Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Everand Logo

Welcome to Everand!

  • Discover millions of ebooks, audiobooks, and so much more with a free trial

    From $11.99/month after trial. Cancel anytime.

    Expert System: Fundamentals and Applications for Teaching Computers to Think like Experts
    Expert System: Fundamentals and Applications for Teaching Computers to Think like Experts
    Expert System: Fundamentals and Applications for Teaching Computers to Think like Experts
    Ebook102 pages1 hour

    Expert System: Fundamentals and Applications for Teaching Computers to Think like Experts

    By Fouad Sabry

    ()

    Read preview

    About this ebook

    What Is Expert System


    In the field of artificial intelligence, an expert system is a type of computer program that simulates the abilities of a human expert to make judgment calls. Instead of using typical procedural code, expert systems reason through bodies of knowledge, which are primarily represented as if-then rules. This is in contrast to traditional computer programs, which tackle complicated issues by writing procedural code. In the 1970s, the first expert systems were developed, and later in the 1980s, their use became more widespread. Expert systems were one of the earliest forms of artificial intelligence (AI) software that was actually successful. An expert system can be broken down into its two component subsystems, which are the knowledge base and the inference engine. The knowledge base is a collection of facts and guidelines. The inference engine takes the rules and applies them to the known data in order to derive new information. The capabilities of explanation and debugging are also sometimes included in inference engines.


    How You Will Benefit


    (I) Insights, and validations about the following topics:


    Chapter 1: Expert system


    Chapter 2: Learning classifier system


    Chapter 3: AI winter


    Chapter 4: Constraint logic programming


    Chapter 5: Constraint satisfaction


    Chapter 6: CLIPS


    Chapter 7: Mycin


    Chapter 8: Knowledge engineering


    Chapter 9: Rule-based machine learning


    Chapter 10: CADUCEUS (expert system)


    (II) Answering the public top questions about expert system.


    (III) Real world examples for the usage of expert system in many fields.


    Who This Book Is For


    Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of expert system.


    What Is Artificial Intelligence Series


    The Artificial Intelligence eBook series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field.
    The Artificial Intelligence eBook series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJul 6, 2023
    Expert System: Fundamentals and Applications for Teaching Computers to Think like Experts

    Related to Expert System

    Titles in the series (100)

    View More
    View More

    Related ebooks

    Intelligence (AI) & Semantics For You

    View More
    View More

    Related podcast episodes

    Related categories

    Reviews for Expert System

    0 ratings

    0 ratings0 reviews

    What did you think?

    Tap to rate

    Review must be at least 10 words

      Book preview

      Expert System - Fouad Sabry

      Chapter 1: Expert system

      In the field of artificial intelligence, an expert system is a kind of computer program that simulates the abilities of a human expert to make judgment calls. Instead of using traditional procedural code, expert systems reason via bodies of knowledge, which are primarily expressed as if–then rules. This is in contrast to traditional computer programs, which tackle complicated issues by writing procedural code. An expert system may be broken down into its two component subsystems, which are the knowledge base and the inference engine. The knowledge base is a collection of information and guidelines. The inference engine takes the rules and applies them to the known data in order to derive new information. The capabilities of explanation and debugging may also be included in inference engines.

      Almost immediately after the development of the first modern computers in the latter half of the 1940s and the early 1950s, The researchers began to see the enormous potential that these technologies had for the contemporary world.

      One of the first challenges was to make such machines capable of thinking like humans – in particular, making it possible for these robots to make significant judgments in the same manner as humans do.

      The medical and healthcare industry posed the enticing issue of figuring out how to make these robots capable of making medical diagnostic conclusions.

      It was common practice to refer to these systems as the first kinds of expert systems.

      However, Researchers came to the conclusion that there were major restrictions associated with the use of conventional methodologies such as flow charts, This condition in the past progressively led to the creation of expert systems, which employed methods based on knowledge. These medical expert systems were known as MYCIN, and they attracted interest from all around the world thanks to the Fifth Generation Computer Systems project in Japan and increasing research funding in Europe.

