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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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Statistical Relational artificial intelligence 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Luc Raedt Kristian Kersting Sriraam Natarajan David Poole

ISBN: (数字)9783031015748

ISBN: (纸本)9783031000225

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 uncertainty. 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.

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

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A Concise Introduction to Multiagent Systems and Distributed artificial intelligence 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Nikos Vlassis

ISBN: (数字)9783031015434

ISBN: (纸本)9783031004155

Multiagent systems is an expanding field that blends classical fields like game theory and decentralized control with modern fields like computer science and machine learning. This monograph provides a concise introduction to the subject, covering the theoretical foundations as well as more recent developments in a coherent and readable manner. The text is centered on the concept of an agent as decision maker. Chapter 1 is a short introduction to the field of multiagent systems. Chapter 2 covers the basic theory of singleagent decision making under uncertainty. Chapter 3 is a brief introduction to game theory, explaining classical concepts like Nash equilibrium. Chapter 4 deals with the fundamental problem of coordinating a team of collaborative agents. Chapter 5 studies the problem of multiagent reasoning and decision making under partial observability. Chapter 6 focuses on the design of protocols that are stable against manipulations by self-interested agents. Chapter 7 provides a short introduction to the rapidly expanding field of multiagent reinforcement learning. The material can be used for teaching a half-semester course on multiagent systems covering, roughly, one chapter per lecture.

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

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Introduction to Logic Programming 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Michael Genesereth Vinay K. Chaudhri

ISBN: (数字)9783031015861

ISBN: (纸本)9783031004582

Logic Programming is a style of programming in which programs take the form of sets of sentences in the language of Symbolic Logic. Over the years, there has been growing interest in Logic Programming due to applications in deductive databases, automated worksheets, Enterprise Management (business rules), Computational Law, and General Game Playing. This book introduces Logic Programming theory, current technology, and popular applications. In this volume, we take an innovative, model-theoretic approach to logic programming. We begin with the fundamental notion of datasets, i.e., sets of ground atoms. Given this fundamental notion, we introduce views, i.e., virtual relations; and we define classical logic programs as sets of view definitions, written using traditional Prolog-like notation but with semantics given in terms of datasets rather than implementation. We then introduce actions, i.e., additions and deletions of ground atoms; and we define dynamic logic programs as sets of action definitions. In addition to the printed book, there is an online version of the text with an interpreter and a compiler for the language used in the text and an integrated development environment for use in developing and deploying practical logic programs.

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

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Markov Logic 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Pedro Domingos Daniel Lowd

ISBN: (数字)9783031015496

ISBN: (纸本)9783031004216

Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / learning / Extensions / Applications / Conclusion

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

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Visual Object Recognition 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Kristen Grauman Bastian Leibe

ISBN: (数字)9783031015533

ISBN: (纸本)9783031004254

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions

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

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Graph Representation learning 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： William L. Hamilton

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Algorithms for Reinforcement learning 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Csaba Szepesvári

ISBN: (数字)9783031015519

ISBN: (纸本)9783031004230

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

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

引用

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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Essentials of Game Theory 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Kevin Leyton-Brown Yoav Shoham

ISBN: (数字)9783031015458

ISBN: (纸本)9783031004179

Game theory is the mathematical study of interaction among independent, self-interested agents. The audience for game theory has grown dramatically in recent years, and now spans disciplines as diverse as political science, biology, psychology, economics, linguistics, sociology, and computer science, among others. What has been missing is a relatively short introduction to the field covering the common basis that anyone with a professional interest in game theory is likely to require. Such a text would minimize notation, ruthlessly focus on essentials, and yet not sacrifice rigor. This synthesis Lecture aims to fill this gap by providing a concise and accessible introduction to the field. It covers the main classes of games, their representations, and the main concepts used to analyze them.

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

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Trading Agents 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Michael P. Wellman

ISBN: (数字)9783031015540

ISBN: (纸本)9783031004261

Automated trading in electronic markets is one of the most common and consequential applications of autonomous software agents. Design of effective trading strategies requires thorough understanding of how market mechanisms operate, and appreciation of strategic issues that commonly manifest in trading scenarios. Drawing on research in auction theory and artificial intelligence, this book presents core principles of strategic reasoning that apply to market situations. The author illustrates trading strategy choices through examples of concrete market environments, such as eBay, as well as abstract market models defined by configurations of auctions and traders. Techniques for addressing these choices constitute essential building blocks for the design of trading strategies for rich market applications. The lecture assumes no prior background in game theory or auction theory, or artificial intelligence. Table of Contents: Introduction / Example: Bidding on eBay / Auction Fundamentals /Continuous Double Auctions / Interdependent Markets / Conclusion

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

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