Understanding Expert Systems in Computer Engineering

 
Expert Systems
Introduction to Expert System
State Institute of Engineering & Technology
Nilokheri, 132117
Department: Computer Engineer
ing
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Expert System
 
An Expert System is an interactive computer-based decision tool that
uses both facts and heuristics to solve different decision making problem
based on knowledge acquired from an expert.
Inference engine+ knowledge =  Expert System.
First Expert System called DENDRAL, was developed in the early 70’s
at Stanford University.
An expert system is a system that employs human knowledge captured
in a computer to solve problems that ordinarily require human
expertise.(Turban).
 
 
 
 
A computer program that emulates the behaviour of human experts
who are solving real-world problems associated with a particular
domain of knowledge. (Pigford & Braur)
Expert Systems are designed to solve real problems in a particular
domain that normally require a human expert. It can solve many
types of problems.
Diagnostic applications.
Play chess.
Monitor real time systems.
Underwrite insurance policies.
 
 
 
Fig.1.1 Block diagram of Expert
System.
 
Components of Expert System
 
An expert system mainly consists of three components:
User Interface
Inference Engine
Knowledge Base
User Interface
With the help of a user interface, the expert system interacts with the user, takes
queries as an input in a readable format, and passes it to the inference engine. After
getting the response from the inference engine, it displays the output to the user. In
other words, it is an interface that helps a non-expert user to communicate with the
expert system to find a solution.
 
Inference Engine (Rules of Engine)
The inference engine is known as the brain of the expert
system as it is the main processing unit of the system. It applies
inference rules to the knowledge base to derive a conclusion or
deduce new information. It helps in deriving an error-free
solution of queries asked by the user.
With the help of an inference engine, the system extracts the
knowledge from the knowledge base.
Knowledge Base
The knowledgebase is a type of storage that stores knowledge
acquired from the different experts of the particular domain. It
is considered as big storage of knowledge. The more the
knowledge base, the more precise will be the Expert System.
 
Representation and organization of knowledge
 
1. Simple relational knowledge:
 It is the simplest way of storing facts which uses the relational method, and
each fact about a set of the object is set out systematically in columns.
This approach of knowledge representation is famous in database systems
where the relationship between different entities is represented.
2. Inheritable knowledge:
 In the inheritable knowledge approach, all data must be stored into a
hierarchy of classes.
All classes should be arranged in a generalized form or a hierarchal manner.
In this approach, we apply inheritance property.
 Elements inherit values from other members of a class.
This approach contains inheritable knowledge which shows a relation
between instance and class, and it is called instance relation.
 
3. Inferential knowledge:
 Inferential knowledge approach represents knowledge in the form of
formal logics.
This approach can be used to derive more facts.
 It guaranteed correctness.
Example: Let's suppose there are two statements: 1. Marcus is a man 2.
All men are mortal Then it can represent as; man(Marcus) 
x = man (x)
----------> mortal (x)s.
4. Procedural knowledge:
 Procedural knowledge approach uses small programs and codes which
describes how to do specific things, and how to proceed.
 In this approach, one important rule is used which is If-Then rule.
 In this knowledge, we can use various coding languages such as LISP
language and Prolog language.
We can easily represent heuristic or domain-specific knowledge using
this approach.
 
Characteristics of Expert System
 
Expert System is capable of handling challenging decision problems and delivering
solutions.
Expert System uses knowledge rather than data for solution. Much of the
knowledge is heuristic based rather than algorithm.
Expert system has the capability to explain how the decision was made.
An Expert  System must be skillful. It should apply its knowledge to produce
solution both effectively and efficiently using shortcuts  or tricks.
 
 
Expert System benefits..
 
Increased output and productivity.
Reduced downtime.
They can be used for risky places where the human presence is not safe.
 Error possibilities are less if the KB contains correct knowledge.
The performance of these systems remains steady as it is not affected by emotions,
tension, or fatigue.
Flexibility.
Easy to develop and modify
Elimination of Expensive Equipment.
Decreased decision making time.
Increased process and product quality.
 
Expert System limitations.
 
The response of the expert system may get wrong if the knowledge base contains
the wrong information.
 Like a human being, it cannot produce a creative output for different scenarios.
 Its maintenance and development costs are very high.
Knowledge acquisition for designing is much difficult.
 For each domain, we require a specific ES, which is one of the big limitations.
 It cannot learn from itself and hence requires manual updates.
 
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
 
Application of Expert System.
 
