal to recreate the largest brain simulation to date. The AIS project
has successfully simulated over 700 billion neurons as of April 28, 2009.
Although the failure for appearance of AI that fully mimics human behavior,
there are numerous of fields, such as natural resources management, medicine,
military, petroleum industry, just to mention a few, has been make full use of AI to
achieve different goals in a smarter way nowadays. Some of the AI applications have
been proven to give great practical benefits, and despite of the widely use of AI
techniques in software and hardware, the existence of AI in all those products mostly
go unnoticed by many people, which also known as the “AI Effect”. Thus, it is not
hard to observe that AI is so important that it has been a part of life in every
industrialized nation. In the next section, there will be some examples on the
application of AI in different fields or industries.
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Chapter 2 Applications of AI
2.1 Games
One of the most studied and most interesting areas of AI is the application of
AI in games and simulations. AI has been extensively used many type of games,
including board games like chess, checker; computer games like strategy games
and massively multiplayer online role-playing game (MMORPG). With the
integration of AI, a game will become livelier and more fun to the user as the AI
used is simulating the real environment or human behavior in the game.
AI applied in games somewhat complex if compared to those AI applied in
problem solving system which can perform precise and accurate decision, such as
expert system. AI for game is about the imitation of human behaviour where it has
to be
 Creative and Smart (to a certain extent),
 Non-repeating behaviour,
 Unpredictable but rational decisions,
 Emotional influences or personality,
 Body language to communicate emotions,
 Being integrated in the environment,
so that the user are able to experience the somewhat „human-like‟ response on any
action being taken in the game.
Early research of AI application in game playing (state space search) was done
using common board games such as checkers, chess, and the 15-puzzle. These
games are chosen to be the subject not only because they have limited and well
defined rules, but they also have huge state spaces due to the complexity of the
game which means that a perfect move is almost impossible in limited time. One
of the most successful examples of the game playing AI in the history is the Deep
Blue, a chess-playing computer that derived its playing strength mainly out of
brute force computing power, which is developed by IBM. On May 11, 1997, the
machine won a six-game match by two wins to one with three draws against world
champion Garry Kasparov.
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Figure 1 Deep Blue
2.2 Expert System
AI programs that achieve expert-level competence in solving problems in task
areas by bringing to bear a body of knowledge about specific tasks are called
expert systems. Often, the term expert systems is reserved for programs whose
knowledge base contains the knowledge used by human experts, in contrast to
knowledge gathered from textbooks or non-experts.
The primary goal of expert systems research is to make expertise available to
decision makers and technicians who need answers quickly. There is always not
enough expertise to go around solving problem at the right place and the right
time. Expert systems can assist supervisors and managers with situation
assessment and long-range planning. Many small systems now exist that bring a
narrow slice of in-depth knowledge to a specific problem, and these provide
evidence that the broader goal is achievable.
Expert systems have been widely used in a number of industries; one of the
examples that utilize it extensively is the aviation industry. The scheduling flights
based on economics, environmental, regulatory requirements and airway traffic
parameters are extremely complex. Any mistakes in any one of the steps involved
in a flight is extremely costly. Typically, expert systems that can be found in
aviation industry include:
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 The Aviation Expert System
This is an expert system that used to clarify psychological
assessment issues in the field of aviation.
 General Aviation Pilot Advisor and Training System (GAPATS)
GAPATS is a computerized airborne expert system that uses fuzzy
logic to infer the flight mode of an aircraft from:
o sensed flight parameters
o an embedded knowledge base, and
o pilot inputs
which the data will then used to assess the pilot‟s flying performance
and issue recommendations for pilot actions.
 Aircraft Maintenance Expert Systems (AMES)
AMES has been used since the early 1990's. Manual procedures
around aircraft maintenance are very strenuous and time consuming.
Diagnosis of aircraft malfunctions is an ideal candidate for an expert
system to assist in the diagnosis of aircraft problems.
