1st assignment of es

8/7/2019 1st Assignment of ES

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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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1st assignment of es

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