 
Credit granting
Information management and retrieval
AI and expert systems embedded in products
Plant layout
Hospitals and medical facilities
Help desks and assistance
Employee performance evaluation
Loan analysis
Virus detection
Repair and maintenance
Shipping
Marketing
Warehouse optimization
 
Knowledge-Engineering
 
Knowledge engineering is the technology behind the creation
of expert system to assist with issues related to their
programmed field of knowledge. Expert systems involve a
large, expandable knowledge base integrated with a rules
engine that specifies how to apply information to each
particular situation
.
A knowledge engineering system looks at the structure of a
task done or decision made by a human. It studies how the
conclusion is reached and resolves the issue or question using a
library of problem-solving methods and a body of collateral
knowledge. Collateral knowledge is information that is not
central to the given issue but is still needed to make judgments.
 
Knowledge engineering generally involves
these five steps:
 
Knowledge is gathered from various sources, such as text,
human experts, big
 
data repositories and sensors.
Knowledge is verified using test cases that human experts run
to ensure they're correct.
Knowledge is organized, encoded and provided in a knowledge
base.
Software makes inferences based on the organized and
encoded knowledge.
An explanation is devised to explain the basis for a certain
conclusion.
 
The most important step in the process is to ensure that the
knowledge base is accurate and timely. Another important
aspect of the knowledge engineering process is to have a
human agent who ensures the system is doing its job. The
engineer will develop rules for the system so it functions like a
human and reaches the same conclusions as a human expert.
 
Why is knowledge engineering important?
 
Speeds decision-making. 
The AI capabilities of these systems
speeds up information processing and decision-making. A
knowledge engineering system can identify a task and work toward
a logical conclusion using its expertise.
Handles large data sets.
 As organizations deal with increasingly
large quantities of data, they need a way to efficiently process it
and make decisions, which knowledge engineering provides.
Develops expert systems.
 Knowledge engineering results in the
expert systems used in many industries, including medicine,
engineering and finance. For example, financial expert
systems provide advice on investment decisions, portfolio
management and risk assessment.
Creates decision-support systems.
 Knowledge engineering
provides the information and tools people need to make better
decisions. 
Decision-support systems
 are used in various fields,
including healthcare, manufacturing and retail.
 
System – building aids
 
The system-building aids consist of programs which help acquire
and represent the domain expert’s
knowledge and programs which help design the expert system
under construction. These programs address
very difficult tasks; many are research tools just beginning to
evolve into practical and useful aids, although a
few are offered as full-blown commercial systems.
Compared with programming and knowledge engineering
languages, relatively few system-building aids have been
developed. Those which exist fall into two major categories;
design aids and knowledge acquisition aids.
The AGE system exemplifies design aids, while TEIRSIAS,
MOLE and SALT exemplify knowledge acquisition, TIMM
system construction and SEEK knowledge refinement aids.
 
AGE: This software tool helps the knowledge engineer design
and build an expert system. AGE provides the user with a set
of components which, like building blocks, can be assembled
to form portions of an expert system. Each component, a
collection of INTERLISP functions, supports an expert system
framework, such as forward chaining, backward chaining, or a
blackboard architecture.
MOLE: This is a knowledge acquisition system for heuristic
classification problem, such as diagnosing diseases. In
particular, it is used in conjunction with the cover-and-
differentiate problem solving method.
 
Stages in the development of Expert
System
 
The following points highlight the five main stages to develop
an expert system. The stages are:
1. Identification
2. Conceptualization
3. Formalization (Designing)
4. Implementation
5. Testing (Validation, Verification and Maintenance)
.
 
Identification:
 
Before we can begin to develop an expert system, it is
important to describe, with as much precision as possible, the
problem which the system is intended to solve. It is not enough
simply to feel that an expert system would be helpful in a
certain situation; we must determine the exact nature of the
problem and state the precise goals which indicate exactly how
the expert system is expected to contribute to the solution.
 
Conceptualization:
 
In the conceptualization stage, the knowledge engineer
frequently creates a diagram of the problem to depict
graphically the relationships between the objects and processes
in the problem domain. It is often helpful at this stage to divide
the problem into a series of sub-problems and to diagram both
the relationships among the pieces of each sub-problem and the
relationships among the various sub-problems.
 
Formalization (Designing):
 
During the formalization stage, the problem is connected to its
proposed solution, an expert system is supplied by analyzing
the relationships depicted in the conceptualization stage. The
knowledge engineer begins to select the techniques which are
appropriate for developing this particular expert system
.
 
