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Present by: Abdul Ahad A Expert System ES Prof. Dr. Aybars UĞUR .
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Expert System - Artificial intelligence

Mar 20, 2017

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Page 1: Expert System - Artificial intelligence

Present by: Abdul Ahad Abro

Expert System ES Prof. Dr. Aybars UĞUR

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Expert SystemHistory of Expert SystemsEarly Expert SystemsExpert Systems TypesCharacteristics of Expert SystemsCapabilities of Expert SystemsComponents of Expert Systems

Knowledge BaseInterface EngineUser Interface

Expert Systems LimitationsApplications of Expert SystemDevelopment of Expert Systems: General StepsBenefits of Expert SystemsDisadvantage

Expert System

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Expert systems (ES) are one of the prominent research domains of AI. It is introduced by the researchers at Stanford University, Computer Science Department.

ES

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What are Expert Systems? The expert systems are the computer applications developed to solve complex problems in a particular domain, at the level of extra-ordinary human intelligence and expertise.

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What are Expert Systems? (2) An expert system is a computer system that emulates, or acts in all respects, with the decision-making capabilities of a human expert.

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History of Expert Systems Expert systems were introduced by the Stanford Heuristic Programming Project led by

Feigenbaum, who is sometimes referred to as the "father of expert systems". The Stanford

researchers tried to identify domains where expertise was highly valued and complex, such as

diagnosing infectious diseases (Mycin) and identifying unknown organic molecules (Dendral).

In addition to Feigenbaum key early contributors were Edward Shortliffe, Bruce Buchanan, and

Randall Davis. Expert systems were among the first truly successful forms of AI software.

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History of Expert Systems (2) In the 1990s and beyond the term "expert system" and the idea of a standalone AI system

mostly dropped from the IT lexicon. There are two interpretations of this. One is that "expert

systems failed": the IT world moved on because expert systems didn't deliver on their over

hyped promise. The fall of expert systems was so spectacular that even AI legend Rishi Sharma

admitted to cheating in his college project regarding expert systems, because he didn't consider

the project worthwhile

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History of Expert Systems (3) The other is the mirror opposite, that expert systems were simply victims of their success. As IT

professionals grasped concepts such as rule engines such tools migrated from standalone tools

for the development of special purpose "expert" systems to one more tool that an IT

professional has at their disposal. Many of the leading major business application suite vendors

such as SAP, Siebel, and Oracle integrated expert system capabilities into their suite of products

as a way of specifying business logic.

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Early Expert Systems DENDRAL – used in chemical mass spectroscopy to identify chemical constituents

MYCIN – medical diagnosis of illness

DIPMETER – geological data analysis for oil

PROSPECTOR – geological data analysis for minerals

XCON/R1 – configuring computer systems

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Expert Systems Types Expert Systems Versus Knowledge-based Systems Rule-based Expert Systems Frame-based Systems Hybrid Systems Model-based Systems Ready-made (Off-the-Shelf) Systems Real-time Expert Systems

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Preferred Languages in Developing ES

LISP - list processing language (MIT). John McCarthy, 1950s.

In the U.S., LISP was the language of choice.

Powerful in its symbolic processing capability, but difficult to master. PROLOG - logical programming language. Marseille, France, 1970. Researchers in the U.K. and Japan adopted PROLOG for developing intelligent programs. It was also the language chosen in Japan for the Fifth Generation effort. Based in a formal well-understood logic, PROLOG offers a language to develop exact deductive programs.

Like LISP, PROLOG required a disciplined student to master it, thus limiting the number of competent programmers.

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Structure of a Rule-Based Expert System

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Characteristics of Expert Systems

High performance

Understandable

Reliable

Highly responsive

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Capabilities of Expert Systems The expert systems are capable of − They are incapable of -

Advising Substituting human decision makers

Instructing and assisting human in decision making Possessing human capabilities

Demonstrating Producing accurate output for inadequate knowledge base

Deriving a solution Refining their own knowledge

Diagnosing

Explaining

Interpreting input

Predicting results

Justifying the conclusion

Suggesting alternative options to a problem

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Components of Expert Systems The components of ES include − Knowledge Base Interface Engine User Interface

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Knowledge Base It contains domain-specific and high-quality knowledge. Knowledge is required to exhibit intelligence. The success of any ES majorly depends upon the collection of highly accurate and precise knowledge.

User Interface User interface provides interaction between user of the ES and the ES itself. It is generally Natural Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES need not be necessarily an expert in Artificial Intelligence.

Interface Engine Use of efficient procedures and rules by the Interface Engine is essential in deducting a correct, flawless solution.In case of knowledge-based ES, the Interface Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.

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Expert Systems Limitations No technology can offer easy and complete solution. Large systems are costly, require significant development time, and computer resources. ESs have their limitations which include −

Limitations of the technology

Difficult knowledge acquisition

ES are difficult to maintain

High development costs

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Applications of Expert System .

Application Description

Design Domain Camera lens design, automobile design.

Medical Domain Diagnosis Systems to deduce cause of disease from observed data, conduction medical operations on humans.

Monitoring Systems Comparing data continuously with observed system or with prescribed behavior such as leakage monitoring in long petroleum pipeline.

Process Control Systems Controlling a physical process based on monitoring.

Knowledge Domain Finding out faults in vehicles, computers.

Finance/Commerce Detection of possible fraud, suspicious transactions, stock market trading, Airline scheduling, cargo scheduling.

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Development of Expert Systems: General Steps

The process of ES development is iterative. Steps in developing the ES include

Identify Problem Domain

The problem must be suitable for an expert system to solve it.

Find the experts in task domain for the ES project.

Establish cost-effectiveness of the system.

Design the System

Identify the ES Technology

Know and establish the degree of integration with the other systems and databases.

Realize how the concepts can represent the domain knowledge best.

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Continue: Develop the Prototype

From Knowledge Base: The knowledge engineer works to −

Acquire domain knowledge from the expert.

Represent it in the form of If-THEN-ELSE rules.

Test and Refine the Prototype

The knowledge engineer uses sample cases to test the prototype for any deficiencies in performance.

End users test the prototypes of the ES.

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Continue: Develop and Complete the ES

Test and ensure the interaction of the ES with all elements of its environment, including end users, databases, and other information systems.

Document the ES project well.

Train the user to use ES.

Maintain the ES

Keep the knowledge base up-to-date by regular review and update.

Cater for new interfaces with other information systems, as those systems evolve.

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Benefits of Expert Systems Availability − They are easily available due to mass production of software.

Less Production Cost − Production cost is reasonable. This makes them affordable.

Speed − They offer great speed. They reduce the amount of work an individual puts in.

Less Error Rate − Error rate is low as compared to human errors.

Reducing Risk − They can work in the environment dangerous to humans.

Steady response − They work steadily without getting motional, tensed or fatigued.

Performance, Multiple expertise, Intelligent database

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Disadvantage

Cost to buy and set up the system

Lacks the human touch

Expert systems have no common sense.

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Thank you …