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Predicting
Diagnosing
Designing
Planning
Monitoring
Debugging and testing
Instructing and training
Controlling
They operate under mathematical and Boolean operators in their executionand arrive at one and only one static solution for a given set of data. The best
application candidates for expert systems are those dealing with expert
heuristics for solving problems. Successful expert systems will be those that
combine facts and heuristics and thus merge human knowledge with
computer power in solving problems.
Advantages :-
y Increase the probability, frequency, and consistency of making goody decisionsy H
elp distribute human expertisey Facilitate real-time, low-cost expert-level decisions by the non experty Enhance the utilization of most of the available data
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y Permit objectivity by weighing evidence without bias and withoutregard for the users personal and emotional reactions
y Permit dynamism through modularity of structurey Free up the mind and time of the human expert to enable him or hery to concentrate on more creative activitiesy Encourage investigations into the subtle areas of a problem
Disadvantages :-
y The Garbage In, Garbage Out (GIGO) phenomenon: A system thatuses expert-system technology provides no guarantee about the quality
of the rules on which it operates. All self-designated "experts" are not
necessarily so, and one notable challenge in expert system design is in
getting a system to recognize the limits to its knowledge.
y An expert system or rule-based approach is not optimal for all problems, and considerable knowledge is required so as to not
misapply the systems.
y Ease of rule creation and rule modification can be double-edged. A
system can be sabotaged by a non-knowledgeable user who can easilyadd worthless rules or rules that conflict with existing ones. Reasons
for the failure of many systems include the absence of (or neglect to
employ diligently) facilities for system audit, detection of possible
conflict, and rule lifecycle management (e.g. version control, or
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thorough testing before deployment). The problems to be addressed
here are as much technological as organizational.
Applications of Expert systems:
Expert systems are designed to facilitate tasks in the fieldsof accounting, medicine, process control, financial
service, production, human resources, among others.
An example and a good demonstration of the limitations of an expertsystem is the Windows operating system troubleshooting software
located in the "help" section in the taskbar menu. Obtaining technical
operating system support is often difficult for individuals not closely
involved with the development of the operating system. Microsoft has
designed their expert system to provide solutions, advice, and
suggestions to common errors encountered while using their operating
systems.
Expert System in Education
In education field, many of the expert systems application are embedded
inside the Intelligent Tutoring System (ITS) by using techniques from
adaptive hypertext and hypermedia. Most of the system usually will assist
student in their learning by using adaptation techniques to personalize with
the environment, prior knowledge of student and students ability to learn.
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In term of technology used, expert system in education has expanded very
consistently from microcomputer to web based (Woodin D.E, 2001) and
agent-based expert system (Vivacqua A., and LiebermanH., 2000). By using
web-based expert system, it can provide an excellent alterative to private
tutoring at anytime from anyplace (Markham H.C, 2001) where Internet is
provided. Also, agent based expert system surely will help users by finding
materials from the web based on the users profile. Supposedly, agent expert
system should have capability to diagnose the users and giving the results
according to the problems.Besides technology used, expert system also had a tremendous changes in
the applying of methods and techniques. Starting from a simple rule based
system; currently expert system techniques had adapted a fuzzy logic
(Michael Starek, Mukesh Tomer, Krishna Bhaskar, and Mario Garcia ,2002)
and hybrid based technique (Jim Prentzas, Ioannis Hatzilygeroudis, C.
Koutsojannis , 2001).
Expert System for Engineering
This expert system using fuzzy logic method as an engine to enable this
system operates adaptively. This expert system was developed to help first
year engineering student gain deep understanding of fundamentals to be able
to follow the more advanced topics in the engineering fields. This ITS will
help adaptively adjust the training for each particular student on the base on
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his/her pace of learning. ITS will monitor the students progress and have the
ability to make decision about the next step in training.
Expert Systems in Environmental ManagementThe most successful application of Artificial Intelligence (AI) so far is the
development of Decision Support System (DSS), particularly expert system,
which is a computer program that act as a consultant or advisor to
decision makers (Wash, 1999). Expert system has been a new dimension of
humans view of life where everything seems to be easy and more useful by
employing expert system. Thus, the application of expert systems technology
in the domain of environmental management is particularly appropriate in
order to assist human in their attempt to preserve and disseminate valuable
expertise efficiently and at reasonable costs. Nowadays, there have been
numbers of expert system application on environmental management domain
including those which are still in the development process as well as some
newly potential proposed system.
Expert systems in Medicine
It also seems that very early on, scientists and doctors alike were captivated
by the potential such a technology might have in medicine (Ledley andLusted, 1959). With intelligent computers able to store and process vast
stores of knowledge, the hope was that they would become a perfect'doctors,
assisting or surpassing clinicians with tasks like diagnosis. In reviewing this
new field in 1984, Clancey and Shortleaf provided the following definition:
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'Medical artificial intelligence is primarily concerned with the construction
of AI programs that perform diagnosis and make therapy recommendations.