 Anti-G Fighter Pilot System
The high maneuverability of modern jet fighters often subjects the
pilots to high Gz acceleration. One of the adverse effects of Gz
acceleration is loss of consciousness. The Anti-G Fighter Pilot System
presents an alternative to the current protection pressure mask and
pressurized G-suit. The system used expert knowledge and pilots'
anthropometric and physiologic data to generate control schedules of
the G-suit and mask pressures of jet fighter pilots.
2.3 Intelligent Agent
In AI, an intelligent agent (IA) is an autonomous entity which observes
and acts upon an environment and directs its activity towards achieving goals.
Intelligent agents may also learn or use knowledge to achieve their goals.
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They may be very simple or very complex; for example, a reflex machine such
as a thermostat is an intelligent agent.
Based on the intelligent agent‟s degree of perceived intelligence and
capability, Russell & Norvig categorize agents into five classes:
i. simple reflex agents
ii. model-based reflex agents
iii. goal-based agents
iv. utility-based agents
v. learning agents.
However, intelligent agents nowadays are normally gathered in a
hierarchical structure containing many “sub-agents” in order to perform more
advance tasks. Intelligent sub-agents process and perform lower level
functions. Taken together, the intelligent agent and sub-agents create a
complete system that able to accomplish difficult tasks or goals with behaviors
and responses that display a form of intelligence.
In the world of e-commerce, intelligent agents known as shopping bots are
used by consumers to search for product and pricing information on the Web.
Each shopping bot operates differently, depending on the business model used
by its operator. Famous internet search engine like Google, Yahoo!, and
Ask.com are also utilizing this technology to perform information searching
for the internet user.
2.4 Simulations
Besides the AI used in game playing with the purpose of having fun,
simulations nowadays has heavily written code for AI to simulate the most
realistic feedback and response for any action by the user; to let the user feels
that he is placed in that particular environment or situation. This is very
important for the industries that utilizing simulations, such as the aviation
industry and auto racing because any slight mistake in a flight or in a race is
very costly, even death. Thus, simulation is used to train the user familiar with
some events that rarely happen but able to respond swiftly if that particular
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event really happens. In a simulator, every input of the user will get responded
by the most accurate feedback to ensure that the user is able to experience it
before he/she go for the real thing, especially in auto racing industry.
The application of artificial intelligence in simulators is proving to be very
useful for the aviation industry. Airplane simulators are using artificial
intelligence in order to process the data taken from simulated flights. Other
than simulated flying, there is also simulated aircraft warfare. The computers
are able to come up with the best success scenarios in these situations. The
computers can also create strategies based on the placement, size, speed, and
strength of the forces and counter forces. Pilots may be given assistance in the
air during combat by computers. The artificial intelligent programs can sort
the information and provide the pilot with the best possible maneuvers, not to
mention getting rid of certain maneuvers that would be impossible for a
sentient being to perform. Multiple aircraft are needed to get good
approximations for some calculations so computer simulated pilots are used to
gather data. These computer simulated pilots are also used to train future air
traffic controllers.
Figure 2 & Figure 3 Flight Simulation and Auto Racing Simulation
2.5 Computer Vision
Computer vision is the branch of artificial intelligence that focuses on
providing computers with the functions typical of human vision. Computer
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vision has been applied in several fields such as industrial automation,
robotics, biomedicine, and satellite observation of Earth. In the field of
industrial automation alone, its applications include guidance for robots to
correctly pick up and place manufactured parts, nondestructive quality and
integrity inspection, and on-line measurements.
When the famous dedicated computer-vision system, the Massively
Parallel Processor (MPP) designed at the Goddard Space Flight Center in 1983,
it does not received good response due to the complexity and very high price.
However, with the advancement in manufacturing technology, the availability
of affordable hardware and software has opened the way for new, pervasive
applications of computer vision. Computer-vision systems have one factor in
common. They tend to be human-centered; that is, either humans are the
targets of the vision system or they wander about wearing small cameras, or
sometimes both. Nowadays, computer-vision systems have been used in quite
a number of applications, including:
o human-computer interfaces (HCIs), the links between computers and
their users
o augmented perception, tools that increase normal perception
capabilities of humans
o automatic media interpretation, which provides an understanding of the
content of modern digital media, such as videos and movies, without
the need for human intervention or annotation
o video surveillance and biometrics.
