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"Artificial intelligence Mathematics."
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Engineering mathematics and artificial intelligence : foundations, methods, and applications
\"Engineering Mathematics and Artificial Intelligence: Foundations, Methods, and Applications discusses the theory behind Machine Learning and shows how Mathematics can be used in Artificial Intelligence. The book illustrates how to improve existing algorithms by using advanced Mathematics and offers cutting-edge Artificial Intelligence technologies. The book goes on to discuss how Machine Learning can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between Machine Learning and complex mathematical techniques is also highlighted within the book\"-- Provided by publisher.
Book
Commonsense reasoning
To endow computers with common sense is one of the major long-term goals of Artificial Intelligence research. One approach to this problem is to formalize commonsense reasoning using mathematical logic. Commonsense Reasoning is a detailed, high-level reference on logic-based commonsense reasoning. It uses the event calculus, a highly powerful and usable tool for commonsense reasoning, which Erik T. Mueller demonstrates as the most effective tool for the broadest range of applications. He provides an up-to-date work promoting the use of the event calculus for commonsense reasoning, and bringing into one place information scattered across many books and papers. Mueller shares the knowledge gained in using the event calculus and extends the literature with detailed event calculus solutions to problems that span many areas of the commonsense world.Covers key areas of commonsense reasoning including action, change, defaults, space, and mental states.The first full book on commonsense reasoning to use the event calculus. Contextualizes the event calculus within the framework of commonsense reasoning, introducing the event calculus as the best method overall. Focuses on how to use the event calculus formalism to perform commonsense reasoning, while existing papers and books examine the formalisms themselves. Includes fully worked out proofs and circumscriptions for every example.
eBook
Mathematical modeling for intelligent systems : theory, methods, and simulation
\"Mathematical Modeling for Intelligent Systems: Theory, Methods and Simulation aims to provide a reference for the applications of mathematical modeling using intelligent techniques in various unique industry problems in the era of Industry 4.0. Providing a thorough introduction to the field of soft computing techniques, the book covers every major technique in artificial intelligence in a clear and practical style. It also highlights current research and applications, addresses issues encountered in the development of applied systems, and describes a wide range of intelligent systems techniques, including neural networks, fuzzy logic, evolutionary strategy, and genetic algorithms. The book demonstrates concepts through simulation examples and practical experimental results. The book offers a well-balanced mathematical analysis of modelling physical systems. Summarizes basic principles in differential geometry and convex analysis as needed. The book covers a wide range of industrial and social applications, and bridges the gap between core theory and costly experiments through simulations and modelling. The focus of the book is manifold ranging from stability of fluid flows, nano fluids, drug delivery, and security of image data to Pandemic modeling etc. The book is primarily aimed at advanced undergraduates and postgraduate students studying computer science, mathematics and statistics. Researchers and professionals will also find this book useful\"-- Provided by publisher.
BOOK
Current practices and future direction of artificial intelligence in mathematics education: A systematic review
Mastering mathematics is often challenging for many students; however, the rise of artificial intelligence (AI) offers numerous advantages, including enhanced data analysis, automated feedback, and the potential for creating more interactive and engaging learning environments. Despite these benefits, there is a need for comprehensive reviews that provide an overview of AI's role in mathematics education to help educators identify the best AI tools, and to inform researchers about current trends and future directions. This study conducts a systematic literature review (SLR) to investigate the applications and trends of AI in mathematics education by examining articles published in reputable journals indexed in Web of Science and Scopus. The review categorizes AI tools into those narrowly addressing mathematical problems, such as solving equations and visualizing geometry, and those offering broader pedagogical support, including adaptive learning systems and generative AI platforms. Key aspects analyzed include the distribution of AI in Mathematics Education (AIME) studies across different educational levels, the types and categories of AI tools used, the functionality of commercialized AIME tools available on the internet, and the emerging trends and future directions in AIME based on recent literature. The insights from this SLR are crucial for educators, policymakers, and researchers, enabling them to integrate AI effectively into mathematics education and tailor tools to specific teaching strategies and learning needs.
