Artificial Intelligence and Mathematical Methods in Pavement and Geomechanical Systems, Attoh-Okine, Nii O.
Автор: Walton Название: Goal-based Reasoning for Argumentation ISBN: 1107119049 ISBN-13(EAN): 9781107119048 Издательство: Cambridge Academ Рейтинг: Цена: 12670.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Practical argumentation is intelligent reasoning from an agent`s goals and known circumstances, and from an action selected as a means, to arrive at a decision on what action to take. This book will appeal to a wide audience, from designers of multi-agent and robotics systems to social scientists.
Автор: Manuel Bodirsky Название: Complexity of Infinite-Domain Constraint Satisfaction ISBN: 1107042844 ISBN-13(EAN): 9781107042841 Издательство: Cambridge Academ Рейтинг: Цена: 20750.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Introduces the universal-algebraic approach to the complexity classification of constraint satisfaction problems in the finite and infinite-domain cases. Including background material from logic, topology, and combinatorics, it is suitable for graduate students and researchers in theoretical computer science and adjacent areas of mathematics.
Автор: Dungar, R. Название: Geomechanical Modelling in Engineering Practice ISBN: 906191518X ISBN-13(EAN): 9789061915188 Издательство: Taylor&Francis Рейтинг: Цена: 49764.00 р. Наличие на складе: Нет в наличии.
Автор: Jun Wu Название: Multi-layer Composite Pavement System under Blast Load ISBN: 9811050007 ISBN-13(EAN): 9789811050008 Издательство: Springer Рейтинг: Цена: 22359.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book proposes the concept of a multi-layer pavement system to fulfill the blast resistance requirement for pavement design. It also presents a damage pattern chart for multi-layer pavement design and rapid repair after blast load. Such a multi-layer system consists of three layers including asphalt concrete (AC) reinforced with Geogrid (GST) at the top, a high-strength concrete (HSC) layer in the middle, and engineered cementitious composites (ECC) at the bottom. A series of large-scale laboratory impact tests were carried out to prove the usefulness of this concept and show its advantages over other conventional pavement system. Furthermore, field blast tests were conducted to show the actual behavior of this multi-layer pavement system subjected to blast load under real-world conditions.
Описание: An important part of the Artificial Intelligence theory is concerned with reasoning based on uncertain information. Classical logic and probability theory are not adequate for this, and many formalisms have been developed. The proceedings contained here aim to contribute to the elucidation of similarities and differences between these formalisms.
Описание: Computer science-especially pattern recognition, signal processing and mathematical algorithms-can offer important information about archaeological finds, information that is otherwise undetectable by the human senses and traditional archaeological approaches. Pattern Recognition and Signal Processing in Archaeometry: Mathematical and Computational Solutions for Archaeology offers state of the art research in computational pattern recognition and digital archaeometry. Computer science researchers in pattern recognition and machine intelligence will find innovative research methodologies combined to create novel and efficient computational systems, offering robust, exact, and reliable performance and results. Archaeologists, conservators, and historians will discover reliable automated methods for quickly reconstructing archaeological materials and benefit from the application of non-destructive, automated processing of archaeological finds.
Автор: Pattanayak, Santanu Название: Pro deep learning with tensorflow 2.0 ISBN: 1484289307 ISBN-13(EAN): 9781484289303 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE. Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications. What You Will Learn * Understand full-stack deep learning using TensorFlow 2.0 * Gain an understanding of the mathematical foundations of deep learning * Deploy complex deep learning solutions in production using TensorFlow 2.0 * Understand generative adversarial networks, graph attention networks, and GraphSAGE Who This Book Is For: Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.
Автор: Jan Krajicek Название: Proof Complexity ISBN: 1108416845 ISBN-13(EAN): 9781108416849 Издательство: Cambridge Academ Рейтинг: Цена: 21384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Proof complexity is a rich subject drawing on methods from logic, combinatorics, algebra and computer science. This self-contained book presents the basic concepts, classical results, current state of the art and possible future directions in the field. Suitable for doctoral students and researchers in mathematics and theoretical computer science.
Автор: Galmiche Название: Automated Reasoning ISBN: 3319942042 ISBN-13(EAN): 9783319942049 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book constitutes the refereed proceedings of the 9th International Joint Conference on Automated Reasoning, IJCAR 2018, held in Oxford, United Kingdom, in July 2018, as part of the Federated Logic Conference, FLoC 2018.
Автор: Li, Fanzhang / Zhang, Li / Zhang, Zhao Название: Dynamic Fuzzy Machine Learning ISBN: 3110518708 ISBN-13(EAN): 9783110518702 Издательство: Walter de Gruyter Рейтинг: Цена: 22439.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.
Описание: Petr Hajek shows that reasoning in fuzzy logic may be put on a strict logical (formal) basis, so contributing to our understanding of what fuzzy logic is and what one is doing when applying fuzzy reasoning.
Описание: 1. Global Introduction to the Artificial Mathematical Intelligence General Program.- 2. Some Basic Technical (Meta-)Mathematical Preliminaries for Cognitive Mathematics.- Part I. New Cognitive Foundations for Mathematics.- 3. General Considerations for the New Cognitive Foundations' Program.- 4. Towards the (Cognitive) Reality of Mathematics and the Mathematics of (Cognitive) Reality).- 5. The Physical Numbers.- 6. Dathematics: A Meta-Isomorphic Version of "Standard" Mathematics Based on Proper Classes.- Part II. Global Taxonomy of the Fundamental Cognitive Mathematical Mechanisms Used in Mathematical Research.- 7. Conceptual Blending in Mathematical Creation/Invention.- 8. Formal Analogical Reasoning in Concrete Mathematical Research.- 9. Conceptual Substratum.- 10. (Initial) Global Taxonomy of the Most Fundamental Cognitive Mechanisms Used in Mathematical Creation/Invention.- Part III. Toward a Universal Meta-Modeling of Mathematical Creation/Invention.- 11. Meta-Modeling of Classic and Modern Mathematical Proofs and Concepts.- 12. The Most Outstanding (Future) Challenges Toward Global AMI and its Plausible Extensions.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
