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    Rule Based System: Fundamentals and Applications
    Rule Based System: Fundamentals and Applications
    Rule Based System: Fundamentals and Applications
    Ebook145 pages1 hour

    Rule Based System: Fundamentals and Applications

    By Fouad Sabry

    ()

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    About this ebook

    What Is Rule Based System


    A rule-based system is used to store and modify knowledge in order to understand information in a manner that is helpful in the field of computer science. Applications and research in the field of artificial intelligence frequently make use of it.


    How You Will Benefit


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


    Chapter 1: Rule-Based System


    Chapter 2: Expert System


    Chapter 3: Inference Engine


    Chapter 4: Production System in Computer Science


    Chapter 5: Fuzzy Logic


    Chapter 6: Artificial Neural Network


    Chapter 7: Genetic Algorithm


    Chapter 8: Rule-Based Machine Learning


    Chapter 9: Logic Programming


    Chapter 10: Lexical Analysis


    (II) Answering the public top questions about rule based system.


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


    (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of rule based system' technologies.


    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 rule based system.

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJun 29, 2023
    Rule Based System: Fundamentals and Applications

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      Book preview

      Rule Based System - Fouad Sabry

      Chapter 1: Rule-based system

      A rule-based system is used to store and modify knowledge in order to understand information in a manner that is helpful in the field of computer science. Applications and research relating to artificial intelligence often make use of it.

      Rule-based systems are often systems that include human-crafted or curated rule sets, which is why the phrase rule-based system is used. Rule-based systems built using automated rule inference, such as rule-based machine learning, are often not considered to be part of this system type.

      One of the most well-known kinds of rule-based systems is the domain-specific expert system, which bases its inferences and decisions on predetermined sets of rules. For instance, an expert system may assist a physician in selecting the appropriate diagnosis based on a collection of symptoms, or it could suggest strategic movements for a game player to pick from.

      Rule-based systems may be used to do lexical analysis, which can then be employed in natural language processing, computer program compilation, or computer program interpretation.

      Rule-based programming works from the premise that a beginning collection of data and rules may be used to produce execution instructions. This approach is less straightforward than the one used by imperative programming languages, which state the stages of execution in the order in which they are performed.

      The standard rule-based system is comprised of the following four fundamental components::

      A knowledge basis that is comprised of a list of rules or a rule base, which is a subcategory of knowledge base.

      An inference engine, also known as a semantic reasoner, is a kind of computer program that draws conclusions or takes action depending on the way input and a rule base interact with one another. The following match-resolve-act cycle is what the interpreter uses to carry out the process of executing a production system program:

      Match: The left-hand sides of all products are compared against the information contained in working memory during this initial step of the process. As a consequence of this, one obtains a conflict set, which is a collection of different instantiations for each fulfilled production. An ordered list of items in working memory that fulfills the left-hand side of the production is an instantiation of the production.

      Resolution of the conflict occurs during the second phase, which consists of selecting one of the production instantiations from the conflict set to be carried out. In the event that none of the products meet the requirements, the interpreter will stop.

      Act: The actions of the production that were chosen in the conflict-resolution phase will be carried out in this third and final phase of the production. It's possible that these acts will alter the contents of your working memory. After the completion of this phase, the execution will proceed to the first phase.

      Memory that is active only temporarily.

      A link to the outside world that allows for the reception and transmission of input and output signals, such as a user interface or another kind of connection.

      {End Chapter 1}

      Chapter 2: 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 operate in. Whether the inference engine is being driven by the rule's antecedent (located on the left hand side), or the consequent (located on the right hand side), results in a distinct set of methods to take. During the forward chaining process,

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