1. 2013
Artificial
Intelligent &
Expert System
Under supervision of Dr. Mahmoud Mostafa
4/7/2013

2. Points of view
1) An Overview AI:
2) Components of AI
3) What is Artificial Intelligence?
4) Characteristics of AI Systems
5) Application of AI
6) Overview ES.
7) How Expert Systems Work
8) Components of ES
9) Knowledge Representation and the Knowledge Base
10) Case-Based Reasoning
11) Inference Engine
12) Forward chaining
13) Backward chaining
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3. 14) Fuzzy Logic Systems
15) Neural Networks
16) Genetic Algorithms
17) Hybrid AI Systems
18) Intelligent Agents
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4. Names ID
DAVID BISHAY SALIB 1087
MOSTAFA NABIL
MOSTAFA EHAB 1232
AHMED HUSSIEN FARRAG 1200
MOHAMED ABBAS MOSTAFA 1119
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5. 1. An Overview AI:
Artificial Intelligence or AI is one of the most important fields of sciences, as new and modern
technologies have become the most dominant thing in our world,the rapid development of computers
has helped many researchers to accomplish many tasks and goals in their field with less time and less
cost also, so if you want to ask about something you want you won’t go anywhere and ask but you can
contact many people through internet and communicate and collaborate with them about your
problem .so according to the developed computerized tools we arrive to that question why we won’t
make a machine that can behave and imitate human being in many things and also with the ability to
learn other things, according to Simon (1977): any individual in the entire world has only limited
abilities and capacities towards solving many problems that face them in their life’s and thinking of a
group of individuals can help to solve this situation, in the fact the problem of communication will often
arise as a drawback to them, so computerized support tools (Artificial Intelligence and any other tools
)can enable people to think and solve their problems quickly and easily and also computerized
supporttools can improve and solve the problem of communication between individuals , process
information to people , allow people to access different kinds of information they want at any place and
many other things .Artificial Intelligence is one of the newest and modern sciences, it had appeared
after the second World War and the name itself was coined in 1956 at the Dartmouth conference, and
since then Artificial Intelligence has expanded because of the theories and principles developed by many
researchers, Artificial Intelligence includes a huge variety of subfields ranging from general-purposetasks
such as learning and perception to specific tasks such as proving mathematical theories, writing poetries
, and diagnosing many diseases. Before approaching the description of artificial intelligence it is very
important to distinguish between artificial intelligence and human intelligence, human intelligence is
defined as the ability or combination of abilities to understand and learn new knowledge and apply
them in the real life situations to gain advantage over others, human intelligence means also to learn
from the past experience and adapt yourself to the new situations. For example: if a student in theBIS
college is going to have 3 midterm exam each one after the other in one day, heshould study the whole
week to prepare and adapt himself to the new situation that he will face in the near future.But the field
of Artificial intelligence focuses on designing machines that can simulate human being behavior,
however designing of the artificial intelligence machine or system can take time to be produced but
won’t be the Strong artificial Intelligence. In fact, some people believe that Strong artificial Intelligence
system or machine is never possible until now due to the major differences in mechanisms between the
individual’s brain and other computer systems, in the future it is expected that artificial intelligence will
have common sense, understand knowledge and expertise like human and will be better than human.
But there are some fears and uncertainty also about the future despite the good things AI can provide,
as artificial Intelligence couldn’t be as we predict because this depends on scientists and researchers
who are studying and solving complex human mind and understanding different emotional feeling of
human, anyway artificial Intelligence has become truly a universal field.
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6. 2. Components of AI
The machine (computer) needs to pass certain test to determine if it possess artificial intelligence or not,
this test is called the TURING test: which was proposed by Alan Turing (1950), it was designed to test
machine (computer) intelligence capabilities, the test can be done by human who acts as judge between
another human and the machine entering the test , the test is based on answering text-questions by the
judge so judge starts asking human certain questions then asks also the machine the same questions ,if
the answers to the questions is similar or just close enough between them that the judge can’t know
who answered that question then the machine passes the test successfully the test does not check the
ability to give the correct answer “as shown in the figure”.AI has focused on the following components
of intelligence: learning, reasoning, problem-solving, perception, and language-understanding.
The computer would need to possess the following components to be intelligent justlike
human:
1) Learning:
It can be done in different ways and techniques; the simplest way is to learn from your errors or
from your past experience. For example: a chess program will try all moves at random until the
program success at one of its moves then the program learns and stores this move for the future
so if the program face this situation again it will be an easy one to solve for the program .rote
learning means to memorize things (as vocabulary)can be done on computers and also more
sophisticated and new way is to learn with generalization which is concluding or extracting the
answer from something that is more general so this help to improve the machine accuracy each
time ,for example: a program with rote learning that learns present continuous won’t be able to
double letter M in the word swim so the result is :is/are swiming if isn’t presented to the
program before but the program with generalization will generalize as in the example the
program will double M IN swim to become is/are +swimming.
