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    Symbolic Artificial Intelligence: Fundamentals and Applications
    Symbolic Artificial Intelligence: Fundamentals and Applications
    Symbolic Artificial Intelligence: Fundamentals and Applications
    Ebook126 pages1 hourArtificial Intelligence

    Symbolic Artificial Intelligence: Fundamentals and Applications

    By Fouad Sabry

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    About this ebook

    What Is Symbolic Artificial Intelligence


    In the field of artificial intelligence, the term "symbolic artificial intelligence" refers to the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of issues, logic, and search. In other words, symbolic artificial intelligence is the name for the collection of all methods in artificial intelligence research. Symbolic AI created applications such as knowledge-based systems, symbolic mathematics, automated theorem provers, ontologies, the semantic web, and automated planning and scheduling systems. It utilized techniques such as logic programming, production rules, and semantic nets and frames. The paradigm of symbolic artificial intelligence led to the development of important ideas in the fields of search, symbolic programming languages, agents, multi-agent systems, the semantic web, as well as the benefits and drawbacks of formal knowledge and reasoning systems.


    How You Will Benefit


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


    Chapter 1: Symbolic Artificial Intelligence


    Chapter 2: Artificial Intelligence


    Chapter 3: Expert System


    Chapter 4: Knowledge Representation and Reasoning


    Chapter 5: Neats and Scruffies


    Chapter 6: Dendral


    Chapter 7: Computational Cognition


    Chapter 8: Physical Symbol System


    Chapter 9: History of Artificial Intelligence


    Chapter 10: Hybrid Intelligent System


    (II) Answering the public top questions about symbolic artificial intelligence.


    (III) Real world examples for the usage of symbolic artificial intelligence in many fields.


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

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJul 3, 2023
    Symbolic Artificial Intelligence: Fundamentals and Applications

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      Book preview

      Symbolic Artificial Intelligence - Fouad Sabry

      Chapter 1: Symbolic artificial intelligence

      In the field of artificial intelligence, the term symbolic artificial intelligence refers to the collection of all methods in artificial intelligence research that are based on high-level symbolic (human-readable) representations of problems, logic, and search. Specifically, symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on the Symbolic artificial intelligence produced applications such as expert systems using methods such as logic programming, production rules, semantic nets, and frames.

      In his book published in 1985 titled Artificial Intelligence: The Very Idea, John Haugeland investigated the philosophical implications of artificial intelligence research. In this book, Haugeland gave symbolic AI the term GOFAI, which stands for Good Old-Fashioned Artificial Intelligence. The equivalent word used in robotics is GOFR (Good Old-Fashioned Robotics). Despite this, the symbolic method would ultimately be discarded in favor of the subsymbolic alternatives, mostly due to the limitations imposed by technological capabilities.

      In the 1960s and 1970s, researchers were certain that symbolic techniques would ultimately be successful in developing a computer with artificial general intelligence. They saw this as the ultimate objective of their discipline. It was superseded by highly mathematical artificial intelligence (AI) that relies heavily on statistical analysis and is primarily geared at solving certain issues and achieving particular objectives. The exploratory subfield known as artificial general intelligence is where research on general intelligence is being conducted at the moment.

      In 1955 and 1956, Allen Newell, Herbert Simon, and Cliff Shaw developed the Logic theorist, which is considered to be the first ever symbolic artificial intelligence program.

      In the middle of the 1960s, Newell and Simon put out a concept that they called the physical symbol systems hypothesis, which aptly summarized the symbolic approach:

      A physical symbol system has the essential and enough means for widespread intelligent action.

      Symbolic techniques saw a significant deal of success in the 1960s when it came to the simulation of intelligent behavior in relatively modest demonstration programs. In the 1960s, the majority of artificial intelligence research was conducted at three universities: Carnegie Mellon University, Stanford University, and (later) the University of Edinburgh. Every one of them created their own unique method of investigation. The earlier methods, which were either based on cybernetics or artificial neural networks, were either discarded or relegated to the background.

