Expert Systems Vs Human Expert

What is Expert Systems? Expert systems are designed to solve complex problems by reasoning about knowledge, like an expert, and not by following the procedure of a developer as is the case in conventional programming.

What is Human Expert? An expert / expert (human expert) is an individual who has a superior capability understanding of a problem. For example: a doctor, financial advisor, an expert in car engines, etc..

 Comparison of Human Expert and Expert Systems

• Time Availability : Human Expert (Working day). Expert Systems (any time)
• Geographical :  Human Expert (specific location). Expert Systems (wherever)
• Security: Human Expert (not replaceable). Expert Systems (can be replaced)
• Perishable : Human Expert (yes). Expert Systems (no)
• Performance : Human Expert (variable). Expert Systems (consistent)
• Speed:Human Expert (variable). Expert Systems (consistent and more fast)

The fundamental reason why the ES (Expert Systems) was developed to replace a experts:

• Can provide expertise at all times and in various locations
• Automatically doing routine tasks that require an expert.
• An expert will retire or leave
• An expert is expensive
• Expertise is needed also in a hostile environment (hostile environtment)

Comparison of Conventional Systems and Expert System

Conventional Systems vs Expert System

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Definition Of Expert Systems

Definition Of Expert Systems

What is Expert Systems? Expert Systems is a computer program designed to model ability to solve problems like a certain expert (human expert). What is human expert? An expert / expert (human expert) is an individual who
has a superior capability understanding of a problem. For example: a doctor, financial advisor, an expert in car engines, etc..
Ability of expertise:

• Can identify (recognizing) and formulate the problem
• Resolving problems quickly and accurately
• Describe the solution
• Learning from experience
• Restructuring of knowledge
• Determining relevance / relationship
• Understand the limits

What is expertise? Expertise is Broad understanding of the task or the specific knowledge obtained from training, reading and experience.
Type-type of knowledge possessed of expertise:

• Theories of problems
• The rules and procedures that refer to the problem areas
• The rules (heuristics) to be work will on the situation
• The global strategy to solve various types of problems
• Meta-knowledge (knowledge about knowledge)
• The facts

What Is Knowledege?
Data+ processing  = Information
Information processing + (experience, training, etc.) = knowledge

 

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Examples of Knowledge Representation Logic

example of knowledge representation logic

Logic is a form of knowledge representation of the oldest. This representation  type uses the expressions in formal logic to represent the knowledge base. Basically the logic is the process of forming conclusions and draw an inference based on the fact that already exist. Input from the process logic a premise or the facts admitted the truth so that by doing logical process of reasoning can be formed on an inference or conclusion that true as well.
Examples of the simple fact that we will represent the logic is as follows:

Helder is a dog
Facts in English they will be represented in logic, namely:
dogs (Helder)
We can also represent another logical facts, namely that all
dogs have tails
x: dog (x)
caudate (x)
Then deductively (reasoning starts from the general principles for get a more specific conclusion) of the mechanism of  this logic we can obtain a new representation:
caudate (dog)
By using the mapping function is backward, we can download the generate sentences in English
Helder caudate

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Advantages Creating Knowledge Representation

advantages make knowledge representation

With representation, many things that we will get in our solve a problem. Below are some advantages that
will we get when we make representations of knowledge, namely:

• With a good representation, create objects and relationships that are important to is clear.
•  Representation uncovers constraints (limits) in a problem. We can reveal the influence of an object or relation to the object or other relations.
• With our representation of objects and relations will get together.
• We will be able to see everything we want in one time
• We can remove all the components that are not associated with problems which we solve. Or we can hide some information we do not need for awhile, and when we we need to show again.
• The representation will make the problems we are finished becomes transparent. We will understand the problems we solve.
• With our representation will be able to uncover a problem in complete, so that issues can be resolved.
• The representation will allow problems to be concise. We will concise thinking (representing what we want to represent the efficient).
• With the representation, it will make our work becomes faster
• With the representation, making the problems we can solve computerized. With this representation we will be able to perform procedures procedure in solving a problem.

Besides the advantages above the one thing a principle in knowledge representation is If a problem is described by
using appropriate representations, it can be ascertained that these problems can be solved.

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Definition Of Knowledge Representation

knowledge representation

Representation is intended to capture the essential properties of problems & make such information accessible to problem-solving procedure. Representation language must be able to make a programmer is able to express the knowledge needed to obtain solutions to problems.

Many ways to represent knowledge (facts) in AI programs.
There are two entities that need attention:
– Fact: the truth. The fact this is what we represent.
– Representation of facts. From this representation, we will be able to manipulate it.

Good representation, should:

• Express it explicitly
• Make the problem transparent
• Complete and efficient
• Featuring natural boundaries that exist
• Suppress / eliminate the necessary details
• Computation can be performed (no limit / konstraint)

In short, knowledge representation are classified into 4 categories:

• Representations of logic: The representation of this type using the expressions in formal logic to represent knowledge base
• Procedural Representation: Representation describes knowledge as set of instructions to solve a problem.
• Network representation: This representation captures the knowledge as a graph where knots describe an object or concept of problems encountered, while edge describes the relationship or association between them
• Structured Representation: Representation structured network extending to the how to make each knot into a complex data structure

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Artificial Intelligence and Natural Intelligence

When compared to natural intelligence, artificial intelligence has the advantage commercial, among others:

• Artificial intelligence is more permanent. Natural intelligence will quickly subject to change.
• Artificial intelligence is more easily duplicated and distributed
• Artificial intelligence is consistent.
• Artificial intelligence can be documented.
• Artificial intelligence can do the job faster than natural intelligence
• Artificial intelligence can do the job better than natural intelligence.