2) Reasoning:
It means to use previous acquired knowledge to answer certain questions, reasoning is based on
inferences as inferences can be divided into main types deductive and inductive .deductive
inference means to reason from real facts and documentations from environment to reach
general conclusion so if the rules are clear and documents also are correct the final conclusion is
correct .example: @all fruits are tasty , @apple is one of the fruitsthe logical conclusion is
apple is tasty .inductive inference: judgment is derived from specific examples so conclusion
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7. won’t be correct . Example: @ all men are taller than 1.85 cm, @David is man David is taller
than1.85 cm wrong conclusion. Computer program must be based on deductive inference so
as to produce correct conclusion for the right situation but one of the drawbacks is that
computer can’t distinguish between relevant and irrelevant documents for the topics.
3) Problem solving
According to many AI researchers and facts to solve any problems it requires internal
understanding and representation of the problem to be able to generate possible solution for
the problem.
There are many algorithm for solving the problems the intelligent machine must possess ,in artificial
intelligence we use many terms to understand the problem one common terms is called state: which
represents the solution at given step in the problem solving procedures so solution of the problem is
combination of many states the problem solving apply a given operator to the state to reach another
state so operator is function or method will be applied on given state and will lead to many another
statesthis process will continue along the problem until the desired solution is reached so this way of
solving the problem is referred to state space approach .example: for the state space approach consider
two boxes”
1 2 A 2
3 A 1 3
Initial state desired state
Now consider we define thefollowing operations for the boxes: AU: A UP, AD: A DOWN, AR:
ARIGHT,AL: ALEFT. The diagram illustrates the process to move from initial state to desired
state.
1 2
3 A
By AL byAU
1 A
1 2 3 2
A 3
By AR BY AU BY AL BY AD
A 2 1 2
1 2 1 3 A 1
3 A
3 A 3 2
Desired state 6

8. But in this example we generate many states to reach to the solution, what about less state to reach the
desired solution so it is better AI algorithm so there are some control strategies that can be used for this
goal: A) generate and test: it means to create many state space from the root (starting state) of the
problem and then continue this process until the goal is reached in this case you can find multiple paths
for the goal so the path that is closer to the root is preferred so this algorithm doesn’t filter states.
B) hill climbing approach: under this approach is to select the original state and measure the cost for
reaching the goal from given state, so if the goal isn’t reached new point is generated from current point
and also measured with the cost, so if the goal not reached also the approach should search and select
another starting state and do the process again. C) Heuristic search: it means to determine best state
among available states so this will limit number of states that are within the middle area, one problem
about these techniques is this technique selection process is complex and can’t be correct. D) Means
and ends analysis: one approach is to decrease the way between current state and desired state to
reach it may be simple and less costly to measure the distance between current states and desired
states and apply specific operator to current state so the distance between current state and desired
state is reduced. Generally problem solving methods can be divided into two main things: specific task
for specific problems and general task for general problems as “means and ends analysis”.
4) Perception
It means to feel the environment surrounding you by your organs and react to the environment
so when you react to the environment you create image in your mind which is then analyzed
and decomposed into separate objects with relationship between objects so object can appear
different to many people depending on the angle from which it is being seen or viewed , at
present artificial perception is sufficiently well capable of mean of perception with help of
electronic sensors , for example: a car that drive with moderate speed on high way and also
robots to clan the floor.
5) Language understanding
Language is referred to system of learning and using this complex system of communication to
interact with other people so to enable computer to understand language is not an easy task so
programmers designed computer programs that are able to respond and interact with users
such as search engine of Google you write the word and then Google try to return the best
results ,one way to make machines efficient in understanding language is to watch and discover
the machine mistake and to be able to fix the problems and also enabling the user to write
feedback to improve the way of understanding for machines.
6) Planning
It means to plan for the problems so it is one way to achieve the goals based on predefined
methods and techniques .planning and reasoning differ into the structure as reasoning deal with
testing the reason for the problems and the possible solution from given collections of data and
knowledge also.
7) Knowledge acquisition:
It means to store knowledge that is new to you as new English word you will write it to
remember it .so knowledge acquisition is difficult for machines but the process that machines
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9. follows in understanding the knowledge make it more familiar to any new knowledge ;this
process consists of:1)generate knowledge from stored base,2)setting the structure for the
knowledfe,3)learn the new knowledge,4)refine the knowledge.
