Mathematical modelling Books

Book SynopsisAutomated 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 / ConclusionTable of ContentsIntroduction.- Example: Bidding on eBay.- Auction Fundamentals.- Continuous Double Auctions.- Interdependent Markets.- Conclusion.

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Book SynopsisThe 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 / ConclusionsTable of ContentsIntroduction.- 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.

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Book SynopsisOne of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future DirectionsTable of ContentsBackground on 3D Scene Models.- Single-view Geometry.- Modeling the Physical Scene.- Categorizing Images and Regions.- Examples of 3D Scene Interpretation.- Background on 3D Recognition.- Modeling 3D Objects.- Recognizing and Understanding 3D Objects.- Examples of 2D 1/2 Layout Models.- Reasoning about Objects and Scenes.- Cascades of Classifiers.- Conclusion and Future Directions.

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Book SynopsisCooperative game theory is a branch of (micro-)economics that studies the behavior of self-interested agents in strategic settings where binding agreements among agents are possible. Our aim in this book is to present a survey of work on the computational aspects of cooperative game theory. We begin by formally defining transferable utility games in characteristic function form, and introducing key solution concepts such as the core and the Shapley value. We then discuss two major issues that arise when considering such games from a computational perspective: identifying compact representations for games, and the closely related problem of efficiently computing solution concepts for games. We survey several formalisms for cooperative games that have been proposed in the literature, including, for example, cooperative games defined on networks, as well as general compact representation schemes such as MC-nets and skill games. As a detailed case study, we consider weighted voting games: a widely-used and practically important class of cooperative games that inherently have a natural compact representation. We investigate the complexity of solution concepts for such games, and generalizations of them. We briefly discuss games with non-transferable utility and partition function games. We then overview algorithms for identifying welfare-maximizing coalition structures and methods used by rational agents to form coalitions (even under uncertainty), including bargaining algorithms. We conclude by considering some developing topics, applications, and future research directions.Table of ContentsIntroduction.- Basic Concepts.- Representations and Algorithms.- Weighted Voting Games.- Beyond Characteristic Function Games.- Coalition Structure Formation.- Advanced Topics.

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Book SynopsisMarkov 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 NotesTable of ContentsIntroduction.- MDPs.- Fundamental Algorithms.- Heuristic Search Algorithms.- Symbolic Algorithms.- Approximation Algorithms.- Advanced Notes.

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Book SynopsisFrom 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 demystify the 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 / DiscussionTable of ContentsPart 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.

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Book SynopsisThe 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 ConsiderationsTable of ContentsAutomating Inquiry.- Uncertainty Sampling.- Searching Through the Hypothesis Space.- Minimizing Expected Error and Variance.- Exploiting Structure in Data.- Theory.- Practical Considerations.

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Book SynopsisPlanning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's BiographyTable of ContentsPreface.- Planning and Autonomous Behavior.- Classical Planning: Full Information and Deterministic Actions.- Classical Planning: Variations and Extensions.- Beyond Classical Planning: Transformations.- Planning with Sensing: Logical Models.- MDP Planning: Stochastic Actions and Full Feedback.- POMDP Planning: Stochastic Actions and Partial Feedback.- Discussion.- Bibliography.- Author's Biography.

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Book SynopsisUrban 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 community.Table of ContentsPreface.- Acknowledgments.- List of Symbols.- Introduction.- Elements of Supply.- Elements of Demand.- Traffic Assignment: Connecting Supply and Demand.- Getting Data for Demand Estimation and Traffic Flow Modeling.- Modeling and Simulation of Advanced Decision Making.- Intelligent Measures in Control and Management.- Driver Support and Guidance.- Trends and New Technologies.- Bibliography.- Authors' Biographies.

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Book SynopsisSolving 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.Table of ContentsPreface.- Summary of Acronyms.- Introduction to Time in AI Systems.- Temporal Frameworks Based on Constraints.- Extensions: Preferences and Uncertainty.- Applications of Temporal Reasoning.- Bibliography.- Authors' Biographies .

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Book SynopsisGeneral 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 and law. 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.Table of ContentsPreface.- Introduction.- Game Description.- Game Management.- Game Playing.- Small Single-Player Games.- Small Multiple-Player Games.- Heuristic Search.- Probabilistic Search.- Propositional Nets.- General Game Playing With Propnets.- Factoring.- Discovery of Heuristics.- Logic.- Analyzing Games with Logic.- Solving Single-Player Games with Logic.- Discovering Heuristics with Logic.- Games with Incomplete Information.- Games with Historical Constraints.- Incomplete Game Descriptions.- Advanced General Game Playing.- Authors' Biographies.