The advantages of natural intelligence:

•   Creative.
• Natural intelligence allows people to use their experience directly. Being on artificial intelligence have to work with symbolic inputs.
• Human thought can be widely used, while the artificial intelligence is very limited.

Artificial Intelligence is the repository for answering questions asked the usual traditional linguists, The philosophy, doctors etc.. And thus can help us to become increasingly smart.

 

 

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Sub Science Discipline in Artificial Intelligence

Expert System picture

Questions first addressed by the Artificial Intelligence is the proof of the theorem and games (games). An Artificial Intelligence researcher named Samuel wrote a chess program not just to play chess, but the program is also made in order to use its experience to enhance his ability. Meanwhile, Newell, a logic theorists strive to prove the theorem, the theorem in mathematics. The more rapid development of technology resulted in the development and expansion of the scope that requires the presence of Artificial Intelligence. Characteristics of smart is required in a variety of disciplines from science and technology. Artificial Intelligence is not just crawl in a variety of other disciplines. Slices between psychology and artificial intelligence to produce an area known as cognition & psycolinguistics. Slices of electrical engineering with artificial intelligence produce all kinds of things such as image processing, control theory, pattern recognition and robotika.

Nowadays, Artificial Intelligence also contributed a substantial in the field of management. The existence of the decision support system, and Information Systems Management also did not escape the contribution Artificial Intelligence.
The existence of slices of the use of Artificial Intelligence in various disciplines such cause quite complicated to classify according to Artificial Intelligence disciplines that use it. To simplify matters, so the scope of classification based on Artificial Intelligence given output.It will be on commercial applications (though actually own Artificial Intelligence not a commercial field).
The main scope of Artificial Intelligence are:

• Expert System . Here the computer is used as a means of keep the knowledge of experts. Thus, the computer will have membership to solve the problem by copying the membership owned by experts.
• Natural Language Processing . With treatment natural language is hoped that this user can communicate with a computer with using everyday language.
• Introduction Speech (Speech Recognition). Through the introduction of reliable speech people are able to communicate with the computer using voice.
• Robotika & Sensor Systems.
• Computer Vision, trying to be able to interpret a picture or object-object appears on a computer.
• Intelligent Computer-aided instruction. Computers can be used as a tutor who can train and teach.
• Game playing. Evolving technology, emerging technologies such Beebers also aims to make computers to be intelligent so that it can simulate people work every day

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History of Artificial Intelligence

History of Artificial Intelligence (AI)

Artificial Intelligence, including a relatively young field of science. In the 1950’s scientists and researchers are beginning to figure out how to make the machine do the work as can be done by humans. Alan Turing, a British mathematician was first proposed a test to see whether or not a smart medias said. The test results are then known as the Turing Test, where the machine is masquerading as if somebody in a game that is able to provide a response to a series of questions posed. Turing thought that, if the machine can believe that he is able to communicate with others, then it can be said to someone that a machine is intelligent (like humans).

Artificial Intelligence (AI) itself is raised by a professor from Massachusetts Institute of Technology named John McCarthy in 1956 at the Dartmouth Conference which was attended by AI researchers. At the conference also defined the main goals of Artificial Intelligence, namely: to know and model the processes of human thinking and designing machines that mimic human behavior such meeting.

Some AI programs that began to be made in the year 1956-1966, among others:

1. Logic Theorist, was introduced at the Dartmouth Conference, the program can proving mathematical theorems.
2. Sad Sam, programmed by Robert K. Lindsay (1960). The program can find out simple sentences written in English and able to give an answer of facts heard in a conversation.
3. ELIZA, programmed by Joseph Weinzenbaum (1967). The program is capable of therapy to patients by providing a few questions.

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Definition Of AI (Artificial Intelligence)

What is artificial intelligence

What is AI (Artificial Intelligence)? Many ways to define Artificial Intelligence, including:

• A study that sought how to apply intelligent computer
• Studies that enable computers to solve difficult problems
• The technology that simulates human intelligence, namely how to define and try to solve problems using computers by mimicking how humans resolve quickly

Artificial Intelligence is concerned with the design of intelligence in an artificial device. The term was coined by McCarthy in 1956.

There are two ideas in the definition.
1. Intelligence
2. Artificial device

What is intelligence?

• Is it that which characterize humans? Or is there an absolute standard of judgement?
•  Accordingly there are two possibilities:
• A system with intelligence is expected to behave as intelligently as a human
•  A system with intelligence is expected to behave in the best possible manner
•  Secondly what type of behavior are we talking about?
• Are we looking at the thought process or reasoning ability of the system?
•  Or are we only interested in the final manifestations of the system in terms of its actions?

Artificial Intelligence (AI) can mimic the human learning process so that new information can be absorbed and used as a reference in the days to come. Humans can absorb new information without the need to change or influence the other information already stored. Using Artificial Intelligence programs require a more modest when compared with the standard program without Artificial Intelligence.

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