Unlike medical applications based on other programming methods, such as
purely statistical and probabilistic methods, medical AI programs are based
on symbolic models of disease entities and their relationship to patient
factors and clinical manifestations.'
Much of the difficulty has been the poor way in which they have fitted into
clinical practice, either solving problems that were not perceived to be an
issue, or imposing changes in the way clinicians worked. What is now beingrealized is that when they fill an appropriately role, intelligent programs does
indeed offer significant benefits. One of the most important tasks now facing
developers of AI-based systems is to characterize accurately those aspects of
medical practice that are best suited to the introduction of artificial
intelligence systems.
Expert or knowledge-based systems are the commonest type of AIM
(Artificial Intelligence in Medicine) system in routine clinical use. They
contain medical knowledge, usually about a very specifically defined task,
and are able to reason with data from individual patients to come up with
reasoned conclusions. Although there are many variations, the knowledge
within an expert system is typically represented in the form of a set of rules
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Agricultural Expert systems
Rice-Crop Doctor
National Institute of Agricultural Extension Management (MANAGE) has
developed an expert system to diagnose pests and diseases for rice crop and
suggest preventive/curative measures. The rice crop doctor illustrates the use
of expert-systems broadly in the area of agriculture and more specifically in
the area of rice production through development of a prototype, taking into
consideration a few major pests and diseases and some deficiency problems
limiting rice yield.
The following diseases and pests have been included in the system for
identification and suggesting preventive and curative measures. The diseases
included are rice blast, brown spots, sheath blight, rice and zinc deficiency
disease. The pests included are stem borers, rice gall midge, brown plant
hopper, rice leaf folder, green leaf hopper and Gundhi bug.
Farm Advisory System
Punjab Agricultural University, Ludhiana, has developed the Farm Advisory
System to support agri-business management. The conversation between the
system and the user is arranged in such a way that the system asks all the
questions from user one by one which it needs to give recommendations on
the topic of farm Management.
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AGREX
Center for Informatics Research and Advancement, Kerala has prepared an
Expert System called AGREX to help the Agricultural field personnel give
timely and correct advice to the farmers. These Expert Systems find
extensive use in the areas of fertilizer application, crop protection, irrigation
scheduling, and diagnosis of diseases in paddy and post harvest technology
of fruits and vegetables.
Expert System For Mineral Identification
This expert system developed to be used for support the teaching of mineral
properties at college level and hence to promote effective and meaningful
learning of scientific observation in earth science. This system used by the
college students, who may or may not have n-depth computer skills. An
expert system building tool which can be easily maintained by people from
non-computer science background. EXSYS (EXSYS inc. 1994) was used to
build this expert system. EXSYS is a commercial expert system building
tool that has been in the market for several years. It is easy to use, easy to
learn and easy to maintain. EXSYS can explain why and how it reaches a
conclusion.
Expert Systems in Environmental Management
The most successful application of Artificial Intelligence (AI) so far is the
development of Decision Support System (DSS), particularly expert system,
which is a computer program that act as a consultant or advisor to
decision makers (Wash, 1999). Expert system has been a new dimension of
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humans view of life where everything seems to be easy and more useful by
employing expert system. Thus, the application of expert systems technology
in the domain of environmental management is particularly appropriate in
order to assist human in their attempt to preserve and disseminate valuable
expertise efficiently and at reasonable costs. Nowadays, there have been
numbers of expert system application on environmental management domain
including those which are still in the development process as well as some
newly potential proposed system.
Q2. What is the relation between Expert systems and
artificial intelligence?
Ans 2. The expert system is a major application ofartificial intelligence
today. Also known as knowledge-based systems, expert systems act as
intelligent assistants to human experts or serve as a resource to people who
may not have access to an expert. The major difference between an expert
system and a simple database containing information on a particular subject
is that the database can only give the user discrete facts about the subject,
whereas an expert system uses reasoning to draw conclusions from stored
information. The purpose of this AI application is not to replace our human
experts, but to make their knowledge and experience more widely available.
An expert system has three parts: knowledge base, inference engine,
and user interface. The knowledge base contains both declarative (factual)
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and procedural (rules-of-usage) knowledge in a very narrow field. The
inference engine runs the system by determining which procedural
knowledge to access in order to obtain the appropriate declarative
knowledge, then draws conclusions and decides when an applicable solution
is found.An interface is usually defined as the point where the machine and
the human "touch." An interface is usually a keyboard, mouse, or similar
devices. In an expert system, there are actually two different user interfaces:
One is for the designer of the system (who is generally experienced with
computers) the other is for the user (generally a computer novice). Becausemost users of an expert system will not be computer experts, it is important
that system be easy for them to use. All user interfaces are bi-directional;
that is, are able to receive information from the user and respond to the user
with its recommendations. The designer's user interface must also be capable
of adding new information to the knowledge base.
