Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

This paper gives an overview of the development of the field of Knowledge Engineering over the last 15 years. We discuss the paradigm shift from a transfer view to a modeling view and describe two approaches which considerably shaped research in Knowledge Engineering: Role-limiting Methods and Generic Tasks. To illustrate various concepts and methods which evolved in recent years we describe three modeling frameworks: CommonKADS, MIKE and PROTEGE-II. This description is supplemented by discussing some important methodological developments in more detail: specification languages for knowledge-based systems, problem-solving methods and ontologies. We conclude by outlining the relationship of Knowledge Engineering to Software Engineering, Information Integration and Knowledge Management.

/pdf/knowledge-engineering-principles-and-methods-6do1hbj75r.pdf

Knowledge engineering: principles and methods

Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.

/pdf/the-many-faces-of-publish-subscribe-59ahrjbweq.pdf

The many faces of publish/subscribe

The term ‘‘Internet-of-Things’’ is used as an umbrella keyword for covering various aspects related to the extension of the Internet and the Web into the physical realm, by means of the widespread deployment of spatially distributed devices with embedded identification, sensing and/or actuation capabilities. Internet-of-Things envisions a future in which digital and physical entities can be linked, by means of appropriate information and communication technologies, to enable a whole new class of applications and services. In this article, we present a survey of technologies, applications and research challenges for Internetof-Things.

/pdf/internet-of-things-vision-applications-and-research-4xfstaiv80.pdf

Internet of things: Vision, applications and research challenges

A critical issue for complex component-based systems design is the modeling and analysis of architecture. One of the complicating factors in developing architectural models is accounting for systems whose architecture changes dynamically (during run time). This is because dynamic changes to architectural structure may interact in subtle ways with on-going computations of the system. In this paper we argue that it is possible and valuable to provide a modeling approach that accounts for the interactions between architectural recon guration and non-recon guration system functionality, while maintaining a separation of concerns between these two aspects of a system. The key to the approach is to use a uniform notation and semantic base for both recon guration and steady-state behavior, while at the same time providing syntactic separation between the two. As we will show, this permits us to view the architecture in terms of a set possible architectural snapshots, each with its own steady-state behavior. Transitions between these snapshots are accounted for by special recon guration-triggering events.

Specifying Dynamism in Software Architectures

: We are moving towards a world of pervasive computing in which users can access and manipulate information from anywhere at anytime Computing devices and networks are becoming ubiquitous In this new world, computing will no longer be tethered to desktops: users will become increasingly mobile As users move across environments, they will have access to a dynamic range of computing devices and software services They will want to use the resources to perform computing tasks Today's computing infrastructure does not support this model of computing very well because computers interact with users in terms of low level abstractions: applications and individual devices Today, if a mobile user wants to use the computing resources of a new environment, he has to manually figure out how to perform a computing task using local resources and/or to migrate his computing context from another environment Such manual operation is unacceptable in a pervasive computing world because it does not scale with the increasing amount of software services, user mobility and resource dynamism It also demands that users spend too much time on non-task related configuration activities This technical report describes an approach called task-driven computing that can be used to solve this problem The approach is based on the insight that with appropriate system support it is possible to let users interact with their computing environments in terms of high level tasks and free them from low level configuration activities

/pdf/task-driven-computing-2a1ycm5lz5.pdf

Task-Driven Computing

As new market opportunities, technologies, platforms, and frameworks become available, systems require large-scale and systematic architectural restructuring to accommodate them. Today's architects have few tools and techniques to help them plan this architecture evolution. In particular, they have little assistance in planning alternative evolution paths, trading off various aspects of the different paths, or knowing best practices for particular domains. In this paper we describe an approach for assisting architects in developing and reasoning about architectural evolution paths. The key insight of our approach is that, architecturally, many system evolutions follow certain common patterns — or evolution styles. We define what we mean by an evolution style, and show how it can be used to provide automated assistance for expressing architectural evolution, and for reasoning about both the correctness and quality of evolution paths.

/pdf/evolution-styles-foundations-and-tool-support-for-software-47x09uohft.pdf

Evolution styles: Foundations and tool support for software architecture evolution

Software architectural modeling is crucial to the development of high-quality software. Tool support is required for this activity, so that models can be developed, viewed, analyzed, and refined to implementations. This support needs to be provided in a flexible and extensible manner so that the tools can fit into a company's process and can use particular, perhaps company-defined, domain-specific architectural styles. In this research demonstration, we describe AcmeStudio, a style-neutral architecture development environment that can be easily specialized for architectural design in different domains.

AcmeStudio: supporting style-centered architecture development

A programming system is the user interface between the programmer and the computer. Programming is a notoriously difficult activity, and some of this difficulty can be attributed to the user interface as opposed to other factors. Historically, the designs of programming languages and tools have not emphasized usability. 
This thesis describes a new process for designing programming systems where HCI knowledge, principles and methods play an important role in all design decisions. The process began with an exhaustive review of three decades of research and observations about the difficulties encountered by beginner programmers. This material was catalogued and organized for this project as well as for the benefit of other future language designers. Where questions remained unanswered, new studies were designed and conducted, to examine how beginners naturally think about and express problem solutions. These studies revealed ways that current popular programming languages fail to support the natural abilities of beginners. 
All of this information was then used to design HANDS, a new programming system for children. HANDS is an event-based system featuring a concrete model for computation based on concepts that are familiar to non-programmers. HANDS provides queries and aggregate operations to match the way non-programmers express problem solutions, and includes domain-specific features to facilitate the creation of interactive animations and simulations. In user tests, children using the HANDS system performed significantly better than children using a version of the system that lacked several of these features. This is evidence that the process described here had a positive impact on the design of HANDS, and could have a similar impact on other new programming language designs. 
The contributions of this thesis include a survey of the knowledge about beginner programmers that is organized for programming system designers, empirical evidence about how non-programmers express problem solutions, the HANDS programming system for children, a new model of computation that is concrete and based on familiar concepts, an evaluation of the effectiveness of key features of HANDS, and a case study of a new user-centered design process for creating programming systems.

David Garlan

Papers

Specifying Dynamism in Software Architectures

Task-Driven Computing

Evolution styles: Foundations and tool support for software architecture evolution

AcmeStudio: supporting style-centered architecture development

A programming system for children that is designed for usability