Implementation:
 
During the implementation stage the formalized concepts are
programmed into the computer which has been chosen for
system development, using the predetermined techniques and
tools to implement a ‘first-pass’ (prototype) of the expert
system.
 
Testing (Validation, Verification and
Maintenance):
 
The chance of prototype expert system executing flawlessly the
first time it is tested are so slim as to be virtually non-existent.
A knowledge engineer does not expect the testing process to
verify that the system has been constructed entirely correctly.
Rather, testing provides an opportunity to identify the
weaknesses in the structure and implementation of the system
and to make the appropriate corrections.
 
PROSPECTOR
 
PROSPECTOR is an expert system which was designed for
decision making problems in mineral exploration. It uses
a structure called an inference network to represent
the data base. PROSPECTOR was written in 1978 by
Richard O. Duda (1979), then of SRI International.
Its rules look like:
     If:
      Magnetite or pyrite in disseminated or vein let from is
present.
     Then:
      (2, – 4) there is favorable mineralization and texture
for the prophylactic stage.
 
The rules describe in previous slide contain two
confidence estimates. The first indicates the extent to
which the presence of the evidence described in the
condition part of this rule suggests the validity of the
rule’s conclusion. Number 2, in the rule shown above
indicates that the presence of the evidence is mildly
encouraging.
It aids geologists in evaluating the favorability of an
exploration site or region for occurrences of ore
deposits of particular types.
 
Some of the important features and
contributions of PROSPECTOR are the
following:
 
1. The system represents fuzzy input on a range from -5 =
certainly false to +5 = certainly true and produces conclusion
with associated uncertainty factors.
2.
 
The system’s expertise is based on hand-crafted knowledge
of twelve major prospect-scale models and 23 smaller regional
scale models.
 
PROSPECTOR has 3 modes of operations:
Interactive consultation; Batch processing;
Compiled execution.
 
For the Interactive consultation mode, there are 2 phases:
1.The antecedent mode. 2.The consequent mode. A typical
consultation session begins with the user volunteering
information in the form of simple assertions. A typical
consequence of initial volunteering is that a few exact and a
number of partial matches are made between volunteered
evidence and the nodes in the inference network, and some
changes occur in the probabilities of the top-level hypotheses.
Second, once a top-level hypothesis H has been chosen, the
program enters the consequent mode. It searches the inference
network below H to determine what question to ask the user to
help resolve the issue.
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Expert systems are interactive computer-based decision tools that utilize facts and heuristics to solve various problems based on knowledge acquired from experts. This system consists of three main components: User Interface, Inference Engine, and Knowledge Base. The User Interface facilitates communication between the system and user, while the Inference Engine processes information and the Knowledge Base stores acquired knowledge. Expert systems are designed to solve real-world problems in specific domains, emulating human expert behavior to provide solutions.


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  1. Expert Systems Introduction to Expert System State Institute of Engineering & Technology Nilokheri, 132117 Department: Computer Engineering

  2. Expert System An Expert System is an interactive computer-based decision tool that uses both facts and heuristics to solve different decision making problem based on knowledge acquired from an expert. Inference engine+ knowledge = Expert System. First Expert System called DENDRAL, was developed in the early 70 s at Stanford University. An expert system is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise.(Turban).

  3. A computer program that emulates the behaviour of human experts who are solving real-world problems associated with a particular domain of knowledge. (Pigford & Braur) Expert Systems are designed to solve real problems in a particular domain that normally require a human expert. It can solve many types of problems. Diagnostic applications. Play chess. Monitor real time systems. Underwrite insurance policies.

  4. Fig.1.1 Block diagram of Expert System.

  5. Components of Expert System An expert system mainly consists of three components: User Interface Inference Engine Knowledge Base User Interface With the help of a user interface, the expert system interacts with the user, takes queries as an input in a readable format, and passes it to the inference engine. After getting the response from the inference engine, it displays the output to the user. In other words, it is an interface that helps a non-expert user to communicate with the expert system to find a solution.

  6. Inference Engine (Rules of Engine) The inference engine is known as the brain of the expert system as it is the main processing unit of the system. It applies inference rules to the knowledge base to derive a conclusion or deduce new information. It helps in deriving an error-free solution of queries asked by the user. With the help of an inference engine, the system extracts the knowledge from the knowledge base. Knowledge Base The knowledgebase is a type of storage that stores knowledge acquired from the different experts of the particular domain. It is considered as big storage of knowledge. The more the knowledge base, the more precise will be the Expert System.

