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    AI-Unit 5

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    Meera
    Expert systems are computer applications that use artificial intelligence to solve complex problems at a level of expertise comparable to that of a human expert. They are developed using a knowledge base of facts and rules, an inference engine that applies the rules to new situations to deduce solutions, and a user interface that allows users to interact with the system. Expert systems are capable of advising, diagnosing, and assisting with decision making but cannot substitute human judgment or handle problems outside their defined knowledge base.

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    AI-Unit 5

    Uploaded by

    Meera
    37 views6 pages
    Expert systems are computer applications that use artificial intelligence to solve complex problems at a level of expertise comparable to that of a human expert. They are developed using a knowledge base of facts and rules, an inference engine that applies the rules to new situations to deduce solutions, and a user interface that allows users to interact with the system. Expert systems are capable of advising, diagnosing, and assisting with decision making but cannot substitute human judgment or handle problems outside their defined knowledge base.

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    Expert systems are computer applications that use artificial intelligence to solve complex problems at a level of expertise comparable to that of a human expert. They are developed using a knowledge base of facts and rules, an inference engine that applies the rules to new situations to deduce solutions, and a user interface that allows users to interact with the system. Expert systems are capable of advising, diagnosing, and assisting with decision making but cannot substitute human judgment or handle problems outside their defined knowledge base.

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    37 views6 pages

    AI-Unit 5

    Uploaded by

    Meera
    Expert systems are computer applications that use artificial intelligence to solve complex problems at a level of expertise comparable to that of a human expert. They are developed using a knowledge base of facts and rules, an inference engine that applies the rules to new situations to deduce solutions, and a user interface that allows users to interact with the system. Expert systems are capable of advising, diagnosing, and assisting with decision making but cannot substitute human judgment or handle problems outside their defined knowledge base.

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    of 6

    Expert systems (ES) are one of the prominent research domains of AI.

    It is introduced by the
    researchers at Stanford University, Computer Science Department.

    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.
    Characteristics of Expert Systems
     High performance
     Understandable
     Reliable
     Highly responsive

    Capabilities of Expert Systems

    The expert systems are capable of −

     Advising
     Instructing and assisting human in decision making
     Demonstrating
     Deriving a solution
     Diagnosing
     Explaining
     Interpreting input
     Predicting results
     Justifying the conclusion
     Suggesting alternative options to a problem
    They are incapable of −

     Substituting human decision makers


     Possessing human capabilities
     Producing accurate output for inadequate knowledge base
     Refining their own knowledge

    Components of Expert Systems

    The components of ES include −

     Knowledge Base
     Inference Engine
     User Interface
    Let us see them one by one briefly −
    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.
    What is Knowledge?
    The data is collection of facts. The information is organized as data and facts about the task
    domain. Data, information, and past experience combined together are termed as
    knowledge.
    Components of Knowledge Base
    The knowledge base of an ES is a store of both, factual and heuristic knowledge.
     Factual Knowledge − It is the information widely accepted by the Knowledge
    Engineers and scholars in the task domain.
     Heuristic Knowledge − It is about practice, accurate judgement, one’s ability
    of evaluation, and guessing.
    Knowledge representation
    It is the method used to organize and formalize the knowledge in the knowledge base. It is in
    the form of IF-THEN-ELSE rules.

    Knowledge Acquisition
    The success of any expert system majorly depends on the quality, completeness, and
    accuracy of the information stored in the knowledge base.
    The knowledge base is formed by readings from various experts, scholars, and
    the Knowledge Engineers. The knowledge engineer is a person with the qualities of
    empathy, quick learning, and case analyzing skills.
    He acquires information from subject expert by recording, interviewing, and observing him at
    work, etc. He then categorizes and organizes the information in a meaningful way, in the
    form of IF-THEN-ELSE rules, to be used by interference machine. The knowledge engineer
    also monitors the development of the ES.
    Inference Engine
    Use of efficient procedures and rules by the Inference Engine is essential in deducting a
    correct, flawless solution.
    In case of knowledge-based ES, the Inference Engine acquires and manipulates the
    knowledge from the knowledge base to arrive at a particular solution.
    In case of rule based ES, it −
     Applies rules repeatedly to the facts, which are obtained from earlier rule
    application.
     Adds new knowledge into the knowledge base if required.
     Resolves rules conflict when multiple rules are applicable to a particular case.
    To recommend a solution, the Inference Engine uses the following strategies −

    Forward Chaining

     Backward Chaining
    Forward Chaining
    It is a strategy of an expert system to answer the question, “What can happen next?”

    Backward Chaining
    With this strategy, an expert system finds out the answer to the question, “Why this
    happened?”
    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.
    It explains how the ES has arrived at a particular recommendation. The explanation may
    appear in the following forms −

     Natural language displayed on screen.


     Verbal narrations in natural language.
     Listing of rule numbers displayed on the screen.
    The user interface makes it easy to trace the credibility of the deductions.
    Requirements of Efficient ES User Interface
     It should help users to accomplish their goals in shortest possible way.
     It should be designed to work for user’s existing or desired work practices.
     Its technology should be adaptable to user’s requirements; not the other way
    round.
     It should make efficient use of user input.

    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

    Applications of Expert System or Role of Expert Systems


    The following table shows where ES can be applied.

    Application Description

    Design Domain Camera lens design, automobile design.

    Diagnosis Systems to deduce cause of disease from observed data,


    Medical Domain
    conduction medical operations on humans.

    Comparing data continuously with observed system or with


    Monitoring Systems prescribed behavior such as leakage monitoring in long petroleum
    pipeline.

    Process Control
    Controlling a physical process based on monitoring.
    Systems
    Knowledge Domain Finding out faults in vehicles, computers.

    Detection of possible fraud, suspicious transactions, stock market


    Finance/Commerce
    trading, Airline scheduling, cargo scheduling.

    Expert System Technology

    There are several levels of ES technologies available. Expert systems technologies include −
     Expert System Development Environment − The ES development
    environment includes hardware and tools. They are −
    o Workstations, minicomputers, mainframes.
    o High level Symbolic Programming Languages such
    as LISt Programming (LISP) and PROgrammation
    en LOGique (PROLOG).
    o Large databases.
     Tools − They reduce the effort and cost involved in developing an expert
    system to large extent.
    o Powerful editors and debugging tools with multi-windows.
    o They provide rapid prototyping
    o Have Inbuilt definitions of model, knowledge representation,
    and inference design.
     Shells − A shell is nothing but an expert system without knowledge base. A
    shell provides the developers with knowledge acquisition, inference engine,
    user interface, and explanation facility. For example, few shells are given
    below −
    o Java Expert System Shell (JESS) that provides fully developed
    Java API for creating an expert system.
    o Vidwan, a shell developed at the National Centre for Software
    Technology, Mumbai in 1993. It enables knowledge encoding in
    the form of IF-THEN rules.

    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.
    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.
    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 System
     Keep the knowledge base up-to-date by regular review and update.
     Cater for new interfaces with other information systems, as those systems
    evolve.

    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.

    Examples of Expert Systems


    Following are the Expert System Examples:

     MYCIN: It was based on backward chaining and could identify various bacteria that
    could cause acute infections. It could also recommend drugs based on the patient’s
    weight. It is one of the best Expert System Example.
     DENDRAL: Expert system used for chemical analysis to predict molecular structure.
     PXDES: An Example of Expert System used to predict the degree and type of lung
    cancer
     CaDet: One of the best Expert System Example that can identify cancer at early
    stages

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