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Book SynopsisSimilarity 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 Metric Learning / Metric Learning for Special Settings / Metric Learning for Structured Data / Generalization Guarantees for Metric Learning / Applications / Conclusion / Bibliography / Authors' BiographiesTable of ContentsIntroduction.- Metrics.- Properties of Metric Learning Algorithms.- Linear Metric Learning.- Nonlinear and Local Metric Learning.- Metric Learning for Special Settings.- Metric Learning for Structured Data.- Generalization Guarantees for Metric Learning.- Applications.- Conclusion.- Bibliography.- Authors' Biographies .

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Book SynopsisLearning 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.Table of ContentsIntroduction.- Human Social Learning.- Modes of Interaction with a Teacher.- Learning Low-Level Motion Trajectories.- Learning High-Level Tasks.- Refining a Learned Task.- Designing and Evaluating an LfD Study.- Future Challenges and Opportunities.- Bibliography.- Authors' Biographies.

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Book SynopsisHow is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private? Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality. Today, many parts of the world demand that technology companies treat user data carefully according to user-privacy laws. The European Union's General Data Protection Regulation (GDPR) is a prime example. In this book, we describe how federated machine learning addresses this problem with novel solutions combining distributed machine learning, cryptography and security, and incentive mechanism design based on economic principles and game theory. We explain different types of privacy-preserving machine learning solutions and their technological backgrounds, and highlight some representative practical use cases. We show how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application.Table of ContentsPreface.- Acknowledgments.- Introduction.- Background.- Distributed Machine Learning.- Horizontal Federated Learning.- Vertical Federated Learning.- Federated Transfer Learning.- Incentive Mechanism Design for Federated Learning.- Federated Learning for Vision, Language, and Recommendation.- Federated Reinforcement Learning.- Selected Applications.- Summary and Outlook.- Bibliography.- Authors' Biographies.

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Book SynopsisJudgment 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 / IndexTable of ContentsPreface.- Acknowledgments.- Logic Meets Social Choice Theory.- Basic Concepts.- Impossibility.- Coping with Impossibility.- Manipulability.- Aggregation Rules.- Deliberation.- Bibliography.- Authors' Biographies.- Index .

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Book SynopsisLifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.Table of ContentsPreface.- Acknowledgments.- Introduction.- Related Learning Paradigms.- Lifelong Supervised Learning.- Continual Learning and Catastrophic Forgetting.- Open-World Learning.- Lifelong Topic Modeling.- Lifelong Information Extraction.- Continuous Knowledge Learning in Chatbots.- Lifelong Reinforcement Learning.- Conclusion and Future Directions.- Bibliography.- Authors' Biographies.

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Book SynopsisMany 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 available information 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. Table of ContentsPreface.- Acknowledgments.- Table of Abbreviations.- Introduction.- Multi-Objective Decision Problems.- Taxonomy.- Inner Loop Planning.- Outer Loop Planning.- Learning.- Applications.- Conclusions and Future Work.- Bibliography.- Authors' Biographies .

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Book SynopsisThis 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 to reason 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.Table of ContentsAcknowledgments.- Qualitative Preferences.- Qualitative Preference Languages.- Model Checking and Computation Tree Logic.- Dominance Testing via Model Checking.- Verifying Preference Equivalence and Subsumption.- Ordering Alternatives With Respect to Preference.- CRISNER: A Practically Efficient Reasoner for Qualitative Preferences.- Postscript.- Bibliography.- Authors' Biographies .

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Book SynopsisPlan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This synergistic area of research combines, unites, and makes use of techniques and research from a wide range of areas including user modeling, machine vision, automated planning, intelligent user interfaces, human-computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including assistive technology, software assistants, computer and network security, human-robot collaboration, natural language processing, video games, and many more. This wide range of applications and disciplines has produced a wealth of ideas, models, tools, and results in the recognition literature. However, it has also contributed to fragmentation in the field, with researchers publishing relevant results in a wide spectrum of journals and conferences. This book seeks to address this fragmentation by providing a high-level introduction and historical overview of the plan and goal recognition literature. It provides a description of the core elements that comprise these recognition problems and practical advice for modeling them. In particular, we define and distinguish the different recognition tasks. We formalize the major approaches to modeling these problems using a single motivating example. Finally, we describe a number of state-of-the-art systems and their extensions, future challenges, and some potential applications.Table of ContentsPreface.- Acknowledgments.- Introduction.- Defining a Recognition Problem.- Implicit vs. Explicit Representation of Knowledge.- Improving a Recognizer.- Future Directions.- Bibliography.- Authors' Biographies.