Intelligent search:
It is important component for solving the problems in computer science as many problems can
be solved with less time through intelligent search, one way of search is to guess the possible
solutions and then refine the solutions. Another way is to use form of organisms (guesses) then
select and recombine the structure to fit the problems.
3. What is Artificial Intelligence?
It is the science of studying, developing and restructuring machine to include intelligence so as to
understand the surrounding environment and other people; this can be a general and abroad
definition than other Artificial Intelligence researchers who tried to define AI, at 1985(HAUGELAND)
defined AI as way that will make computer think so machines will have mind as normal human
another researcher at 1991(Kurzweil) defined AI as it is the art of creating machine that will do tasks
that require intelligence such as problem solving ,decision making. At 1998: from point of view of
Nilsson defined AI as a technique that is concerned with behavior of human being, all disciplines in
our life had been working toward artificial intelligence from long years, the most important
disciplines that referred to AI is philosophy, mathematics ,psychology and computer science, why
those fields are most concerned with AI because 1) philosophy: Aristotle was the first to develop an
informal system of logical thinking for reasoning which is used to generate conclusions
.2)mathematics: ALFRED TARSKI (1902-1983)introduced a theory of reference that determine how
to relate objects in the world and then determine what will you do with logic and computation
beside that the theory of probability on which AI is based was invented by Italian GEROLAMO (1501-
1576)that describe possible outcome from a game.3)psychology: it is concerned with studying how
human and animal think and act and it views a brain as an information processor device which is the
goal ofAI.4)computer science: which is all about building and efficient and effective computer
systems and programs to help individuals with their everyday work. Artificial intelligence is our
future because AI will:1)dojobs with less cost ,high efficient , with less time and effort .2)machines
won’t sleep ,won’t get ill so they will do a lot of work.3)they can help many people who lost their
terminals or disable people so they will represent the source of information , learning and teaching
.4)they can help in security alerting in case of fire , crime .5)machine don’t feel so they will enforce
rules and policies without mistake for example:if you are a amanager and one of your employees
came late you can simply forgive him.But besides all those advanatages there some disadantage
with machines :1)wewill depend on them and this can lead to many concequeicies as they control
our lives ,they will control us also.2)they won’t provide us with touch and quality that you expect,3)
Limited sensory input compared to our brains as artificial mind is only capable of understanding
small amount of information and also need individuals to input devices. But if you need something
to done efficiently in less time and also with less cost or when it is too dangerous for human to
perform required task you should turn to artificial intelligence. The picture below is an example of
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10. an artificial intelligence; you ask the question then the machine answers, the answer to the question
is automated.
4. Characteristics of AI Systems
1. Non algorithm processing: non algorithm approach contain the main logic for the application in
contrast with algorithm approach which contain predefined steps for the problem to solve so non
algorithm is reactive system as it will change with changing the problem, one important technique is
the neural network which attempt to emulate processing pattern of the biological brain for human
being and it also accepts many inputs the process the inputs and produce one single output.
2. Symbolic processing: it is the attempt to create AI using programming language so as to process
symbols ,symbols are like variable and they represent ideas and objects in our real world ,the goal of
symbols is to construct a communication so symbolic AI process a real world entities and objects
but non symbolic AI uses numbers to describe statistical patterns .symbols can be arranged in
different structure such as networks, symbolic processing succeeded in in creating machine with
artificial general intelligence. The major disadvantage of symbolic processing is that they can’t deal
with perception, learning and simulation pattern so many AI researchers began to look into “sub
symbolic theory “to solve problems that symbolic processing can’t deal with. Sub symbolic such as
neural networks and fuzzy system (logic) which consists of variety of techniques and methods for
representing knowledge and information that is imprecise and uncertain, fuzzy logic created rules to
deal with uncertainty so fuzzy system are closer to the way people think than traditional.
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11. 3. High performance: AI systems should be capable of performing tasks with high speed and with
efficiency also as they were designed with intelligence, they are expected that they can reach level
of performance equal to or exceed human experts.
5. Application of AI
Typical artificial intelligence applications are:
A. Game playing: as many youth interesting in paying games and they want to every competition
they want to enter against anyone, now AI machines can offer youth what they want as AI
machines can try all possibilities to achieve winning strategy against competitors for example: in
chess game you can just easily beat the world champion player by just using AI machines as a AI
machines can look for 200 million positions per second.
B. Speech recognition: it is the translation of spoken language by individuals into text
understandable to do specific things .for example: voice dialing (I want to call work), data entry
by voice (storing and entering different products by human being on the computer), it is
possible to construct computer using speech recognition so users can just talk to computer and
don’t use keyboard and mouse.