      In 1981, IBM released the very first personal computer, which ran on the PC DOS operating system. The imbalance between the high affordability of the relatively powerful chips in the PC and the much more expensive cost of processing power in the mainframes that dominated the corporate IT world at the time resulted in the creation of a new type of architecture for corporate computing. This model is known as the client–server model, and it began making regular appearances around the same time.

      The SID (Synthesis of Integral Design) software program, which was created in 1982, was the first expert system to be employed in a design capacity for a large-scale product. This was accomplished in 1982. SID, which was written in LISP, was responsible for generating 93% of the logic gates used by the VAX 9000 CPU. The program was given a set of rules that had been developed by a group of knowledgeable logic designers. The rules were significantly increased by SID, and the resulting software logic synthesis procedures were several times larger than the rules themselves. Surprisingly, the combination of these guidelines resulted in an overall design that surpassed the skills of the experts themselves, and in many instances out-performed the human equivalents. This was accomplished by creating a design that outperformed the human counterparts. Although several of the rules were in direct conflict with one another, the top-level control parameters for speed and area served as the tie-breaker. The program was quite contentious, yet it was nonetheless used despite the fact that there were financial limits on the project. After the conclusion of the VAX 9000 project, logic designers decided to scrap the program.

      In the years leading up to the middle of the 1970s, there was a shift in, In many different areas, people have a tendency to have overly high expectations for what may be achieved by expert systems.

      At the outset of these first research efforts, The researchers' goal was to create a fully automated (that is, no human), expert systems (that are wholly or partially computerized).

      People's expectations of what computers are capable of doing are usually unrealistically utopian.

      After Richard M. took office, this predicament underwent a complete transformation.

      Karp published his breakthrough paper: Reducibility among Combinatorial Problems in the early 1970s.

      Researchers have been motivated to design new sorts of methodologies in response to the limits imposed by the earlier type of expert systems. They have devised methods that are more effective, adaptable, and powerful in order to imitate the decision-making process that humans go through. Some of the strategies that have been created by researchers are based on new ways of artificial intelligence (AI), namely in machine learning and data mining strategies that include a feedback mechanism. It is common practice for recurrent neural networks to make use of such techniques. The argument presented in the section under Disadvantages is connected.

      New information can be incorporated into modern systems more quickly, making it simpler for such systems to update themselves. These kinds of systems are better able to generalize from the information that is already available and to cope with huge volumes of complicated data. The topic of this article is big data, which is related. The term intelligent systems is used to refer to these kinds of expert systems on occasion.

      One example of a knowledge-based system is something known as an expert system. The earliest commercially available systems that use a knowledge-based architecture were called expert systems. To provide a broad overview, an expert system is comprised of the following components: a user interface, as well as a knowledge base, an inference engine, an explanation facility, and a facility for the acquisition of knowledge.

      The knowledge base contains a collection of factual information about the globe. In the first expert systems, such as Mycin and Dendral, these information were mostly expressed as declarative statements about the variables. Later expert systems designed with commercial shells had a knowledge base that was more structured and employed notions from object-oriented programming. These later systems were more sophisticated. Classes, subclasses, and instances were used to represent the world, and assertions were changed to be substituted by the values of object instances. The rules were able to function properly by interrogating and verifying the values of the objects.

      An automated reasoning system that examines the present state of the knowledge-base, applies applicable rules, and then asserts new information into the knowledge-base is referred to as the inference engine. The inference engine might also include explanation capabilities, which would allow it to walk a user through the logic that led to a specific conclusion by retracing the execution of the rules that led to the assertion. This would allow the user to understand how the engine arrived at its conclusion.

      Chaining in the forward direction and chaining in the reverse direction are the two primary modes that an inference engine may

      Enjoying the preview?
      Page 1 of 1