Although the success of the usage of computer vision in our daily life, to
date, computer vision systems still unable to emulate the full capabilities of
the human visual system. The human eye-brain combination still proved to be
the best which is able to categorize previously unseen objects with ease, using
background knowledge and context. Nevertheless, computer vision has helped
to solve a lot of problems and difficulties in human‟s life; hence it is
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foreseeable that the usage of computer vision will continue to grow to improve
humans‟ life.
Figure 4 Video surveillance system software
2.6 Natural Language Processing
Natural Language Processing (NLP) is both a modern computational
technology and a method of investigating and evaluating claims about human
(natural) language itself. NLP uses computers to process written and spoken
language for some practical, useful, purpose: to translate languages, to get
information from the database to answer an enquiry, to carry on conversations
with machines, so as to get advice about, say, pensions and so on. And these are
only examples of major types of NLP. There is also a huge range of lesser but
interesting applications, e.g. getting a computer to decide if one newspaper story
has been rewritten from another or not. Hence, NLP is not simply applications but
the core technical methods and theories that the major tasks above divide up into,
such as Machine Learning techniques, which is automating the construction and
adaptation of machine dictionaries, modeling human agents' beliefs and desires
etc. Artificial Intelligence is an essential component of NLP if the computers have
to engage in realistic conversations with human.
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One of the modern AI research in NLP is the research in chatterbot. A
chatterbot (or chatbot) is a type of conversational agent, a computer program
designed to simulate an intelligent conversation with one or more human users via
auditory or textual methods. Also known as Artificial Conversational Entity
(ACE) and, though many appear to be intelligently interpreting the human input
prior to providing a response, most of the chatterbots simply look for keywords
within the input and reply with the most matching keywords or the most similar
wording pattern from a local database. The classic and early famous chatterbots
are ELIZA (1966) and PARRY (1972). More recent programs are Racter, Verbots,
A.L.I.C.E., SmarterChild, and ELLA. With the growing number of research in
chatterbots, the initial purpose of creating chatterbots has been expanded to many
other usages, for example story „writing‟ by Racter.
Some of the organizations are already beginning to implement a so-called
Automated Conversational Systems which is a system that evolved from the
original designs of the first widely used chatbots. In the UK, large commercial
entities such as Lloyds TSB, Royal Bank of Scotland, Renault, Citroë and One
n
Railway are already utilizing Virtual Assistants to reduce expenditures on Call
Centres and provide a first point of contact that can inform the user exactly of
points of interest, provide support, capture data from the user and promote
products for sale.
Figure 5 A map of ALICE's "brain" plots all the words she knows.
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2.7 Machine Learning
Machine learning is a scientific discipline that is concerned with the design
and development of algorithms that allow computers to learn based on data, such
as from sensor data or databases. A major focus of machine learning research is to
automatically learn from past examples and to detect hard-to-discern patterns from
large, noisy or complex data sets.
Expert systems and data mining programs are the most common applications
for improving algorithms through the use of machine learning. Among the most
common approaches are the use of artificial neural networks (weighted decision
paths) and genetic algorithms (algorithms “bred” and culled to produce
successively fitter programs).
Due to the nature of machine learning, its‟ capability is particularly well-suited
to medical applications, especially those that depend on complex proteomic and
genomic measurements. As a result, machine learning is frequently used in cancer
diagnosis and detection. More recently machine learning has been applied to
cancer prognosis and prediction. This latter approach is particularly interesting as
it is part of a growing trend towards personalized, predictive medicine. Among the
better designed and validated studies it is clear that machine learning methods can
be used to substantially (15–25%) improve the accuracy of predicting cancer
susceptibility, recurrence and mortality. At a more fundamental level, it is also
evident that machine learning is also helping to improve basic understanding of
cancer development and progression.