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Q3.: What are the steps in the development of an Expert
System? Explain in detail.
Ans.3: Steps to Develop an Expert System:
Identify: Decide on Topic i.e. Universe of Discourse (Area of Discussion)
y Define Topic + Limits of Topicy Negotiate approval for topic with teCreate a Specification
y Identificationy Why is the Expert System needed ?y What is the problem you are solving by developing this Expert System?y Do not discuss the actual ES. It is the solution, not the problem.
Specify
y Define the solutiony Who will use it?y How it will work?y Where it will be used?y When it will be used?y How it will be made available?y What hardware will be required?y Set clear objectives for the finished project
Design
y Build the Decision Tree in ES-Tree Builder
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y Use short terms for attributes, values and conclusions; details can beimplemented later
y Export tree to .esb format for use in ES-Buildery Enter Rules into Decision Table in ES-Builder. Exporting does not over-
write rules, so it is safe to export multiple times from ES-Tree Builder.
y N.B. Do not delete any attributes in ES-Builder until all work is completedin ES-Tree Builder and exported.
Document
y Combine all Identification and Specification documentation into a singlefile. (e.g. specs.doc)
Implement The SystemImplement as much internal documentation in the data panel in ES-
TreeBuilder as possible including:
y details about the Universe of Discourse, creator and conclusion typey long definition of attributesy long definition of conclusionsy attribute notes and imagesy conclusion notes and images.Export completed tree with data
y Remove any surplus attribute by:y Combining all possible values from like attributes into the one to be
retained on the attributes page
y Dragging the surplus attribute column to last in the decision table theny Going back to the attributes page and deleting the surplus attribute.
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Complete decision table resetting rules for combined attributes.
Check rules are set correctly:
y Sub-groups of conclusions with the same value for an attribute are treatedseparately, but all must be subjected to the next attribute that any of the
sub-group is tested for, i.e. part of the sub-group can not be na for an
attribute that is being used to test other members of that sub-group.
y Once a conclusion becomes unique by virtue of being separated from allother conclusions it must have na selected for every attribute that
follows.
y Attributes can easily be re-ordered by dragging and dropping theappropriate column header.
Testing
y Testing Reporty Have at least 4 people test the completed expert systemy create UserFeedback Sheetsy leave room for both positive and negative criticismy Summarize testing in at least one paragraphy Combine Testing Report into Identification and Specification Document
Evaluation
y Measure success against stated objectives from design phasey Comment on success on each individual objective
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y Comment on overall successy Comment on significant difficulties encountered in projecty Combine Evaluation into Identification and Specification Document
Submission
y Add any references and bibliographical detail required to specificationdocument.
y Save specification document into publishing folder in HTML format asspecs.htm
y Save complete folder to location specified by teacher.y Hand in task and criteria sheets with of Statement of Authorship
completed.
y
Q.4 : Discuss how AI technique helps in building an
automated system?
Ans.4: Present-day computers have made possible the most advanced forms
of automation: operations that are designed to replicate human thought
processes. The enormous capability of a computer makes it possible for an
automated machine to analyze many more options, compare options with
each other, consider possible outcomes for various options, and perform
basic reasoning and problem-solving steps not contained within the
machine's programmed memory. At this point, the automated machine can
be said to be approaching the types of mental functions normally associated
with human beings, that is, to have artificial intelligence. An automated
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machine is able to perform tasks that could be dangerous or difficult for
humans. The impact of automation on individuals and societies has been
profound. On one level, many otherwise dangerous, unpleasant, or time-
consuming tasks are now being performed by machines. The transformation
of the communications industry is one example of the way in which
automation has made life better for the average person. Today, millions of
telephone calls that would once have had to go through human operators are
now handled by automatic switching machines. Automated systems also
make it much easier for people to work in nontraditional settings. They maybe able to stay home, for example, and do their jobs by communicating with
other individuals and machines by means of highly automated
communications systems. However, automation has also had some negative
effects on employment. When one machine can do the work of ten workers,
most or all of those people will be out of a job. In many cases, those workers
will have to be retrainedoften learning newer and higher skillsbefore
they can be reemployed.
Q.5: Explain the different components of an expert
system.
Asn.5: MAJORCOMPONENTS OF EXPERT SYSTEMS:
The user interface : The user interface is the means of communication
between a user and the expert systems problem-solving processes. A good
expert system is not very useful unless it has an effective interface. It has to
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be able to accept the queries or instructions in a form that the user enters and
translate them into working instructions for the rest of the system. It also has
to be able to translate the answers, produced by the system, into a form that
the user can understand. Careful attention should be given to the screen
design in order to make the expert system appear friendly to the user.