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Book SynopsisThe 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, finance, 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.Table of ContentsList of Figures.- Preface.- Acknowledgments.- Introduction.- Machine Learning Preliminaries.- Categories of Attacks on Machine Learning.- Attacks at Decision Time.- Defending Against Decision-Time Attacks.- Data Poisoning Attacks.- Defending Against Data Poisoning.- Attacking and Defending Deep Learning.- The Road Ahead.- Bibliography.- Authors' Biographies.- Index .

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Book SynopsisThis book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties. Table of ContentsIntroduction 1.1 introduction 1.2 Notations used in Book 1.3 Contents covered in this book 2 Representations of Networks 2.1 Introduction 2.2 Networks Represented as Graphs 2.3 Data Structures to Represent Graphs 2.3.1 Matrix Representation 2.3.2 Adjacency List 2.4 Network Embeddings 2.5 Evaluation Datasets 2.5.1 Evaluation Datasets 2.5.2 Evaluation Metrics 2.6 Machine Learning Downstream Tasks 2.6.1 Classification 2.6.2 Clustering 2.6.3 Link Prediction (LP) 2.6.4 Visualization 2.6.5 Network Reconstruction 2.7 Embeddings based on Matrix Factorization 2.7.1 Singular Value Decomposition (SVD) 2.7.2 Matrix Factorization based Clustering 2.7.3 Soft Clustering as Matrix Factorization 2.7.4 Non-negative Matrix factorization (NMF) 2.8 Word2vec 2.8.1 Skipgram model 2.9 Learning Network Embeddings 2.9.1 Supervised Learning 2.9.2 Unsupervised Learning 2.9.3 Node and Edge Embeddings 2.9.4 Graph Embedding 2.10 Summary 3 Deep Learning 3.1 Introduction 3.2 Neural Networks 3.2.1 Perceptron 3.2.2 Characteristics of Neural Networks 3.2.3 Multilayer Perceptron Networks 3.2.4 Training MLP Networks 3.3 Convolution Neural Networks 3.3.1 Activation Function 3.3.2 Initialization of Weights 3.3.3 Deep Feedforward Neural Network 3.4 Recurrent Networks 3.4.1 Recurrent Neural Networks 3.4.2 Long Short Term Memory 3.4.3 Different Gates used by LSTM 3.4.4 Training of LSTM Models 3.5 Learning Representations using Autoencoders 3.5.1 Types of Autoencoders 3.6 Summary References 4 Embedding Nodes and Edge 4.1 Introduction 4.2 Representation of Node and Edges as Vectors 4.3 Embeddings based on Random Walks 4.4 Embeddings based on Matrix Factorization 4.5 Graph Neural Network Models 4.6 State of the art algorithms 4.7 Evaluation methods and Machine Learning tasks 4.8 Summary References 5 Embedding Graphs 5.1 Introduction 5.2 Representation of Graphs as Vectors 5.3 Graph Representation using Node Embeddings 5.4 Graph Pooling Techniques 5.4.1 Global Pooling Methods 5.4.2 Hierarchical Pooling Methods 5.5 State of the art algorithms 5.6 Evaluation methods and Machine Learning tasks 5.7 Summary References

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Book SynopsisThe idea of modelling systems using graph theory has its origin in several scientific areas: in statistical physics (the study of large particle systems), in genetics (studying inheritable properties of natural species), and in interactions in contingency tables. The use of graphical models in statistics has increased considerably over recent years and the theory has been greatly developed and extended. This book provides the first comprehensive and authoritative account of the theory of graphical models and is written by a leading expert in the field. It contains the fundamental graph theory required and a thorough study of Markov properties associated with various type of graphs. The statistical theory of log-linear and graphical models for contingency tables, covariance selection models, and graphical models with mixed discrete-continous variables in developed detail. Special topics, such as the application of graphical models to probabilistic expert systems, are described briefly,Trade Review...this is an excellent reference book for those who are interested in studying the mathematical theory for graphical models. * Short Book Reviews, vol. 18, no. 1, April 1998 *Table of ContentsIntroduction ; 1. Graphs and Hypergraphs ; 2. Conditional Independence and Markov Properties ; 3. Contingency Tables ; 4. Multivariate Normal Models ; 5. Models for Mixed Data ; 6. Further topics ; A Various Prerequisites ; B Linear Algebra and Random Vectors ; C The Multivariate Distribution ; D Exponential Models

