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检索条件"丛书名=Synthesis Lectures on Artificial Intelligence and Machine Learning"

共 95 条记录，以下是51-60 订阅

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Toward Robots That Reason: Logic, Probability & Causal Laws 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Vaishak Belle

ISBN: (数字)9783031210037

ISBN: (纸本)9783031210020;9783031210051

This book discusses the two fundamental elements that underline the science and design of artificial intelligence (AI) systems: the learning and acquisition of knowledge from observational data, and the reasoning of that knowledge together with whatever information is available about the application at hand. It then presents a mathematical treatment of the core issues that arise when unifying first-order logic and probability, especially in the presence of dynamics, including physical actions, sensing actions and their effects. A model for expressing causal laws describing dynamics is also considered, along with computational ideas for reasoning with such laws over probabilistic logical knowledge.

关键词： artificial intelligence Robotics Probability and Statistics in Computer Science Logic Design Computer Applications Data Mining and Knowledge Discovery

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Network Embedding 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Cheng Yang Chuan Shi Zhiyuan Liu Cunchao Tu Maosong Sun

heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward t... 详细信息

ISBN: (数字)9783031015908

ISBN: (纸本)9783031004629

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Explainable and Interpretable Reinforcement learning for Robotics 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Aaron M. Roth Dinesh Manocha Ram D. Sriram Elham Tabassi

ISBN: (数字)9783031475184

ISBN: (纸本)9783031475177;9783031475207

The authors consider classification techniques used in past surveys and papers and attempt to unify terminology across the field. The book provides an in-depth exploration of 12 attributes that can be used to classify explainable/interpretable techniques. These include whether the RL method is model-agnostic or model-specific, self-explainable or post-hoc, as well as additional analysis of the attributes of scope, when-produced, format, knowledge limits, explanation accuracy, audience, predictability, legibility, readability, and reactivity. The book is organized around a discussion of these methods broken down into 42 categories and subcategories, where each category can be classified according to some of the attributes. The authors close by identifying gaps in the current research and highlighting areas for future investigation.

关键词： artificial intelligence machine learning Robotics Robotics and Automation Control, Robotics, Mechatronics Visualization

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Introduction to Intelligent Systems in Traffic and Transportation 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Ana L.C. Bazzan Franziska Klügl

ISBN: (数字)9783031015656

ISBN: (纸本)9783031004377

Urban mobility is not only one of the pillars of modern economic systems, but also a key issue in the quest for equality of opportunity, once it can improve access to other services. Currently, however, there are a number of negative issues related to traffic, especially in mega-cities, such as economical issues (cost of opportunity caused by delays), environmental (externalities related to emissions of pollutants), and social (traffic accidents). Solutions to these issues are more and more closely tied to information and communication technology. Indeed, a search in the technical literature (using the keyword ``urban traffic" to filter out articles on data network traffic) retrieved the following number of articles (as of December 3, 2013): 9,443 (ACM Digital Library), 26,054 (Scopus), and 1,730,000 (Google Scholar). Moreover, articles listed in the ACM query relate to conferences as diverse as MobiCom, CHI, PADS, and AAMAS. This means that there is a big and diverse community of computer scientists and computer engineers who tackle research that is connected to the development of intelligent traffic and transportation systems. It is also possible to see that this community is growing, and that research projects are getting more and more interdisciplinary. To foster the cooperation among the involved communities, this book aims at giving a broad introduction into the basic but relevant concepts related to transportation systems, targeting researchers and practitioners from computer science and information technology. In addition, the second part of the book gives a panorama of some of the most exciting and newest technologies, originating in computer science and computer engineering, that are now being employed in projects related to car-to-car communication, interconnected vehicles, car navigation, platooning, crowd sensing and sensor networks, among others. This material will also be of interest to engineers and researchers from the traffic and transportation com

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Metric learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Aurélien Bellet Amaury Habrard Marc Sebban

