Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning)
De Raedt, Luc,Kersting, Kristian,Poole, David,Natarajan, Sriraam
Sprache: Englisch
Verlag: Springer, 2016
Serie: Buch 7 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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hardcover. Zustand: Good. Damage on front cover.
Sprache: Englisch
Verlag: Springer, 2020
Serie: Buch 14 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Multi-Objective Decision Making (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Sprache: Englisch
Verlag: Springer, 2017
Serie: Buch 8 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Sprache: Englisch
Verlag: Springer, 2014
Serie: Buch 3 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Sprache: Englisch
Verlag: Springer, 2014
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Sprache: Englisch
Verlag: Springer, 2014
Serie: Buch 4 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Statistical Relational Artificial Intelligence: Logic, Probability, and Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning)
De Raedt, Luc; Kersting, Kristian; Natarajan, Sriraam; Poole, David
Sprache: Englisch
Verlag: Springer, 2016
Serie: Buch 7 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Hardcover
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 10 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 11 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Federated Learning (Synthesis Lectures on Artificial Intelligence and Machine Learning)
Yang, Qiang; Liu, Yang; Cheng, Yong; Kang, Yan; Chen, Tianjian; Yu, Han
Sprache: Englisch
Verlag: Springer, 2019
Serie: Buch 13 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 12 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Sprache: Englisch
Verlag: Springer International Publishing, 2017
Serie: Buch 8 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Many real-world decision problems have multiple objectives. For example, when choosing a medical treatment plan, we want to maximize the efficacy of the treatment, but also minimize the side effects. These objectives typically conflict, e.g., we can…often increase the efficacy of the treatment, but at the cost of more severe side effects. In this book, we outline how to deal with multiple objectives in decision-theoretic planning and reinforcement learning algorithms. To illustrate this, we employ the popular problem classes of multi-objective Markov decision processes (MOMDPs) and multi-objective coordination graphs (MO-CoGs).First, we discuss different use cases for multi-objective decision making, and why they often necessitate explicitly multi-objective algorithms. We advocate a utility-based approach to multi-objective decision making, i.e., that what constitutes an optimal solution to a multi-objective decision problem should be derived from the availableinformation about user utility. We show how different assumptions about user utility and what types of policies are allowed lead to different solution concepts, which we outline in a taxonomy of multi-objective decision problems.Second, we show how to create new methods for multi-objective decision making using existing single-objective methods as a basis. Focusing on planning, we describe two ways to creating multi-objective algorithms: in the inner loop approach, the inner workings of a single-objective method are adapted to work with multi-objective solution concepts; in the outer loop approach, a wrapper is created around a single-objective method that solves the multi-objective problem as a series of single-objective problems. After discussing the creation of such methods for the planning setting, we discuss how these approaches apply to the learning setting.Next, we discuss three promising application domains for multi-objective decision making algorithms: energy, health, and infrastructure and transportation. Finally, we conclude by outlining important open problems and promising future directions.
Sprache: Englisch
Verlag: Springer International Publishing, 2014
Serie: Buch 3 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational… elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. The book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. This book is intended for students and researchers interested in constraint-based temporal reasoning. It provides a self-contained guide to the different representations of time, as well as examples of recent applications of time in AI systems.
Sprache: Englisch
Verlag: Springer, 2020
Serie: Buch 14 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Sprache: Englisch
Verlag: Springer International Publishing, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose 'queries,' usually in the form of unlabeled data insta…nces to be labeled by an 'oracle' (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or 'query selection frameworks.' We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations.
Sprache: Englisch
Verlag: Springer, 2014
Serie: Buch 4 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Judgment aggregation is a mathematical theory of collective decision-making. It concerns the methods whereby individual opinions about logically interconnected issues of interest can, or cannot, be aggregated into one collective stance. Aggregation p…roblems have traditionally been of interest for disciplines like economics and the political sciences, as well as philosophy, where judgment aggregation itself originates from, but have recently captured the attention of disciplines like computer science, artificial intelligence and multi-agent systems. Judgment aggregation has emerged in the last decade as a unifying paradigm for the formalization and understanding of aggregation problems. Still, no comprehensive presentation of the theory is available to date. This Synthesis Lecture aims at filling this gap presenting the key motivations, results, abstractions and techniques underpinning it. Table of Contents: Preface / Acknowledgments / Logic Meets Social Choice Theory / Basic Concepts /Impossibility / Coping with Impossibility / Manipulability / Aggregation Rules / Deliberation / Bibliography / Authors' Biographies / Index.