C. Understanding natural language: problems of understanding language depends on syntactic
and semantic meaning of the phrases so syntactic required to correct grammar mistakes and
analyze sentences and semantics is performed after the syntactic to determine meaning of
phrases and the linkage between words to be understandable . So a robot should understand
natural language as it is classified as intelligent machine.
D. Expert systems “will be discussed in more details”: it is the core AI application ,it is a decision
making or problem solving software package that attempt to reach level of performance that
emulate human expert problem solving.si expert system consists of :1)knowledge base: it is
collection of huge information extracted from human experts ,2)inference engine: it is the
engine that is responsible for taking orders and information from the user .so it is the processing
elements among other components and it makes the use of knowledge base to generate
conclusion for many situatioins,3)working memory: contain data received from the users so
knowledge base can create ,update and delete in working memory,4)other components
includes: A)user interface: user communicates and commands expert system through this
system.
B) Explanation mechanism: it is the way for reaching general conclusion and it is very important
in reasoning process.
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12. E. Robotics and navigation: robots are machine designed to perform complex tasks and serve
human being in their daily life, robots at the past was capable to actions that has been before
and couldn’t do new actions based on their own but now AI researchers and scientists
developed robotics to include artificial intelligence so robotics can now mimic human actions,
new term is introduced and called autonomous robotics which is referred to capability of robots
to adapt themselves with surrounding environment and learn new things also.
 This diagram shows disciplines of AI along with AI components and applications areas of AI; this
diagram describes what artificial intelligence is all about.
AI has been used along many fields such as:
1) Finance: banks use intelligent software to analyze financial data and financial market
fluctuations and to predict stock prices in stock markets.
2) Hospital and medicine: hospitals can now use AI to arrange beds, to check for empty room if
patient is coming new to the hospital and help doctors to periodically check their patient; neural
network can help in supporting decisions for the whole clinic.
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13. Expert system
INTRODUCTION
Expert systems are computer based systems that uses knowledge and reasoning techniques to solve the
problem that require human experience knowledge from experts and other sources such as text books
and journal articles entered into a system in a coded form which is then used by the system reasoning
presses to offer advice on request
Expert systems belong to border discipline of artificial intelligence which has been defined by barr and
FEIGENBAUM in 1981 as the part of computer science that is concerned with designing intelligent
computer systems meaning that designing system that can have characteristics such as learning
language and understanding problems
Artificial intelligence as a separate discipline started in 1950 when it was recognized that the computers
were just a giant calculators dealing only with numbers so after inventing the artificial intelligence it
spread rapidly in making robots machine industry and most of the practical fields
HISTORY OF EXPERT SYSTEM
Expert systems were introduced by researchers in the Stanford Heuristic Programming Project, including
the "father of expert systems" with the Dendral and Mycin systems. Principal contributors to the
technology were Bruce Buchanan, Edward Shortliffe, Randall Davis, William vanmelle, Carli Scott and
others at Stanford. Expert systems were among the first truly successful forms of AI software
Research is also very active in France, where researchers focused on the automation of reasoning and
logic engines. The French Prolog computer language, designed in 1972, marks a real advance over expert
systems like Dendral or Mycin: it is a shell,[16] that is to say, a software structure ready to receive any
expert system and to run it. Prolog has an integrated inference engine using First-Order logic, with rules
and facts. Prolog is a tool for mass production of expert systems and was the first operational
declarative language later becoming the bestselling AI language in the world However Prolog is not
particularly user friendly and incorporates Horn Logic, which is an order of logic away from human logic.
In 1981 the first IBM PC was introduced, with MS-DOS operating system. Its low price started to multiply
users and opened a new market for computing and expert systems. In the 80's the image of AI was very
good and people believed it would succeed within a short time
The development of expert systems was aided by the development of the symbolic processing
languages Lisp and Prolog. To avoid re-inventing the wheel, expert system shells were created that had
more specialized features for building large systems
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14. EXPERT SYSTEM DEVELOPMENT
Knowledge engineers expect to work with systems that cannot be well defined in advance. The
interaction phases with the user are crucial to the development of the expert system. The
process tends to be circular rather than linear (Fig. A2). The original rules developed may later
be rewritten entirely or dropped, altogether as the experts and knowledge engineers gradually
refine their understanding of the knowledge that must be included in the knowledge base.
Interaction with the user in the early stages is crucial. Typically, steps in expert system
development includes:
Front-end analysis: Problem identification, cost and effectiveness requirements, stakeholder
buy-in.
Task analysis: Identify task(s), behavioral sequence and required knowledge.
Prototype development: Identify case studies, develop small scale system to prove concept and
provide practice.
System development: Rearrange overall structure as required, add knowledge.
Field testing: Test system with actual users, revise as necessary.