A trend can be seen that machine learning has been used quite often in a lot of
different systems, be it a diagnostic system or education system, due to the long
term benefit. The success of machine learning programs suggests the existence of
a set of general learning principles that will allow the construction of programs
with the ability to learn in realistic domains.
2.8 Interfaces
Even the most sophisticated and powerful system will be next to useless
without an effective user interface. A good and user-friendly interfaces that
controls a complex machine is must-equipped software to allow the interaction
between the user and the machine. However, with the rapid advancement in
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technology, the demand in interfaces become higher and higher. Most of the users
already started to ask for a smarter interface to learn and remember their
preferences, instead of having the user input the same preference everytime and
thus, interface agents are started to get developed due to the demand.
Interface agents are computer programs that employ Artificial Intelligence
methods to provide active assistance to a user of a particular computer application.
The metaphor used is that of a personal assistant who is collaborating with the
user in the same work environment. The assistant becomes gradually more
effective as it learns the user's interests, habits and preferences.
One of the examples of commercialized interface agent has been used in
Microsoft Windows Vista called SuperFetch, a feature that predicts which
applications are used when, then pre-loads them so that they're instantly available.
„Microsoft Research contributed to the SuperFetch effort, a feature within Vista
that predicts which applications are used when, then pre-loads them so that they're
instantly available.‟ „As part of a long term set of projects, we want to teach the
computer to learn from users to make the machine more proactive,‟ says Eric
Horvitz, a principal researcher with Microsoft's R&D as well as the president-
elect of the American Association for Artificial Intelligence. „We want to use the
system's idle time to make things punchier.‟
Therefore, it is not surprising that the interface agent become part of the
important components, no matter in the hardware or in the software, due to the
high demand of human.
2.9 Robotics
Robots have become common in many industries. They are often given jobs
that are considered dangerous to humans. Robots have proven effective in jobs
that are very repetitive which may lead to mistakes or accidents due to a lapse in
concentration and other jobs which humans may find degrading. General Motors
uses around 16,000 robots for tasks such as painting, welding, and assembly.
Japan is the leader in using and producing robots in the world. In 1995, 700,000
robots were in use worldwide; over 500,000 of which were from Japan. Thus,
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from the number itself, it can be seen that it is very important to have AI in the
robots to keep the productivity at a constant level.
Besides the application of AI in heavy industries robots, AI also part of the
crucial components in human-like robots such as famous Android Aiko and
ASIMO. Both of the robots have the ability to recognize speech, voice, faces,
motion, objects and also learn simple thing with the AI implemented in the
software that operating them. Without AI, a robot will be doing a same thing
everytime as programmed.
The latest breakthrough in robotics AI happens when a relatively new
company called AI Robotics based in Kobe, Japan has developed a female robot
called „Lisa – The Perfect Woman‟ which equipped with RKS, “Recognition Krax
System”, which allows for vocal, tactile and visual recognition. Lisa is able to
recognize objects and persons and she can even differentiate between roses and
tulips for example. Claimed by the creators, Lisa can cook a meal based on what
is in the fridge using visual recognition. She also can go shopping, do household
work or give a hydraulic massage, and she can also play chess and video games
and even learn to do certain sports.
Figure 6 & Figure 7 Android Aiko (Left) and Lisa (Right)
2.10 Theorem Proving
Automated theorem proving is appealing due to the rigor and generality of
logic. Because it is a formal system, logic lends itself to automation. A wide
variety of problems can be attacked by representing the problem description and
relevant background information as logical axioms and treating problem instances
as theorems to be proved. This insight is the basis of work in automatic theorem
proving and mathematical reasoning systems.
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Unfortunately, early efforts at writing theorem provers failed to develop a
system that could consistently solve complicated problems. This was due to the
ability of any reasonably complex logical system to generate an infinite number of
provable theorems: without powerful techniques (heuristics) to guide their search,
automated theorem provers proved large numbers of irrelevant theorems before
stumbling onto the correct one. In response to this inefficiency, many argue that
purely formal, syntactic methods of guiding search are inherently incapable of
handling such a huge space and that the only alternative is to rely on the informal,
ad hoc strategies that humans seem to use in solving problems. This is the
approach underlying the development of expert systems (Chapter 8), and it has
proved to be a fruitful one.