  7. Representation and organization of knowledge 1. Simple relational knowledge: It is the simplest way of storing facts which uses the relational method, and each fact about a set of the object is set out systematically in columns. This approach of knowledge representation is famous in database systems where the relationship between different entities is represented. 2. Inheritable knowledge: In the inheritable knowledge approach, all data must be stored into a hierarchy of classes. All classes should be arranged in a generalized form or a hierarchal manner. In this approach, we apply inheritance property. Elements inherit values from other members of a class. This approach contains inheritable knowledge which shows a relation between instance and class, and it is called instance relation.

  8. 3. Inferential knowledge: Inferential knowledge approach represents knowledge in the form of formal logics. This approach can be used to derive more facts. It guaranteed correctness. Example: Let's suppose there are two statements: 1. Marcus is a man 2. All men are mortal Then it can represent as; man(Marcus) x = man (x) ----------> mortal (x)s. 4. Procedural knowledge: Procedural knowledge approach uses small programs and codes which describes how to do specific things, and how to proceed. In this approach, one important rule is used which is If-Then rule. In this knowledge, we can use various coding languages such as LISP language and Prolog language. We can easily represent heuristic or domain-specific knowledge using this approach.

  9. Characteristics of Expert System Expert System is capable of handling challenging decision problems and delivering solutions. Expert System uses knowledge rather than data for solution. Much of the knowledge is heuristic based rather than algorithm. Expert system has the capability to explain how the decision was made. An Expert System must be skillful. It should apply its knowledge to produce solution both effectively and efficiently using shortcuts or tricks.

  10. Expert System benefits.. Increased output and productivity. Reduced downtime. They can be used for risky places where the human presence is not safe. Error possibilities are less if the KB contains correct knowledge. The performance of these systems remains steady as it is not affected by emotions, tension, or fatigue. Flexibility. Easy to develop and modify Elimination of Expensive Equipment. Decreased decision making time. Increased process and product quality.

  11. Expert System limitations. The response of the expert system may get wrong if the knowledge base contains the wrong information. Like a human being, it cannot produce a creative output for different scenarios. Its maintenance and development costs are very high. Knowledge acquisition for designing is much difficult. For each domain, we require a specific ES, which is one of the big limitations. It cannot learn from itself and hence requires manual updates.

  12. 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

  13. Application of Expert System. Credit granting Information management and retrieval AI and expert systems embedded in products Plant layout Hospitals and medical facilities Help desks and assistance Employee performance evaluation Loan analysis Virus detection Repair and maintenance Shipping Marketing Warehouse optimization

  14. Knowledge-Engineering Knowledge engineering is the technology behind the creation of expert system to assist with issues related to their programmed field of knowledge. Expert systems involve a large, expandable knowledge base integrated with a rules engine that specifies how to apply information to each particular situation. A knowledge engineering system looks at the structure of a task done or decision made by a human. It studies how the conclusion is reached and resolves the issue or question using a library of problem-solving methods and a body of collateral knowledge. Collateral knowledge is information that is not central to the given issue but is still needed to make judgments.

  15. Knowledge engineering generally involves these five steps: Knowledge is gathered from various sources, such as text, human experts, big data repositories and sensors. Knowledge is verified using test cases that human experts run to ensure they're correct. Knowledge is organized, encoded and provided in a knowledge base. Software makes inferences based on the organized and encoded knowledge. An explanation is devised to explain the basis for a certain conclusion.

  16. The most important step in the process is to ensure that the knowledge base is accurate and timely. Another important aspect of the knowledge engineering process is to have a human agent who ensures the system is doing its job. The engineer will develop rules for the system so it functions like a human and reaches the same conclusions as a human expert.