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Book SynopsisSimulating, Analyzing, and Animating Dynamical Systems: A Guide to XPPAUT for Researchers and Students provides sophisticated numerical methods for the fast and accurate solution of a variety of equations, including ordinary differential equations, delay equations, integral equations, functional equations, and some partial differential equations, as well as boundary value problems. It introduces many modeling techniques and methods for analyzing the resulting equations. Instructors, students, and researchers will all benefit from this book, which demonstrates how to use software tools to simulate and study sets of equations that arise in a variety of applications. Instructors will learn how to use computer software in their differential equations and modeling classes, while students will learn how to create animations of their equations that can be displayed on the World Wide Web. Researchers will be introduced to useful tricks that will allow them to take full advantage of XPPAUT's ca

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Book SynopsisTiefe und breite Erkenntnisse in den Life Sciences erschließen sich nur mit Mathematik. In diesem Buch erfahren Sie, wie klassische und aktuelle mathematische Modelle Vorgänge in der belebten Natur beschreiben. Erleben Sie, wie einfache Überlegungen zu mathematischen Modellen führen, die unser Verständnis von den Phänomenen in der Biologie und der Medizin grundlegend neugestalten, und wie auf dieser Grundlage ganz neue Erkenntnisse und Fortschritte möglich werden. Die Reise beginnt bei Fragen zur Ausbreitung von Epidemien und zu Impfstrategien und führt unter anderem über Gewässergesundheit, ein- oder mehrdimensionale Populationsdynamiken und pharmakokinetische Prozesse bis hin zur optimalen Medikationen bei Erkrankungen. Jedes Kapitel greift dabei auf so viel Mathematik zurück, wie Sie für die Beantwortung der Forschungsfragen benötigen. Die übersichtliche Gestaltung und der Einsatz von Farben im Text und in den zahlreichen Abbildungen erleichtern das Lesen und Verstehen. Aufgabenserien ermuntern Sie zu eigener Aktivität und eigenem Forschen. Table of ContentsEinleitung .- Modellbildung.- Diskrete Modelle.- Lineare Modelle.- Fourier-Reihen.- Nichtlineare Modelle.- Partielle Differentialgleichungen.- Laplace-Transformation.- Optimal Control.- Literaturverzeichnis.- Index.

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Book SynopsisThis book helps develop a better understanding of how researchers from different scientific backgrounds view models and uncertainty. It provides key steps in fostering and encouraging interdisciplinary research, which is vital in addressing several big issues that society faces today, such as climate change, longevity, financial and actuarial risk management. To make progress in these areas, researchers must develop an understanding of differing perspectives and methods of those working in other disciplines.This title presents the views and understandings of eminent people in their respective fields through interviews on the topic of modelling and uncertainty. Each expert was asked the same set of questions to help readers understand the similarities and differences existing between various disciplines. It also helps to bridge some of the gaps encountered by those carrying out inter- and multi-disciplinary research and suggests new approaches to modelling and uncertainty quantification.

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Book SynopsisQuestions about variation, similarity, enumeration, and classification of musical structures have long intrigued both musicians and mathematicians. Mathematical models can be found from theoretical analysis to actual composition or sound production. Increasingly in the last few decades, musical scholarship has incorporated modern mathematical content. One example is the application of methods from Algebraic Combinatorics, or Topology and Graph Theory, to the classification of different musical objects. However, these applications of mathematics in the understanding of music have also led to interesting open problems in mathematics itself.The reach and depth of the contributions on mathematical music theory presented in this volume is significant. Each contribution is in a section within these subjects: (i) Algebraic and Combinatorial Approaches; (ii) Geometric, Topological, and Graph-Theoretical Approaches; and (iii) Distance and Similarity Measures in Music.

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Book SynopsisThis monograph is devoted to recent mathematical theories on the bottom up self-organization observed in closed and isolated thermo-dynamical systems. Its main features include:

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