[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzk5MjU","title","Discrete Mathematics Foundations for Computer Science and Symbolic Artificial Intelligence","state","published","contactAuthorMethod","email","aboutText","

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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