Mathematics and Artificial Intelligence

Erscheinungsjahr 2027
ISBN: 978-3-032-39160-5
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

This volume explores the enduring and increasingly vital relationship between mathematics and artificial intelligence (AI), highlighting how mathematical ideas continue to provide the foundation for advances across the AI landscape. Bringing together leading perspectives from diverse disciplines, the book examines recent developments in areas such as computational geometry, combinatorial optimization, mathematical logic, automated reasoning, probability theory, game theory, and data analysis. The contributions demonstrate how rigorous mathematical methods not only underpin intelligent systems but also drive innovation in emerging AI applications. Based on the latest research and survey presentations, the book offers a timely overview of both established principles and new directions at the interface of mathematics and artificial intelligence. 

Serving as a sequel to earlier landmark works on AI and mathematics, this collection reflects more than three decades of scholarly exchange fostered through the journal and the biennial conference. The chapters showcase a broad spectrum of theoretical and applied research, including topics presented at the 2025 anniversary session on AI and Mathematics at the . Dedicated to the memory of Peter L. Hammer, whose visionary contributions helped shape the field, the book celebrates the continuing power of interdisciplinary collaboration and points toward future opportunities for discovery at the intersection of mathematical sciences and artificial intelligence.

Zielgruppe

Research

Autoren/Hrsg.

Golumbic, Martin Charles

Hoffman, Frederick

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Informatik Mathematik für Informatiker

Inhaltsverzeichnis

An introduction to AI and mathematics (Golumbic, Hoffman).- PAC learning and label noise (Diochnos).- Techniques for formalizing domain knowledge within extensions of first-order login and modal logic (Morgenstern).- Control in computational social choice (Rothe).- AI through mathematical formalization (Venable).- Synthesizing biometric data (Ross).- A survey on statistical queries (Reyzin).- Automated theorem proving for mathematics using classical logic in the TPTP World (Sutcliffe).- Data science and optimization: How to build a deep reinforcement learning method for solving a combinatorial optimization problem (Zhang).- Model based deep learning applications to imaging and communication (Eldar).

Über Autor(innen)

Martin Charles Golumbic  is Professor of Computer Science and Founding Director of the Caesarea Rothschild Institute at the University of Haifa. He is the founding Editor-in-Chief of the journal  . His books include   and (as co-author)  . His current research is in combinatorial mathematics and its applications.

Frederick Hoffman is Professor of mathematical sciences at Florida Atlantic University, Boca Raton, USA. He obtained his PhD in Mathematics (1964) from the University of Virginia, USA. Dr. Hoffman directed thirty-five of the Southeastern International Conferences on Combinatorics, Graph Theory and Computing. He was one of the founding faculty of the FAU Computer Science Department, and a founding fellow of The Institute of Combinatorics and its Applications. His research interests focus on finite groups, combinatorics and applications, and artificial intelligence.