Implementation: Port system to hardware to be used in the field; train users to use the system.
Maintenance: Establish means to update the system, update as required.
The development is the result of a collaborative effort from among knowledge engineers,
domain experts and end-users. The knowledge engineer acts as a bridge between the domain
expert and the knowledge encapsulation environment. The tasks are:
- Acquire knowledge from domain experts; formalize terms, eliminate vagueness and
inconsistencies
- Model and organize information received from domain experts
- Integrate the facts, rules, objects and relationship information into the expert system source
code.ert systems
HOW DOES THE EXPERT SYSTEM WORK
An expert system is made up of three parts:
1. A user interface - This is the system that allows a non-expert user to query (question) the
expert system, and to receive advice. The user-interface is designed to be a simple to use as
possible.
2. A knowledge base - This is a collection of facts and rules. The knowledge base is created
from information provided by human experts
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15. 3. An inference engine - these acts rather like a search engine, examining the knowledge base for
information that matches the user's query
Knowledge representation
Knowledge representation (KR) is an area of artificial intelligence that aimed at representing
knowledge in symbols to facilitate the process; KR research involves analysis of how to reason
and effectively and to use symbols to represent a set of facts within a knowledge domain. A
good knowledge representation covers six basic characteristics:
1. Coverage: which means the KR covers many of the information. Without a wide coverage, the
KR cannot resolve any problems that face it.
2. Understandable by humans. KR is viewed as a natural language, so it is simple to be
understood and will contain simple logic
3. Consistency: KR can eliminate redundant knowledge so as not to be confused.
4. Efficient: KR will represent knowledge with more speed and at high level of correctness
5. Easiness: for altering, deleting and updating data.
6. Supports the intelligent activity which uses the knowledge base
Problems with KR
One problem in knowledge representation is how to store and retrieve knowledge easily in
information system.
THE RULE BASE OR KNOWLEDGE BASE SYTEM
In expert system technology, the knowledge base is expressed with natural language rules IF For
examples:
"IF it is living THEN it is mortal"
"IF his age = known THEN his year of birth = current year - his age in years"
"IF the identity of the germ is not known with certainty AND the germ is gram-positive AND the
morphology of the organism is "rod" AND the germ is aerobic THEN there is a strong probability (0.8)
that the germ is of type enterobacteriacae
This formulation has the advantage of speaking in everyday language which is very rare in computer
science (a classic program is coded). Rules express the knowledge to be exploited by the expert system.
There exist other formulations of rules, which are not in everyday language, understandable only to
computer scientists. Each rule style is adapted to an engine style.
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16. BENEFITS OF EXPERT SYSTEMS
Permanence: Expert systems do not forget.
Reproducibility: Copies of an expert system can be made.
Power: For applications where there is a maze of rules exhibited, it can be unravelled by the expert
system.
Efficiency: Expert systems can increase throughput and reduce personnel costs.
- Expert systems are inexpensive to operate.
- Development costs can be amortized over many years.
- Expert systems can eliminate routine costs and reduce major maintenance costs.
Consistency: With expert systems, similar events are handled the same way. Expert systems will make
comparable recommendations for 'like' situations and are not affected by recent or primary effects.
Documentation: Expert systems provide permanent documentation of the decision process.
Completeness: An expert system can review all the transactions or possibilities.
Timeliness: Fraud and/or errors can be prevented. Information is available sooner for decision making
and action. The expert system works 24 hours a day, all year long.
Scope: The expert system can encompass the cumulative expertise of many human experts.
Business success: Owners reduce the inherent risks of conducting their business due to:
- Consistency of decision making.
- Documentation (ISO requirements)
- Acquired expertise
Positive impacts:
- Productivity gains and cost savings.
- Critical new tool for managers and a proactive answer to expertise attrition.
- Decisions and solutions are more consistent and less subject to biases or sensitivity to the environment
- Employment: shift to-wards more satisfying work. ET
EXPERT SYSTEM STRUCTURE
The structure and operation of an expert system are
. Experts use their knowledge about a given domain coupled with specific information about the
current problem to arrive at a solution. For Example, a physician wouldpossess knowledge about variety
of possible diseases and, coupled with specific Information about a given patient, would be able to
diagnose the patient's problem.
Expert systems solve problems using a process which Is very similar to the methods used by the human
expert
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17. ADVANTAGES
1-CONVERSATIONAL
Expert systems offer many advantages for users when compared to traditional programs because they
operate like a human brain
2-QUICK AVAILABILITY AND OPPORTUNITY TO PROGRAM ITSELF
As the rule base is in everyday language (the engine is untouchable), expert system can be written much
faster than a conventional program, by users or experts, bypassing professional developers and avoiding
the need to explain the subject.