Sprache: Englisch
Verlag: Springer, 2014
Serie: Buch 5 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at 'runtime' (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue,… general game players cannot rely on algorithms designed in advance for specific games; they must discover such algorithms themselves. General game playing expertise depends on intelligence on the part of the game player and not just intelligence of the programmer of the game player. GGP is an interesting application in its own right. It is intellectually engaging and more than a little fun. But it is much more than that. It provides a theoretical framework for modeling discrete dynamic systems and defining rationality in a way that takes into account problem representation and complexities like incompleteness of information and resource bounds. It has practical applications in areas where these features are important, e.g., in business andlaw. More fundamentally, it raises questions about the nature of intelligence and serves as a laboratory in which to evaluate competing approaches to artificial intelligence. This book is an elementary introduction to General Game Playing (GGP). (1) It presents the theory of General Game Playing and leading GGP technologies. (2) It shows how to create GGP programs capable of competing against other programs and humans. (3) It offers a glimpse of some of the real-world applications of General Game Playing.
Weitere BilderSprache: Englisch
Verlag: Springer, 2014
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Taschenbuch. Zustand: Neu. An Introduction to Constraint-Based Temporal Reasoning | Roman Barták (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xiii | Englisch | 2014 | Springer | EAN 9783031004391 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 He…idelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, 2017
Serie: Buch 8 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. Multi-Objective Decision Making | Diederik M. Roijers (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvii | Englisch | 2017 | Springer | EAN 9783031004483 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juerge…n[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, 2014
Serie: Buch 5 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. General Game Playing | Michael Genesereth (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvi | Englisch | 2014 | Springer | EAN 9783031004414 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartman…n[at]springer[dot]com | Anbieter: preigu.
Weitere BilderSprache: Englisch
Verlag: Springer, 2014
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Taschenbuch. Zustand: Neu. Judgment Aggregation | A Primer | Davide Grossi (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvii | Englisch | 2014 | Springer | EAN 9783031004407 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]…hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, 2012
Serie: Buch 2 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. Active Learning | Burr Settles | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xiv | Englisch | 2012 | Springer | EAN 9783031004322 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]c…om | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, 2019
Serie: Buch 13 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Sprache: Englisch
Verlag: Springer International Publishing, 2018
Serie: Buch 10 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Hardcover
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Human decision-making often transcends our formal models of 'rationality.' Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the ta…sk of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures-from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well asthe most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.
Sprache: Englisch
Verlag: Springer International Publishing, 2016
Serie: Buch 7 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Hardcover
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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with u…ncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
Sprache: Englisch
Verlag: Springer International Publishing, 2020
Serie: Buch 14 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.
Sprache: Englisch
Verlag: Springer, 2020
Serie: Buch 14 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. Graph Representation Learning | William L. Hamilton | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvii | Englisch | 2020 | Springer | EAN 9783031004605 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hart…mann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, 2018
Serie: Buch 11 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
- Softcover
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, financ…e, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop.The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for improving robustness of deep neural networks. We conclude with a discussion of several important issues in the area of adversarial learning that in our view warrant further research.Given the increasing interest in the area of adversarial machine learning, we hope this book provides readers with the tools necessary to successfully engage in research and practice of machine learning in adversarial settings.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Buch 11 von 14 - Synthesis Lectures on Artificial Intelligence and Machine Learning
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Taschenbuch. Zustand: Neu. Adversarial Machine Learning | Yevgeniy Vorobeychik (u. a.) | Taschenbuch | Synthesis Lectures on Artificial Intelligence and Machine Learning | xvii | Englisch | 2018 | Springer | EAN 9783031004520 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[…dot]hartmann[at]springer[dot]com | Anbieter: preigu.