  17. Why is knowledge engineering important? Speeds decision-making. The AI capabilities of these systems speeds up information processing and decision-making. A knowledge engineering system can identify a task and work toward a logical conclusion using its expertise. Handles large data sets. As organizations deal with increasingly large quantities of data, they need a way to efficiently process it and make decisions, which knowledge engineering provides. Develops expert systems. Knowledge engineering results in the expert systems used in many industries, including medicine, engineering and finance. For example, systems provide advice on investment decisions, portfolio management and risk assessment. Creates decision-support systems. provides the information and tools people need to make better decisions. Decision-support systems are used in various fields, including healthcare, manufacturing and retail. financial expert Knowledge engineering

  18. System building aids The system-building aids consist of programs which help acquire and represent the domain expert s knowledge and programs which help design the expert system under construction. These programs address very difficult tasks; many are research tools just beginning to evolve into practical and useful aids, although a few are offered as full-blown commercial systems. Compared with programming and knowledge engineering languages, relatively few system-building aids have been developed. Those which exist fall into two major categories; design aids and knowledge acquisition aids. The AGE system exemplifies design aids, while TEIRSIAS, MOLE and SALT exemplify knowledge acquisition, TIMM system construction and SEEK knowledge refinement aids.

  19. AGE: This software tool helps the knowledge engineer design and build an expert system. AGE provides the user with a set of components which, like building blocks, can be assembled to form portions of an expert system. Each component, a collection of INTERLISP functions, supports an expert system framework, such as forward chaining, backward chaining, or a blackboard architecture. MOLE: This is a knowledge acquisition system for heuristic classification problem, such as diagnosing diseases. In particular, it is used in conjunction with the cover-and- differentiate problem solving method.

  20. Stages in the development of Expert System The following points highlight the five main stages to develop an expert system. The stages are: 1. Identification 2. Conceptualization 3. Formalization (Designing) 4. Implementation 5. Testing (Validation, Verification and Maintenance).

  21. Identification: Before we can begin to develop an expert system, it is important to describe, with as much precision as possible, the problem which the system is intended to solve. It is not enough simply to feel that an expert system would be helpful in a certain situation; we must determine the exact nature of the problem and state the precise goals which indicate exactly how the expert system is expected to contribute to the solution.

  22. Conceptualization: In the conceptualization stage, the knowledge engineer frequently creates a diagram of the problem to depict graphically the relationships between the objects and processes in the problem domain. It is often helpful at this stage to divide the problem into a series of sub-problems and to diagram both the relationships among the pieces of each sub-problem and the relationships among the various sub-problems.

  23. Formalization (Designing): During the formalization stage, the problem is connected to its proposed solution, an expert system is supplied by analyzing the relationships depicted in the conceptualization stage. The knowledge engineer begins to select the techniques which are appropriate for developing this particular expert system.

  24. Implementation: During the implementation stage the formalized concepts are programmed into the computer which has been chosen for system development, using the predetermined techniques and tools to implement a first-pass (prototype) of the expert system.

  25. Testing (Validation, Verification and Maintenance): The chance of prototype expert system executing flawlessly the first time it is tested are so slim as to be virtually non-existent. A knowledge engineer does not expect the testing process to verify that the system has been constructed entirely correctly. Rather, testing provides an opportunity to identify the weaknesses in the structure and implementation of the system and to make the appropriate corrections.

  26. PROSPECTOR PROSPECTOR is an expert system which was designed for decision making problems in mineral exploration. It uses a structure called an inference network to represent the data base. PROSPECTOR was written in 1978 by Richard O. Duda (1979), then of SRI International. Its rules look like: If: Magnetite or pyrite in disseminated or vein let from is present. Then: (2, 4) there is favorable mineralization and texture for the prophylactic stage.

  27. The rules describe in previous slide contain two confidence estimates. The first indicates the extent to which the presence of the evidence described in the condition part of this rule suggests the validity of the rule s conclusion. Number 2, in the rule shown above indicates that the presence of the evidence is mildly encouraging. It aids geologists in evaluating the favorability of an exploration site or region for occurrences of ore deposits of particular types.

  28. Some of the important features and contributions of PROSPECTOR are the following: 1. The system represents fuzzy input on a range from -5 = certainly false to +5 = certainly true and produces conclusion with associated uncertainty factors. 2. The system s expertise is based on hand-crafted knowledge of twelve major prospect-scale models and 23 smaller regional scale models.

  29. PROSPECTOR has 3 modes of operations: Interactive consultation; Batch processing; Compiled execution. For the Interactive consultation mode, there are 2 phases: 1.The antecedent mode. 2.The consequent mode. A typical consultation session begins with the user volunteering information in the form of simple assertions. A typical consequence of initial volunteering is that a few exact and a number of partial matches are made between volunteered evidence and the nodes in the inference network, and some changes occur in the probabilities of the top-level hypotheses. Second, once a top-level hypothesis H has been chosen, the program enters the consequent mode. It searches the inference network below H to determine what question to ask the user to help resolve the issue.

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