3-ABILITY TO EXPLOIT A CONSIDERABLE AMOUNT OF KNOWLEDGE
The expert system uses a rule base, unlike conventional programs, which means that the volume of
knowledge to program is not a major concern. Whether the rule base has 10 rules or 10 000, the
engine operation is the same.
4-RELIABILITY
The reliability of an expert system is the same as the reliability of a database, i.e. good, higher than
that of a classical program. It also depends on the size of knowledge base.
5-SCALABILITY
Evolving an expert system is to add, modify or delete rules. Since the rules are written in
6-PEDAGOGY
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18. The engines that are run by a true logic are able to explain to the user in plain language why they
ask a question and how they arrived at each deduction. In doing so, they show knowledge of the
expert contained in the expert system. So, user can learn this knowledge in its context. Moreover,
they can communicate their deductions step by step. So, the user has information about their
problem even before the final answer of the expert system.
DISADVATAGES
Every expert system has a major flaw, which explains its low success despite the principles that it is
based upon having existed for 70 years: knowledge collection and its interpretation into rules,
Every expert system has a major flaw, which explains its low success despite the principles that it is
based upon having existed for 70 years: knowledge collection and its interpretation into rules, or
knowledge engineering. Most developers have no automated method to perform this task; instead
they work manually, increasing the likelihood of errors. Expert knowledge is generally not well
understood; for example, rules may not exist, be contradictory, or be poorly written and unusable.
Worse still, most expert systems use a computational engine incapable of reasoning. As a result, an
expert system will often work poorly, and the project will be abandoned. Correct development
methodology can mitigate these problems. There exists software capable of interviewing a true
expert on a subject and automatically writing the rule base, or knowledge base, from the answers.
The expert system can then be simultaneously run before the true expert's eyes, performing a
consistency of rules check. Experts and users can check the quality of the software before it is
finished
Many expert systems are also penalized by the logic used. Most formal systems of logic operate
on variable facts, i.e. facts the value of which changes several times during one reasoning. This is
considered a property belonging to more powerful logic.
Case-based reasoning:-
It is the process of solving the problems using the solution of identical old problem or from past
experience. It isn’t used only for computers but it is powerful for everyday life.
Ex) An auto mechanic who fixes an engine by that exhibited similar symptoms is using case-based
reasoning.
Case-based reasoning:-Process:-
1. Retrieve: Given the problem. Use the memory to select similar case to solve the problem. A case
consists of a problem, its solution, and, typically, the way about how the solution was derived.
2. Reuse: this step determines how the solution is derived from previous cases to the target
problem. This may involve adapting the solution as needed to fit the new situation.
3. Revise: as you get the solution to the target situation, test the new solution in the real situations,
then revise.
4. Retain: After the solution is obtained for the problem and adapted, store the results of the
problem in the memory for further use.
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19. Criticism about case base reasoning: case base reasoning is based on generalization rules and
generalization rules can’t be correct so case base can provide many uncorrected information so
there is a recent work that developers will assign case base reasoning with statistical framework
so it will be based on probability that will tell you the information with percentage of confidence
and reliability
Inference engine:-
In the field of computer science, and artificial intelligence, an inference engine: is computer software
that tries to find and extract answers from data and information that is stored in the knowledge base
using many methods of artificial intelligence, In order to produce a reasoning, it should be based on
logic. There are several kinds of logic: propositional logic, predicates of order 1 or more, epistemic logic,
modal logic, temporal logic, fuzzy logic, etc. Except for propositional logic, all are complex and can only
be understood by mathematicians, logicians or computer scientists. Propositional logic is the basic
human logic that is expressed in syllogisms. The expert system that uses that logic is also called a zeroth-
order expert system. With logic, the engine is able to generate new information from the knowledge
contained in the rule base and data to be processed.. A strong interest in using logic is that this kind of
software is able to give the user clear explanation of what it is doing and what it has deduced Better yet,
thanks to logic, the most sophisticated expert systems are able to detect contradictions in user
information or in the knowledge and can explain them clearly, revealing at the same time the expert's
knowledge and way of thinking. It is the attempt to emulate some process of the brain system and many
expert systems use this brain justify the information they need to achieve certain goal from the
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20. knowledge, inference engines are also capable of performing logical processing, and can do many
probability calculations to reach conclusions that the knowledge database can’t do
The program uses inference engine can be seen as a mechanism for selective active where the
processing operations are directed by the recent value of data, expert system have two general methods
of processing the stored data (forward chaining, backward chaining), the rules of the expert system is to
analyze data by the means of the inference engine, and the results are feed back into the system's data
storage as new data. The two methods are: 1. Forward chaining: is a method of reasoning, it starts
with data available on hand and uses inferences to increase amount of data related to the objective that
will be achieved so forward chaining depends on searching all possibilities until it concludes the final
result so after the process of finding data and the goal is reached, it adds new information to its own
data.