Still, the appeal of reasoning based in formal mathematical logic is too strong
to ignore. Many important problems such as the design and verification of logic
circuits, verification of the correctness of computer programs, and control of
complex systems seem to respond to such an approach. In addition, the theorem-
proving community has enjoyed success in devising powerful solution heuristics
that rely solely on an evaluation of the syntactic form of a logical expression, and
as a result, reducing the complexity of the search space without resorting to the ad
hoc techniques used by most human problem solvers.
Another reason for the continued interest in automatic theorem provers is the
realization that such a system does not have to be capable of independently
solving extremely complex problems without human assistance. Many modern
theorem provers function as intelligent assistants, letting humans perform the
more demanding tasks of decomposing a large problem into subproblems and
devising heuristics for searching the space of possible proofs. The theorem prover
then performs the simpler but still demanding task of proving lemmas, verifying
smaller conjectures, and completing the formal aspects of a proof outlined by its
human associate.
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Chapter 3 Expert System
3.1 Introduction
As mentioned previously, an expert system is actually an advisory system that
embedded with human expertise which mainly used in solving particular types of
problems. Expert systems were developed in the mid-1960s and have begun to
emerge in the following decade. By 1980s, it has also started to be implemented in
commercial field other than academic applications. Expert systems have being the
most common applied AI technology up to nowadays.
Expert systems can be used in various problem areas, such as interpretation
systems, prediction systems, diagnostic systems, design systems, developing plans to
achieve goal, comparing observations, debugging, repairing some diagnosed
problems, instructing or correcting student performance and controlling the system
behavior.
Below are several types of expert systems:
Categories Description
Rule-based expert systems It is normally implemented in commercial field, its
knowledge is presented as production rules (i.e. IF
some conditions, THEN action).
Frame-based expert systems The knowledge is presented as frames, which the
knowledge of a particular object is organized in a
special hierarchical structure.
Hybrid expert systems There includes a combination of several knowledge
representation approaches in this systems.
Model-based expert systems Model being used in this system as reference in
comparing with experimental subject.
Systems classified by their This system will classify the nature of the problem
nature and solve it by retrieving data according to the
class of the problem.
Ready-made expert systems Computer systems that are designed based on
general use instead of the needs of particular users.
Real-time expert systems It has to response as fast as possible by the time it
is needed.
Table 1 Types of expert systems
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The remainder of this assignment will be introducing the fundamentals of
expert systems, which includes the basic ideas of expert systems and how it works.
We will also briefly introduce the application of expert systems in the medical field.
3.2 Basic Concepts of Expert Systems
In consequence of the fast growing technologies nowadays, experts are highly
on demand in this competitive world. Limited experts in various fields such as
engineering, has become a problem for them to stay in pace with the development. In
response to this problem, knowledge lies within experts is needed to be captured.
Expert systems in this case act as an advisory system or even can be used by
experts as knowledgeable assistants. In building such expert systems, the knowledge
of experts will be transferred into a computer system. It can then provide some
advices even explanations to non-experts or novices. The basic concepts of expert
systems include: expertise, experts, transferring expertise and explanation capability.
Expertise
Expertise, normally defined as a high level of knowledge or skill, but in expert
systems we define it as a specific knowledge or skill acquired from training, reading
and experience. It is the main element that is to be implemented in the expert systems,
which enables non-experts or novices to solve particular type of problems.
Experts
The so-called expert is the person who possessed of expertise in their field and
playing the most important role in problem solving and decision making. They are the
main knowledge resources in building expert systems. To mimic the human experts,
expert systems need to have the ability in solving problems, learn from experience,
restructure knowledge and rebuild the rules where necessary.
