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Discrete Mathematics Foundations for Computer Science and Symbolic Artificial Intelligence

Build a solid mathematical foundation for computer science and symbolic artificial intelligence. Explore sets, relations, combinatorial probability, arithmetic structures, information encoding, and numeration systems through a rigorous yet accessible approach that connects mathematical theory to real-world computational applications.

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About

About

About the Book

Mathematics lies at the heart of computer science and symbolic artificial intelligence. Behind every algorithm, data structure, inference engine, expert system, or knowledge representation model lies a set of mathematical principles that make formal reasoning, computation, and intelligent decision-making possible.

Discrete Mathematics Foundations for Computer Science and Symbolic Artificial Intelligence provides a solid yet accessible introduction to the mathematical concepts that underpin modern computing and symbolic AI. Designed for students, educators, software engineers, artificial intelligence practitioners, and lifelong learners, the book bridges the gap between abstract mathematical theory and its practical role in computational systems.

The volume begins by establishing the conceptual relationship between computer science, symbolic artificial intelligence, and discrete mathematics. It then develops the essential foundations of set theory, relations, functions, cardinality, combinatorial probability, arithmetic structures, information encoding, numeration systems, and binary computation. Throughout the book, each concept is systematically connected to its applications in computer science and symbolic artificial intelligence, enabling readers to understand not only the mathematics itself but also its significance in real-world computational contexts.

Unlike traditional mathematics textbooks that often emphasize theory in isolation, this work adopts an integrated perspective in which mathematical structures are continuously related to algorithmic thinking, knowledge representation, reasoning systems, symbolic learning, uncertainty management, information processing, and intelligent decision support.

The book contains numerous examples, illustrations, exercises, and fully developed solutions designed to reinforce understanding and promote active learning. Whether used as a university textbook, a self-study resource, or a professional reference, it provides a solid foundation for further study in mathematical logic, artificial intelligence, data structures, algorithms, software engineering, and advanced computing disciplines.

As the first volume of The Mathematics for Computer Science and Symbolic Artificial Intelligence Series, this book establishes the mathematical foundations upon which subsequent volumes develop logical reasoning, symbolic inference, knowledge representation, graphs, trees, and intelligent computational structures.

If you seek to understand not only how computer systems operate, but also the mathematical principles that make computation, reasoning, and symbolic intelligence possible, this book provides a comprehensive and structured pathway toward that goal.

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Author

About the Author

Aimé Mbobi, Ph.D.

Aimé M. Mbobi, Ph.D., is a telecommunications engineer, computer scientist, professor, researcher, technology executive, and author with more than three decades of experience in higher education, research, and the ICT industry. He holds degrees from IMT Nord Europe, the University of Lille, the École des Mines de Paris, and Paris-Saclay University. His work focuses on programming, software engineering, telecommunications, artificial intelligence, scientific computing, data structures and algorithms, numerical methods, and applied mathematics. Through his books, he seeks to combine rigorous academic foundations, comparative analysis, and real-world industrial experience to help learners develop strong technical and problem-solving skills.

Contents

Table of Contents

Table of Contents

  • Preface
  • Chapter 1 - Computational and Mathematical Foundations of Symbolic Artificial Intelligence
    • 1.1 Introduction
    • 1.2 Computer Science as a Science of Representation, Formalization, and Processing
    • 1.3 Symbolic Artificial Intelligence as a Formal Extension of Computer Science
    • 1.4 Discrete Mathematics: Nature, Scope, and Specificity
    • 1.5 Discrete Mathematics at the Foundation of Computer Science
    • 1.6 Discrete Mathematics at the Foundation of Symbolic Artificial Intelligence
    • 1.7 Intersections, Complementarities, and the Guiding Line of the Book
    • 1.8 Conclusion and Transition to Set Theory
  • Chapter 2 - Set Theory, Mappings, and Relations
    • 2.1 Introduction
    • 2.2 Sets
    • 2.3 Operations on Sets
    • 2.4 Properties of the Union and Intersection Operators
    • 2.5 Mappings
    • 2.6 Relations
    • 2.7 Set Theory in Artificial Intelligence
    • 2.8 Conclusion of the Chapter
    • 2.9 Exercises
    • 2.10 Solutions
  • Chapter 3 - Cardinality and Combinatorics
    • 3.1 Introduction
    • 3.2 Cardinality of a Set
    • 3.3 Finite and Countable Sets
    • 3.4 Combinatorics
    • 3.5 Combinatorics in Artificial Intelligence
    • 3.6 Conclusion of the Chapter
    • 3.7 Exercises
    • 3.8 Solutions
  • Chapter 4 - Combinatorial Probability
    • 4.1 Introduction
    • 4.2 Fundamental Definitions
    • 4.3 Boolean Algebra
    • 4.4 Axioms and Fundamental Properties of Probability
    • 4.5 Conditional Probability and Independence
    • 4.6 Independence of Two Events
    • 4.7 Bayes’ Theorem
    • 4.8 Combinatorial Probability and Artificial Intelligence
    • 4.9 Conclusion of the Chapter
    • 4.10 Exercises
    • 4.11 Solutions
  • Chapter 5 - Arithmetic
    • 5.1 Introduction
    • 5.2 Euclidean Division
    • 5.3 Prime Numbers
    • 5.4 Greatest Common Divisor
    • 5.5 Computing the GCD by Euclid’s Algorithm
    • 5.6 Least Common Multiple
    • 5.7 Arithmetic and Artificial Intelligence: Symbolic Foundations and Algorithmic Applications
    • 5.8 Conclusion of the Chapter
    • 5.9 Exercises
    • 5.10 Solutions
  • Chapter 6 - Information Encoding and Numeration
    • 6.1 Introduction
    • 6.2 Numeration Systems
    • 6.3 Base Conversion
    • 6.4 Binary Arithmetic
    • 6.5 Conclusion of the Chapter
    • 6.6 Exercises
    • 6.7 Solutions
  • About the Author
  • The Mathematics for Computer Science and Symbolic Artificial Intelligence Series
  • Author’s Publishing Collections

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