For example: suppose our objective is to reach whether “a cat named kitty eats mouse or not”. Suppose
the following rule bases: 1) if X is a cat then X is black, 2) if X is black then X eats mouse. So forward
chaining will be: 1) kitty is a cat,2) kitty is domestic,3) kitty eats mouse. Forward reasoning will be: 1)
kitty is a cat and kitty is domestic,2)kitty is a cat and is domestic. so 3)kitty eats mouse. In this example
computer process data and tries to reach the objective so this method of searching is called data-driven.
2. Backward chaining: it is the opposite to forward chaining as it works backward toward the targeted
objective so backward chaining starts by listing the objectives and then works backward searching for
the data that deliver to the objective.
In the previous of forward reasoning example “a cat named kitty eats mouse or not “, backward
reasoning will work in the opposite direction: 1)? Does kitty eats mouse, 2)? Is black and eats mouse, 3)
kitty is black and kitty eats mouse. So the computer derived to the objective by first questioning about
the goal itself. This method is called goal-driven, so backward chaining is often used by expert system.
Comparison between advantages and disadvantages of the techniques
Advantages of forward chaining disadvantages of forward chaining
1) Can provide large amount of Take much time as the system will try
information from small amount of and ask all questions that could deliver
data. to the correct answer.
2) It fits many management tasks such
as: planning, controlling and
monitoring.
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21. Advantages of backward chaining disadvantages of backward chaining
1) It focuses in the goals so less time is It will use specific way of reasoning
consumed and specific questions is even if this way isn’t required as system
asked and will lead to the correct will switch to new one.
answer.
2) It can be used in debugging
,diagnostics and many others
 Those two techniques are used by expert system, many expert systems use both forward
chaining and backward chaining so for a given situation expert system can use one of the
techniques that will achieve the goal directly and leave the other technique.
 Inference engine relies on: 1) interpreter: executes the next step by applying specific rules on
it,2)scheduler: it maintains controls over steps in interpreter by measuring the effects of
applying information rules.3)consistency enforcer: the foal of this components to assure
consistency and provide consistency solution.
The recognize-act cycle:-
The inference engine can represent many states with cycle processing of three action states: match
rules, select rules, and execute rules. 1) Match rule: the inference engine finds all of the rules that by the
current contents of the data store.2) select rules: applies some selection strategy to determine which
rules will actually be executed. The selection strategy can be coded into the engine .3) execute rules:
executes or fires the selected rules.
Data-driven computation versus procedural control:-
The inference engine control is performing testing of the data store states. This is referred to qualify
data-driven in contrast to the more traditional procedural control: in which information about the
problem is combined with instructions about the control, the inference engine model allows a more
complete separation of the knowledge from the control (the inference engine).
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22. INTELLIGENT TECHNIQUES
This techniques is considered as apart of artificial intelligence which are :
Intelligent agents
Intelligent agents: automatic entities which direct their activities to accomplish certain goals, so we can
describe it as RATIONAL. These entities may be very simple or extremely complex. Intelligent agents are
sometimes described as abstract units. Intelligent agents in artificial intelligence can be related to
economics.
IA: has been defined in many ways:-
For Nikola KASABOV: system must show the following aspects:-
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23. 1- Accommodate problem solving techniques (adopt a way that similar to the expert system which
always learn and can act by it self
2- Can act online and in the same time(real time)
3- Ability of analyze itself in terms of acting error& success
4- Learn new techniques and demands from the working environment
5- Can store and retrieve
Classes of intelligent agents:-
Divided into 5 classes based on their degree of intelligence and capability:-
1- Simple reflex agents: act only on the basis of the current principle or in other words the simple
reflex is based On: if condition then action. This agent only operates in an environment that is
only observable. So this agent always uses the infinite loop but in the case that the agent could
randomize its actions it may be possible to escape this infinite loop.
2- Model-based reflex agents: can deal with partially observable environment. it should adopt an
internal model approach
3- Goal-based agents: a model that reflect the desired outcomes. This allows the agent to pick a
solution among many multiples so it can select the option that will achieve the set goal
4- Utility-based agents: can define between goal states and non-goal state .it defines a measure to
tell the difference which is can be generated through the use of utility function which maps the
difference in the outcomes of every state
5- Rational utility-based system: pick up the action associated with the highest outcome
6- Learning agents: allows the agent to operate on unknown environment where there are no
explicit data or clear information. In that agent model we must differentiate between two
definitions firstly :-
 The learning element which is responsible for making innovations and improvements.
