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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61-70 订阅

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Representing and Reasoning with Qualitative Preferences 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Ganesh Ram Santhanam Samik Basu Vasant Honavar

ISBN: (数字)9783031015731

ISBN: (纸本)9783031004452

This book provides a tutorial introduction to modern techniques for representing and reasoning about qualitative preferences with respect to a set of alternatives. The syntax and semantics of several languages for representing preference languages, including CP-nets, TCP-nets, CI-nets, and CP-theories, are reviewed. Some key problems in reasoning about preferences are introduced, including determining whether one alternative is preferred to another, or whether they are equivalent, with respect to a given set of preferences. These tasks can be reduced to model checking in temporal logic. Specifically, an induced preference graph that represents a given set of preferences can be efficiently encoded using a Kripke Structure for Computational Tree Logic (CTL). One can translate preference queries with respect to a set of preferences into an equivalent set of formulae in CTL, such that the CTL formula is satisfied whenever the preference query holds. This allows us to use a model checker toreason about preferences, i.e., answer preference queries, and to obtain a justification as to why a preference query is satisfied (or not) with respect to a set of preferences. This book defines the notions of the equivalence of two sets of preferences, including what it means for one set of preferences to subsume another, and shows how to answer preferential equivalence and subsumption queries using model checking. Furthermore, this book demontrates how to generate alternatives ordered by preference, along with providing ways to deal with inconsistent preference specifications. A description of CRISNER—an open source software implementation of the model checking approach to qualitative preference reasoning in CP-nets, TCP-nets, and CP-theories is included, as well as examples illustrating its use.

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

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Federated learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Qiang Yang Yang Liu Yong Cheng Yan Kang Tianjian Chen Han Yu

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Robot learning from Human Teachers 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Sonia Chernova Andrea L. Thomaz

ISBN: (数字)9783031015700

ISBN: (纸本)9783031004421

learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstrations. Next, is the choice of modeling technique. Currently, there is a dichotomy in the field between approaches that model low-level motor skills and those that model high-level tasks composed of primitive actions. We devote a chapter to each of these. Chapter 7 is devoted to interactive and active learning approaches that allow the robot to refine an existing task model. And finally, Chapter 8 provides best practices for evaluation of LfD systems, with a focus on how to approach experiments with human subjects in this domain.

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

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Adversarial machine learning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Yevgeniy Vorobeychik Murat Kantarcioglu

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Data Integration 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Michael Genesereth

ISBN: (数字)9783031015502

ISBN: (纸本)9783031004223

Data integration is a critical problem in our increasingly interconnected but inevitably heterogeneous world. There are numerous data sources available in organizational databases and on public information systems like the World Wide Web. Not surprisingly, the sources often use different vocabularies and different data structures, being created, as they are, by different people, at different times, for different purposes. The goal of data integration is to provide programmatic and human users with integrated access to multiple, heterogeneous data sources, giving each user the illusion of a single, homogeneous database designed for his or her specific need. The good news is that, in many cases, the data integration process can be automated. This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem, viz., the relational logic approach. Relational logic provides a theoretical framework for discussing data integration. Moreover, in many important cases, it provides algorithms for solving the problem in a computationally practical way. In many respects, relational logic does for data integration what relational algebra did for database theory several decades ago. A companion web site provides interactive demonstrations of the algorithms. Table of Contents: Preface / Interactive Edition / Introduction / Basic Concepts / Query Folding / Query Planning / Master Schema Management / Appendix / References / Index / Author Biography Don't have access? Recommend our synthesis Digital Library to your library or purchase a personal subscription. Email info@*** for details.

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

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Strategic Voting 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Reshef Meir

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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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ACTION PROGRAMMING LANGUAGES 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Michael Thielscher

ISBN: (数字)9783031015472

ISBN: (纸本)9783031004193

artificial systems that think and behave intelligently are one of the most exciting and challenging goals of artificial intelligence. Action Programming is the art and science of devising high-level control strategies for autonomous systems which employ a mental model of their environment and which reason about their actions as a means to achieve their goals. Applications of this programming paradigm include autonomous software agents, mobile robots with high-level reasoning capabilities, and General Game Playing. These lecture notes give an in-depth introduction to the current state-of-the-art in action programming. The main topics are knowledge representation for actions, procedural action programming, planning, agent logic programs, and reactive, behavior-based agents. The only prerequisite for understanding the material in these lecture notes is some general programming experience and basic knowledge of classical first-order logic. Table of Contents: Introduction / Mathematical Preliminaries / Procedural Action Programs / Action Programs and Planning / Declarative Action Programs / Reactive Action Programs / Suggested Further Reading

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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Judgment Aggregation: A Primer 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Davide Grossi Gabriella Pigozzi

ISBN: (数字)9783031015687

ISBN: (纸本)9783031004407

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 problems 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

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

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