Journal Article
Resisting AI : an anti-fascist approach to artificial intelligence
\"Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can't be fixed. Calling for the restructuring of AI, Dan McQuillan sets out an anti-fascist approach that replaces exclusions with caring and outlines new mechanisms that support collective freedom. Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can't be fixed. Instead of helping to address our current crises, AI causes divisions that limit people's life chances, and even suggests fascistic solutions to social problems. This book provides an analysis of AI's deep learning technology and its political effects and traces the ways that it resonates with contemporary political and social currents, from global austerity to the rise of the far right. Dan McQuillan calls for us to resist AI as we know it and restructure it by prioritising the common good over algorithmic optimisation. He sets out an anti-fascist approach to AI that replaces exclusions with caring, proposes people's councils as a way to restructure AI through mutual aid and outlines new mechanisms that would adapt to changing times by supporting collective freedom. Academically rigorous, yet accessible to a socially engaged readership, this unique book will be of interest to all who wish to challenge the social logic of AI by reasserting the importance of the common good\"--Back cover.
Book
Foundations of Constraint Satisfaction: Computation in Cognitive Science
Foundations of Constraint Satisfaction discusses the foundations of constraint satisfaction and presents algorithms for solving constraint satisfaction problems (CSPs). Most of the algorithms described in this book are explained in pseudo code, and sometimes illustrated with Prolog codes (to illustrate how the algorithms could be implemented).Comprised of 10 chapters, this volume begins by defining the standard CSP and the important concepts around it and presenting examples and applications of CSPs. The reader is then introduced to the main features of CSPs and CSP solving techniques (problem reduction, searching, and solution synthesis); some of the most important concepts related to CSP solving; and problem reduction algorithms. Subsequent chapters deal with basic control strategies of searching which are relevant to CSP solving; the significance of ordering the variables, values and compatibility checking in searching; specialized search techniques which gain their efficiency by exploiting problem-specific features; and stochastic search approaches (including hill climbing and connectionist approaches) for CSP solving. The book also considers how solutions can be synthesized rather than searched for before concluding with an analysis of optimization in CSPs.This monograph can be used as a reference by artificial intelligence (AI) researchers or as a textbook by students on advanced AI courses, and should also help knowledge engineers apply existing techniques to solve CSPs or problems which embed CSPs.
eBook
Diagrammatic Reasoning in AI
Pioneering work shows how using Diagrams facilitates the design of better AI systems The publication of Diagrammatic Reasoning in AI marks an important milestone for anyone seeking to design graphical user interfaces to support decision-making and problem-solving tasks. The author expertly demonstrates how diagrammatic representations can simplify our interaction with increasingly complex information technologies and computer-based information systems. In particular, the book emphasizes how diagrammatic user interfaces can help us better understand and visualize artificial intelligence (AI) systems. It examines how diagrammatic reasoning enhances various AI programming strategies used to emulate human thinking and problem-solving, including: Expert systems Model-based reasoning Inexact reasoning such as certainty factors and Bayesian networks Logic reasoning A key part of the book is its extensive development of applications and graphical illustrations, drawing on such fields as the physical sciences, macroeconomics, finance, business logistics management, and medicine. Despite such tremendous diversity of usage, in terms of applications and diagramming notations, the book classifies and organizes diagrams around six major themes: system topology; sequence and flow; hierarchy and classification; association; cause and effect; and logic reasoning. Readers will benefit from the author's discussion of how diagrams can be more than just a static picture or representation and how diagrams can be a central part of an intelligent user interface, meant to be manipulated and modified, and in some cases, utilized to infer solutions to difficult problems. This book is ideal for many different types of readers: practitioners and researchers in AI and human-computer interaction; business and computing professionals; graphic designers and designers of graphical user interfaces; and just about anyone interested in understanding the power of diagrams. By discovering the many different types of diagrams and their applications in AI, all readers will gain a deeper appreciation of diagrammatic reasoning.
eBook