ISBN: (数字)9783031015724

ISBN: (纸本)9783031004445

Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data. We first introduce relevant definitions and classic metric functions, as well as examples of their use in machine learning and data mining. We then review a wide range of metric learning algorithms, starting with the simple setting of linear distance and similarity learning. We show how one may scale-up these methods to very large amounts of training data. To go beyond the linear case, we discuss methods that learn nonlinear metrics or multiple linear metrics throughout the feature space, and review methods for more complex settings such as multi-task and semi-supervised learning. Although most of the existing work has focused on numerical data, we cover the literature on metric learning for structured data like strings, trees, graphs and time series. In the more technical part of the book, we present some recent statistical frameworks for analyzing the generalization performance in metric learning and derive results for some of the algorithms presented earlier. Finally, we illustrate the relevance of metric learning in real-world problems through a series of successful applications to computer vision, bioinformatics and information retrieval. Table of Contents: Introduction / Metrics / Properties of Metric learning Algorithms / Linear Metric learning / Nonlinear and Local Metri

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Essential Principles for Autonomous Robotics 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Henry Hexmoor

ISBN: (数字)9783031015632

ISBN: (纸本)9783031004353

From driving, flying, and swimming, to digging for unknown objects in space exploration, autonomous robots take on varied shapes and sizes. In part, autonomous robots are designed to perform tasks that are too dirty, dull, or dangerous for humans. With nontrivial autonomy and volition, they may soon claim their own place in human society. These robots will be our allies as we strive for understanding our natural and man-made environments and build positive synergies around us. Although we may never perfect replication of biological capabilities in robots, we must harness the inevitable emergence of robots that synchronizes with our own capacities to live, learn, and grow. This book is a snapshot of motivations and methodologies for our collective attempts to transform our lives and enable us to cohabit with robots that work with and for us. It reviews and guides the reader to seminal and continual developments that are the foundations for successful paradigms. It attempts to demystifythe abilities and limitations of robots. It is a progress report on the continuing work that will fuel future endeavors. Table of Contents: Part I: Preliminaries/Agency, Motion, and Anatomy/Behaviors / Architectures / Affect/Sensors / Manipulators/Part II: Mobility/Potential Fields/Roadmaps / Reactive Navigation / Multi-Robot Mapping: Brick and Mortar Strategy / Part III: State of the Art / Multi-Robotics Phenomena / Human-Robot Interaction / Fuzzy Control / Decision Theory and Game Theory / Part IV: On the Horizon / Applications: Macro and Micro Robots / References / Author Biography / Discussion

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Active learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Burr Settles

ISBN: (数字)9783031015601

ISBN: (纸本)9783031004322

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 instances 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

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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Predicting Human Decision-Making 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Ariel Rosenfeld Sarit Kraus

ISBN: (数字)9783031015786

ISBN: (纸本)9783031000232

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 task 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;. 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.

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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learning and Decision-Making from Rank Data 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Lirong Xia

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Representation Discovery using Harmonic Analysis 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Sridhar Mahadevan

ISBN: (数字)9783031015465

ISBN: (纸本)9783031004186

Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis. A central goal of this book is to show that these analytical tools can be generalized from their usual setting in (infinite-dimensional) Euclidean spaces to discrete (finite-dimensional) spaces typically studied in many subfields of AI. Generalizing harmonic analysis to discrete spaces poses many challenges: a discrete representation of the space must be adaptively acquired; basis functions are not pre-defined, but rather must be constructed. Algorithms for efficiently computing and representing bases require dealing with the curse of dimensionality. However, the benefits can outweigh the costs, since the extracted basis functions outperform parametric bases as they often reflect the irregular shape of a particular state space. Case studies from computer graphics, information retrieval, machine learning, and state space planning are used to illustrate the benefits of the proposed framework, and the challenges that remain to be addressed. Representation discovery is an actively developing field, and the author hopes this book will encourage other researchers to explore this exciting area of research. Tabl

关键词： artificial intelligence machine learning Mathematical Models of Cognitive Processes and Neural Networks

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