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

共 95 条记录，以下是71-80 订阅

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Intelligent Autonomous Robotics 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Peter Stone

ISBN: (数字)9783031015441

ISBN: (纸本)9783031004162

Robotics technology has recently advanced to the point of being widely accessible for relatively low-budget research, as well as for graduate, undergraduate, and even secondary and primary school education. This lecture provides an example of how to productively use a cutting-edge advanced robotics platform for education and research by providing a detailed case study with the Sony AIBO robot, a vision-based legged robot. The case study used for this lecture is the UT Austin Villa RoboCup Four-Legged Team. This lecture describes both the development process and the technical details of its end result. The main contributions of this lecture are (i) a roadmap for new classes and research groups interested in intelligent autonomous robotics who are starting from scratch with a new robot, and (ii) documentation of the algorithms behind our own approach on the AIBOs with the goal of making them accessible for use on other vision-based and/or legged robot platforms.

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

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Introduction to Semi-Supervised learning 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Xiaojin Zhu Andrew B. Goldberg

ISBN: (数字)9783031015489

ISBN: (纸本)9783031004209

Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems such as humans learn in the presence of both labeled and unlabeled data. Traditionally, learning has been studied either in the unsupervised paradigm (e.g., clustering, outlier detection) where all the data are unlabeled, or in the supervised paradigm (e.g., classification, regression) where all the data are labeled. The goal of semi-supervised learning is to understand how combining labeled and unlabeled data may change the learning behavior, and design algorithms that take advantage of such a combination. Semi-supervised learning is of great interest in machine learning and data mining because it can use readily available unlabeled data to improve supervised learning tasks when the labeled data are scarce or expensive. Semi-supervised learning also shows potential as a quantitative tool to understand human category learning, where most of the input is self-evidently unlabeled. In this introductory book, we present some popular semi-supervised learning models, including self-training, mixture models, co-training and multiview learning, graph-based methods, and semi-supervised support vector machines. For each model, we discuss its basic mathematical formulation. The success of semi-supervised learning depends critically on some underlying assumptions. We emphasize the assumptions made by each model and give counterexamples when appropriate to demonstrate the limitations of the different models. In addition, we discuss semi-supervised learning for cognitive psychology. Finally, we give a computational learning theoretic perspective on semi-supervised learning, and we conclude the book with a brief discussion of open questions in the field. Table of Contents: Introduction to Statistical machine learning / Overview of Semi-Supervised learning / Mixture Models and EM / Co-Training / Graph-Based Semi-Supervised learning / Semi-Supervised Support Vector machines/

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

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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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An Introduction to Constraint-Based Temporal Reasoning 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Roman Barták Robert A. Morris K. Brent Venable

ISBN: (数字)9783031015670

ISBN: (纸本)9783031004391

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.

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

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An Introduction to the Planning Domain Definition Language 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Patrik Haslum Nir Lipovetzky Daniele Magazzeni Christian Muise

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Planning with Markov Decision Processes 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Mausam Andrey Kolobov

ISBN: (数字)9783031015595

ISBN: (纸本)9783031004315

Markov Decision Processes (MDPs) are widely popular in artificial intelligence for modeling sequential decision-making scenarios with probabilistic dynamics. They are the framework of choice when designing an intelligent agent that needs to act for long periods of time in an environment where its actions could have uncertain outcomes. MDPs are actively researched in two related subareas of AI, probabilistic planning and reinforcement learning. Probabilistic planning assumes known models for the agent's goals and domain dynamics, and focuses on determining how the agent should behave to achieve its objectives. On the other hand, reinforcement learning additionally learns these models based on the feedback the agent gets from the environment. This book provides a concise introduction to the use of MDPs for solving probabilistic planning problems, with an emphasis on the algorithmic perspective. It covers the whole spectrum of the field, from the basics to state-of-the-art optimal and approximation algorithms. We first describe the theoretical foundations of MDPs and the fundamental solution techniques for them. We then discuss modern optimal algorithms based on heuristic search and the use of structured representations. A major focus of the book is on the numerous approximation schemes for MDPs that have been developed in the AI literature. These include determinization-based approaches, sampling techniques, heuristic functions, dimensionality reduction, and hierarchical representations. Finally, we briefly introduce several extensions of the standard MDP classes that model and solve even more complex planning problems. Table of Contents: Introduction / MDPs / Fundamental Algorithms / Heuristic Search Algorithms / Symbolic Algorithms / Approximation Algorithms / Advanced Notes

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

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Graph-Based Semi-Supervised learning 1

引用

丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Amarnag Subramanya Partha Pratim Talukdar

ISBN: (数字)9783031015717

ISBN: (纸本)9783031004438

While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer vision, natural language processing, and other areas of artificial intelligence. Recognizing this promising and emerging area of research, this synthesis lecture focuses on graph-based SSL algorithms (e.g., label propagation methods). Our hope is that after reading this book, the reader will walk away with the following: (1) an in-depth knowledge of the current state-of-the-art in graph-based SSL algorithms, and the ability to implement them; (2) the ability to decide on the suitability of graph-based SSL methods for a problem; and (3) familiarity with different applications where graph-based SSL methods have been successfully applied. Table of Contents: Introduction / Graph Construction / learning and Inference / Scalability / Applications / Future Work / Bibliography / Authors' Biographies / Index

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

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Introduction to Logic Programming 1

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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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Reasoning with Probabilistic and Deterministic Graphical Models 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Rina Dechter

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Explainable Human-AI Interaction 1

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丛书名： synthesis lectures on artificial intelligence and machine learning

1000年

作者： Sarath Sreedharan Anagha Kulkarni Subbarao Kambhampati

ISBN: (数字)9783031037672

ISBN: (纸本)9783031037573

From its inception, artificial intelligence (AI) has had a rather ambivalent relationship with humans—swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever-increasing pace, there is a greater need for AI systems to work synergistically with humans. One critical requirement for such synergistic human‒AI interaction is that the AI systems' behavior be explainable to the humans in the loop. To do this effectively, AI agents need to go beyond planning with their own models of the world, and take into account the mental model of the human in the loop. At a minimum, AI agents need approximations of the human's task and goal models, as well as the human's model of the AI agent's task and goal models. The former will guide the agent to anticipate and manage the needs, desires and attention of the humans in the loop, and the latter allow it to act in ways that are interpretable to humans (by conforming to their mental models of it), andbe ready to provide customized explanations when needed. The authors draw from several years of research in their lab to discuss how an AI agent can use these mental models to either conform to human expectations or change those expectations through explanatory communication. While the focus of the book is on cooperative scenarios, it also covers how the same mental models can be used for obfuscation and deception. The book also describes several real-world application systems for collaborative decision-making that are based on the framework and techniques developed here. Although primarily driven by the authors' own research in these areas, every chapter will provide ample connections to relevant research from the wider literature. The technical topics covered in the book are self-contained and are accessible to readers with a basic background in AI.

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

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