The Knowledge Base : The knowledge base stores all the facts and rules
about a particular problem domain. It makes these available to the inference
engine in a form that it can use. The facts may be in the form of background
information built into the system or facts that are input by the user during aconsultation. The rules include both the production rules that apply to the
domain of the expert system and the heuristics or rules-of-thumb that are
provided by the domain expert in order to make the system find solutions
more efficiently by taking short cuts.
The Shell or Inference Engine : The inference engine is the program that
locates the appropriate knowledge in the knowledge base, and infers new
knowledge by applying logical processing and problem-solving strategies.
Working Memory : Working memory contains the data that is received
from the user during the expert system session. Values in working memory
are used to evaluate antecedents in the knowledge base. Consequents from
rules in the knowledge base may create new values in working memory,update old values, or remove existing values.
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Q.6: What are the different areas of Artificial Intelligence?
Explain.
Ans.6 : There are no set areas of AI when it is looked at from what the AI
system does. This is because many of the systems do many different things.
For example, a robot many include areas of Natural Language Processing,
Pattern Recognition and Machine Learning that help it to do its job.
AI can be spilt into seven main sections.
y Pattern Recognition Recognizing patters in given data.y Robotics Allowing mechanical devices to navigate and manipulate
their environment.
y Natural Language Processing Communicating with humansthrough natural text and speech.
y Artificial Life - Modeling and mimicking living systems.y Machine Learning Analyzing data and treads to help with a task
latter.
y Automatic Programming The creation of programs from aprogrammers specification.
y Intelligent computer-aided instruction Customizing the tutoring ofa student to fit the students learning style.
Pattern Recognition:
Pattern recognition involves determining the characteristics in specific
samples and sorting them into classes; a process called classification. This is
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usually done with Machine Learning techniques, allowing the system to
adapt to the data given to it. It can be applied to detecting single words in
speech, recognizing voices, sorting scanned objects by type and filtering out
unwanted pictures (among many others).
Robotics:
The main aspect of robotics today is mobility. For example how can a
mechanical device be controlled to move its body parts in a planned fashion,
or navigate around a room? This can be done by learning the task in a virtual
simulation, and then applying it to the real robot. If specific conditions of
training are respected, the problem has a high probability of working in real
life, but this is no guarantee.
Natural Language Processing:
This is the task of extracting meaning from text, also known as
computational linguistics. Once this meaning is processed, it can also
potentially be interpreted and understood, or at least the basics! One of the
first approach was symbolic, assigning semantic meaning to each word
(verb, noun, adjective). The basic structure of valid sentences would have to
be defined manually, and a search would be performed to match the templatewith the current sentence. A lot of time needed to be spent resolving
ambiguous sentences, and getting the person and tenses of the verbs to
match. If the programmer spends enough time creating the sentence
templates, the results would be fairly encouraging. But this monotonous task
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needs to be repeated for new sentence constructs and new languages all
together.
Artificial Life:
This is a very popular aspect of Artificial Intelligence, which involves
modeling and mimicking living systems. This includes ant hills, wasp nests,
larger forests, towns and cities. To date, very complex and interesting
systems have been created by a multitude of very simple entities. For
example many ants programmed by very small programs would potentially
create an entire system with signs of emergent intelligence.
Machine learning: It is the subfield of AI that studies the automated
acquisition of domain-specific knowledge. The goal of these systems is to
improve their performance as the result of experience. Studies in this field
include problem classification and decomposition, principals of intelligence,
reasoning, and natural language processing. Machine learning can be looked
at as a framework for doing AI research and development. Five main areas
of machine learning are: analytic learning methods; neural network
(connectionist) learning methods; genetic algorithms and classifier systems;
empirical methods for inducing rules and decision trees; and case-based
approaches to learningAutomatic Programming: Computer programming is the process of
constructing executable code from fragmentary information. ... When
computer programming is done by a machine, the process is called automatic
programming. AI researchers are interested in studying automatic
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programming for two reasons: First, it would be highly useful to have a
powerful automatic programming systems that could receive casual and
imprecise specifications for a desired target program and then correctly
generate that program; second, automatic programming is widely believed to
be a necessary component of any intelligent system and is therefore a topic
for fundamental research in its own right.
Intelligent computer-aided instruction: Educational devices incorporating
artificial intelligence (AI) would understand what, whom and how they
were teaching and could therefore tailor content and method to the needs ofan individual learner without being limited to a repertoire of pre specified
responses (as are conventional computer assisted instruction systems).
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SUBMITTED TO SUBMITTED BY
Er.Sonia Manhas Mandeep
(HOD I.T) 80503113027
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