Transferring expertise
Transferring expertise is the main objective of the expert systems, which
transfer from expert to computer system and then to non-experts or novices. The
transferring process basically involves knowledge acquisition, representation,
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inferencing and transfer to the user. Knowledge acquisition is the process that
transfers the knowledge of experts to the computer system, which then stored in the
system and represented in the computer. And knowledge inferencing is a unique
feature of the expert systems which enables the systems to reason. After inferencing
knowledge, finally it will transfer the solution to the user based on the rules and facts
regarding to the problems.
Explanation capability
Expert systems are distinct from the conventional computer systems, which it
is capable in explaining its advices or operations. This feature allows users to
understand more on the advices provided and also enables the system to justify its
own reasoning.
Data, problems, questions
Knowledge Base
Human Knowledge Inference
Experts Engineer Engine User
Knowledge, concepts, solutions
Figure 8 Process of transferring expertise
3.4 How Expert Systems Work
Common components in Expert Systems
There are few common components that might exist in the expert systems:
Knowledge acquisition subsystem
It is responsible in constructing and expanding the knowledge base by
transferring the knowledge from all possible sources such as human experts,
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books, databases and graphical resources to the computer system. A
knowledge engineer might be playing an important role in this process.
Knowledge base
Typically, the knowledge base is separated into facts and rules. Facts are
something like problem situations and theories underneath, whereby rules are
used to manage suitable knowledge in solving particular problems.
Inference engine
This component is the reasoning tool of the expert systems. It interprets on the
information from knowledge base and blackboard, and provides a direction to
the appropriate system‟s knowledge.
Blackboard
It is also known as the workplace of the expert systems, which mainly used in
recording the specified problem (input) and also the intermediate hypotheses
and decisions.
User interface
Expert systems are user-friendly, which its user interface provides a platform
for user to communicate with the system in natural language.
Explanation subsystem
This subsystem provides the explanation capability to the expert systems.
Knowledge refining system
Refinement is essential to build a good expert system. With refinement, expert
systems able to check on its own performance, make improvements and learn
from experience.
Human elements involved
The main human elements that involved in expert systems are as follows:
Experts
Knowledge engineer
Basically, a knowledge engineer is responsible in interacting with human
experts to build the knowledge base. In building the expert systems, they may
cooperate with other computerized systems to integrate it.
User
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How it works?
Basically, the operation of the expert systems can be categorized into 2 parts:
development and consultation.
In the first stage, development, activities that involved includes constructing
the knowledge base, inference engine, blackboard, explanation facility and any other
necessary software. These activities may be complex; therefore a tool called ES shell
has been used to speed up the process. The ES shell includes all basic components of
expert systems except the knowledge. Improvement can be done to the systems by
rapid prototyping during their development. Rapid prototyping will represent the
knowledge acquired in a better manner that allows quick inference process.
After completed the construction stage, the system will be tested and
validated, and then comes to the consultation part. Expert systems are well designed
to be user-friendly with intuitive features. Hence, the user can easily input their
problems and get advices and explanation in the consultation environment. During the
consultation process, more questions may be asked and answered to reach a
conclusion. The inference engine involve in the reasoning process whereby
explanation facility provides explanations to the user.
Inference
Engine
Knowledge Acquiring Subsystem
User
User Interface
Blackboard
Knowledge
Base
Explanation
Subsystem
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3.5 Expert Systems in Medical Field
Concerning medicine, there are a number of expert systems for different usage
have been developed. Some of the well-known examples are as follows:
Expert systems Description
MYCIN For diagnosing bacteria that cause severe infections and
recommending antibiotic dosage according to different
patient‟s body weight
HELP A complete knowledge based hospital information
system
deDombal`s Leeds For acute abdominal pain
Abdominal Pain System
Table 2 Lists of well-known expert systems in medical field
Other than the above common medical expert systems, there is more expert
systems have been developed recent years in assisting medical works. The
development includes in:
Acute care systems,
Decision support systems,
Education systems,
Quality assurance and administration,
Medical imaging,
Drug administration,
Laboratory systems. (Coiera, 1997)
Categories Example application
Acute care systems Coronary care admission, giving advices on the
management of chest pain patients in the emergency room.
