Representation Theorems in Computer Science: A Treatment in Logic Engineering, Цzзep Цzgьr Lьtfь
Автор: Gelfond, M. and Kahl, Y. Название: Knowledge Representation, Reasoning, and the Design of Intelligent Agents ISBN: 1107029562 ISBN-13(EAN): 9781107029569 Издательство: Cambridge Academ Рейтинг: Цена: 53850.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This in-depth introduction for students and researchers shows how to use ASP for intelligent tasks, including answering queries, planning, and diagnostics.
Formal specifications are an important tool for the construction, verification and analysis of systems, since without it is hardly possible to explain whether a system worked correctly or showed an expected behavior. This book proposes the use of representation theorems as a means to develop an understanding of all models of a specification in order to exclude possible unintended models, demonstrating the general methodology with representation theorems for applications in qualitative spatial reasoning, data stream processing, and belief revision.
For qualitative spatial reasoning, it develops a model of spatial relatedness that captures the scaling context with hierarchical partitions of a spatial domain, and axiomatically characterizes the resulting relations. It also shows that various important properties of stream processing, such as prefix-determinedness or various factorization properties can be axiomatized, and that the axioms are fulfilled by natural classes of stream functions. The third example is belief revision, which is concerned with the revision of knowledge bases under new, potentially incompatible information. In this context, the book considers a subclass of revision operators, namely the class of reinterpretation operators, and characterizes them axiomatically. A characteristic property of reinterpretation operators is that of dissolving potential inconsistencies by reinterpreting symbols of the knowledge base.
Intended for researchers in theoretical computer science or one of the above application domains, the book presents results that demonstrate the use of representation theorems for the design and evaluation of formal specifications, and provide the basis for future application-development kits that support application designers with automatically built representations.
Автор: Hamilton William L. Название: Graph Representation Learning ISBN: 1681739658 ISBN-13(EAN): 9781681739656 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 75770.00 T Наличие на складе: Невозможна поставка. Описание:
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.
This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs--a nascent but quickly growing subset of graph representation learning.
Автор: Hamilton William L. Название: Graph Representation Learning ISBN: 1681739631 ISBN-13(EAN): 9781681739632 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 57290.00 T Наличие на складе: Невозможна поставка. Описание: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.
This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs-a nascent but quickly growing subset of graph representation learning.
Автор: Thomas Eiter; Hannes Strass; Miros?aw Truszczy?ski Название: Advances in Knowledge Representation, Logic Programming, and Abstract Argumentation ISBN: 3319147250 ISBN-13(EAN): 9783319147253 Издательство: Springer Рейтинг: Цена: 52170.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This Festschrift is published in honor of Gerhard Brewka on the occasion of his 60th birthday and contains articles from fields reflecting the breadth of Gerd`s work.
Автор: E. Cornell Way Название: Knowledge Representation and Metaphor ISBN: 904814079X ISBN-13(EAN): 9789048140794 Издательство: Springer Рейтинг: Цена: 181630.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information, and data- processing systems of all kinds, no matter whether human, (other) animal, or machine.
Автор: Croitoru Название: Graph Structures for Knowledge Representation and Reasoning ISBN: 3319781014 ISBN-13(EAN): 9783319781013 Издательство: Springer Рейтинг: Цена: 39130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2017, held in Melbourne, VIC, Australia, in August 2017, associated with IJCAI 2017, the 26th International Joint Conference on Artificial Intelligence.
Автор: Albarqouni Shadi, Cardoso M. Jorge, Dou Qi Название: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health: Third MICCAI Workshop, DART 2021, ISBN: 3030877213 ISBN-13(EAN): 9783030877217 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021.
Автор: Gruson, Caroline Serganova, Vera Название: Journey through representation theory ISBN: 3319982699 ISBN-13(EAN): 9783319982694 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Невозможна поставка. Описание: This text covers a variety of topics in representation theory and is intended for graduate students and more advanced researchers who are interested in the field. The book begins with classical representation theory of finite groups over complex numbers and ends with results on representation theory of quivers. The text includes in particular infinite-dimensional unitary representations for abelian groups, Heisenberg groups and SL(2), and representation theory of finite-dimensional algebras. The last chapter is devoted to some applications of quivers, including Harish-Chandra modules for SL(2). Ample examples are provided and some are revisited with a different approach when new methods are introduced, leading to deeper results. Exercises are spread throughout each chapter.Prerequisites include an advanced course in linear algebra that covers Jordan normal forms and tensor products as well as basic results on groups and rings.
Автор: Bernhard Nebel Название: Reasoning and Revision in Hybrid Representation Systems ISBN: 3540524436 ISBN-13(EAN): 9783540524434 Издательство: Springer Рейтинг: Цена: 32600.00 T Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Jia Yan, Gu Zhaoquan, Li Aiping Название: Mdata: A New Knowledge Representation Model: Theory, Methods and Applications ISBN: 3030715892 ISBN-13(EAN): 9783030715892 Издательство: Springer Цена: 69870.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Although many representation models or cognitive models have been proposed, such as expert systems or knowledge graphs, they cannot represent procedural knowledge, i.e., dynamic knowledge, in an efficient way.This book introduces a new knowledge representation model called MDATA (Multi-dimensional Data Association and inTelligent Analysis).
Автор: Gem Stapleton; John Howse; John Lee Название: Diagrammatic Representation and Inference ISBN: 3540877290 ISBN-13(EAN): 9783540877295 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Constitutes the proceedings of the 5th International Conference on Theory and Application of Diagrams, Diagrams 2008, held in Herrsching, Germany, in September 2008. This book organizes the papers in topical sections on diagram aesthetics and layout, psychological and cognitive issues, applications of diagrams, and theoretical aspects.
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