      Their study on human problem-solving abilities and attempts to codify them established the groundwork for the area of artificial intelligence, as well as cognitive science, operations research, and management science. Herbert Simon and Allen Newell are credited as being the pioneers of the discipline. Their research team made use of the findings of psychological investigations in order to construct computer programs that emulated the strategies that individuals utilized in order to solve difficulties.

      John McCarthy held the opinion that, in contrast to Simon and Newell, machines did not require the ability to simulate human thought. Instead, he believed that machines should work toward discovering the essence of abstract reasoning and problem-solving, regardless of whether or not people used the same algorithms. His research group at Stanford, known as SAIL, concentrated on the use of formal logic to address a diverse range of issues, including as the representation of knowledge, the process of planning, and the acquisition of new information. In addition, logic was the focal point of research conducted at the University of Edinburgh and elsewhere in Europe, which ultimately resulted in the creation of the programming language Prolog as well as the discipline of logic programming.

      Scientists working at MIT (such as Marvin Minsky and Seymour Papert)

      Around the year 1970, the availability of computers with huge memory prompted academics from all three schools of thought to begin applying their own bodies of knowledge to AI problems. The awareness that even relatively simple AI applications will need tremendous volumes of information was a driving force behind the knowledge revolution.

      Realization of a symbolic artificial intelligence system is possible in the form of a microworld, such as blocks world. Within the memory of the computer is where you'll find a representation of the actual world called the microworld. It is characterized by lists that include symbols, and the intelligent agent makes use of operators in order to transition the system into a new state. The program that searches across the state space for the next action of the intelligent agent is the production system. The sensory experience provides the foundation for the symbols that are used to portray the world. Heuristics, as opposed to neural networks, are employed by the whole system, which means that domain-specific information is used to optimize the state space search.

      This knowledge revolution resulted in the creation and implementation of expert systems, the first really effective kind of artificial intelligence software. Edward Feigenbaum is credited with the invention of expert systems. The knowledge base, which holds facts and rules that show artificial intelligence, is an essential element of the system architecture for all expert systems. These make use of a rules-based production network. The connection between two symbols in a production rule is very much like that of an If-Then expression. The rules are processed by the expert system, which then uses symbols that are understandable by humans to decide what deductions to make and what extra information it need, also known as what questions to ask. Because symbolic AI operates according to predetermined rules and has access to ever-increasing processing power, it is able to handle more difficult tasks. In 1996, as a result of this, IBM's Deep Blue was able to defeat Garry Kasparov, who was the reigning world chess champion at the time, in a game of chess with the assistance of symbolic AI.

      Hubert Dreyfus, a French philosopher, is credited as being one of the first critics of symbolic AI. In a string of articles and books that began in the 1960s, Dreyfus directed his criticism at the intellectual underpinnings of the science of artificial intelligence (AI). He forecast that it would only be applicable to simple situations, and he believed that it would not be feasible to develop more complicated systems or scale the notion up such that it could be implemented in practical software.

      The similar reasoning was presented in the Lighthill study, which was the impetus for the beginning of the AI Winter in the middle of the 1970s.

      In the 1980s, opponents of the symbolic approach included roboticists like Rodney Brooks, who aimed to produce autonomous robots without symbolic representation (or with only minimal representation), as well as researchers in computational intelligence who used methods like neural networks and optimization to solve problems in machine learning and control engineering.

      When the data being entered is definitive and may be classified as certain, symbols may be used. However, when there is a possibility of error, such as in the process of making predictions, the representation is carried out by means of artificial neural networks.

      In recent years, there have been concerted attempts made in the direction of combining the symbolic and connectionist AI methodologies under the general heading of neural-symbolic computing. The successful building of rich computational cognitive models requires the combination of solid symbolic thinking with efficient (machine learning) models, as suggested by Valiant and many others. This is a requirement for the effective construction of rich computational cognitive models.

      {End Chapter 1}

      Chapter 2: Artificial intelligence

      As contrast to the natural intelligence exhibited by animals, including humans, artificial intelligence (AI) refers to the intelligence demonstrated by robots. Research in artificial intelligence (AI) has been described as the area of study of intelligent agents, which refers to any system that senses its surroundings and performs actions that optimize its possibility

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