Decision support Typical example is HELP system that has been mentioned
systems above.
Education systems Delivering knowledge on how to reduce risk of cancer.
Quality assurance Monitoring patient clinical data for potential adverse drug
effect.
Medical imaging Automatic interpretation of medical imaging data such as
Cardiac SPECT data.
Laboratory systems Haematology analyzer
Table 3 Application example in different categories of medical expert systems
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3.6 Pros and Cons of Expert Systems
Advantages of expert systems
Expert systems have brought numerous benefits to the users:
Productivity will increase as expert systems work much faster than human.
And the output will also increase due to the reduction of workers needed
which in turn reduce in costs.
Scarce expertise can be captured.
Minimize the employees training costs.
Expert systems can be made in many copies where eliminate the needs of
human experts to travel around and increase the accessibility of expertise.
Expert systems can integrate knowledge from several experts.
Expert systems can operate in hazardous environments and they will not affect
by temper or tiredness because they do not have feelings.
Limitations of expert systems
Beside benefits stated above, there are also some problems and limitations in dealing
with expert systems:
It is not easy to gather knowledge for the computer systems.
Situation assessment may vary from different experts in different conditions.
Expert systems may make mistakes.
There is no common sense being used in making decision.
The construction of expert systems is complex and expensive.
Expert systems will only work well in a narrow domain.
Expert systems have no flexibility and ability to adapt to changing
environment or gaining new knowledge without reprogramming.
3.7 Conclusion
Expert systems are very beneficial computer systems which able to give
crucial advices or opinions to non-experts by using their acquired knowledge. These
systems are definitely not replacing the current available human experts but are
assisting experts in dealing with their job or decision making process. Although
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expert systems are having a very wide range of applications in many fields, there are
still some limitations and problems occur as we discussed in the previous section.
Following the technology and market trend, hard work must be done on expert
systems in solving these limitations for the systems to stay competitive. The future of
expert systems may include the following: increasing system learning capabilities,
using multiple sources of expertise, improving reasoning capabilities and combining
few expert systems working together.
Besides that, expert systems may also cooperate with other technology such as
fuzzy logic, robotics, neural network and so forth. Neural network in this case could
help easing the knowledge acquisition task in the construction of expert systems,
which can cut down the cost and time in employing a knowledge engineer.
Therefore, current expert systems still can be potentially improved. Various
research topics that have great impact in improving expert systems are under
investigation.
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Chapter 4 Conclusion
We have attempted to define artificial intelligence through discussion of its
major areas of research and application. This has shown that AI can be applied in
intelligent problem solving, planning, and communication skills to a wide range of
practical problems. Some of the common features in the application of AI in every
fields including:
1. The use of computers to do reasoning, pattern recognition, learning, or
some other form of inference.
2. The use of large amounts of domain-specific knowledge in solving
problems. This is the basis of expert systems.
3. The purpose of using AI mainly to increase the productivity and decrease
the workload of a human.
As a conclusion, the application of AI has become very wide, and it is
expected to expand even wider to other fields. It is predictable that in the future,
some of the AI application will get combined and builds up an even more
powerful and stronger AI system.
Reference
Turban, Efraim. (1992), Expert systems and applied artificial intelligence. Macmillan.
Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.),
Upper Saddle River, NJ: Prentice Hall.
Wikipedia – Application of Artificial Intelligence .(2009), Retrieved June 20, 2009, from
Wikipedia website: http://en.wikipedia.org/wiki/Applications_of_artificial_intelligence
Machine learning. (2009). In Encyclopædia Britannica. Retrieved June 24, 2009, from
Encyclopædia Britannica Online:
http://www.britannica.com/EBchecked/topic/1116194/machine-learning
AI Overview. (2008). Retrieved June 19, 2009 from AAAI.org website:
http://www.aaai.org/AITopics/pmwiki/pmwiki.php/AITopics/AIOverview
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