 The performance element: responsible for choosing the external actions.
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24. Structure of the agent
Is a simple agents which is defined as [Agent function]. It also planned to arrange definition to possible
action that can depend and perform on it. The function that affect at the end of these action is:
Other classes for the intelligent agents:-
1. Decision agents : supports the decision making process
2. Input agents: This agent is like a process that predicts and makes sense about input sensors.
3. Process agents: solving problems e.g. speech recognition
4. World agents: combine different classes to provide the automatic behavior (random)behavior
5. Physical agents : an entity acts through sensors and actuators
6. Temporal agents: use stored information to offer instructions.
Intelligent agents are used in automated online assistants where they receive customers
inquiries and trying to solve it automatically
EX: the dialogue system , an avatar , and an expert system
Neural networks
The term refers to a biological circuit, the modern usage o f the term often refers to artificial neural
system .which is composed of artificial nodes, these networks may be used for predicative modeling
A biological neural network consists of a group of chemically connected historically; digital computers
evolved from the von Neumann model, and operate via the execution of explicit instructions via access
to memory by a number of processors. On the other hand, the origins of neural networks are based on
efforts to model information processing in biological systems. Unlike the von Neumann model, neural
network computing does not separate memory and processing.
Criticism or disadvantages :-
 Requires a large and complex training for real-world operation. But that is natural as every
learning machine needs sufficient training in in order to implement it in the real world.
Hybrid System
The objective of computerized-based information system is to assist management for solving problems
inside organization. The managerial decision making process combined with MSS technologies in solving
problems. From the cognitive science prospective every natural intelligent system is hybrid because it
performs mental operations. So hybrid system may combines different techniques as: fuzzy logic ,
genetic fuzzy system. The integration of different learning and adaptation techniques, to overcome
individual limitations and achieve synergetic effects through hybridization or fusion of these techniques,
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25. has in recent years contributed to an emergence of large number of new superior class of intelligence
known as Hybrid Intelligence.
The problems in MSS it can be solved by employee’s different tools techniques:
1) These tools must be independence to solve problems and making decision process.
2) Using several integrated tools.
_The goal of hybrid system is reaching a good and successful decision to solving problems.
_Hybrid system provides information techniques and performs many tasks and supports each of them.
_They working together to reach many answers and producing smart answers to solve problems.
 Fuzzy logic: this technique consists of variety of concepts and techniques for representing many
uncertain events and information so fuzzy logic creates and enforces many rules that deals with
subjective values and uncertain data so fuzzy logic is so closer to the way people think.
 Genetic algorithm: refers to the adaptive computation so it consists of variety of problem
solving methods that promote evolution of solution to specific problems using the model of
living organism adapting to their environment so it can be used to maximize profit in advertising
field.
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26. REFRENCES
For artificial intelligence
1. http://library.thinkquest.org/2705/basics.html
2. Decision Support Systems and Intelligent Systems (7th Edition) PDF.pdf
3. Russell S., Norvig P. Artificial intelligence- a modern approach
(2ed,PH,2003)(T)(1112s).pdf
4. http://www.differencebetween.com/difference-between-artificial-intelligence-and-vs-
human-intelligence/
5. Wikipedia
6. http://www.alanturing.net/turing_archive/pages/reference%20articles/What%20is%20
AI.html
7. http://www.learnartificialneuralnetworks.com/ai.html
8. http://www.infobarrel.com/Advantages_and_Disadvantages_for_Artificial_Intelligence_
The_Pros_and_Cons_of_AI
9. http://www.umsl.edu/~joshik/msis480/chapt11.htm
10. http://www.electronicsteacher.com/robotics/robotics-technology/artificial-
intelligence.php
For INFERENCE ENGINE AND CASE BASE REASONING
1-http://www.wisegeek.com/what-is-an-inference-engine.htm
2- http://en.wikipedia.org
3-
http://books.google.com.eg/books?id=WRUSR2IkDjIC&pg=PA8&lpg=PA8&dq=what+is+advantage+a
nd+disadvantage+of+forward+chaining&source=bl&ots=6nM0UNt5iA&sig=dl27wkTOhZj2KZoY5d9r8
2FChqE&hl=ar&sa=X&ei=EmRgUfqcE4PFPf7ygagL&ved=0CCwQ6AEwAA#v=onepage&q=what%20is
%20advantage%20and%20disadvantage%20of%20forward%20chaining&f=false
For expert system
1. Wikipedia
2. Preview to expert system by ALN
3. Priprinciples. of expert system
 For genetic algorithm and intelligent agent and neural network, hybrid
system fuzzy logic:
1. Wikipedia
Dss book
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