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.

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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

1 What is software architecture? A critical aspect of the design for any large software system is its gross structure – that is, its high-level organization of computational elements and interactions between those elements [3, 6]. Broadly speaking, we refer to this as the software architectural level of design. Recently software architecture has begun to emerge as an important field of study for software engineering practitioners and researchers. This emergence is evidenced by a large body of recent work in areas such as module interface languages, domain specific architectures, architectural description languages , formal underpinnings for architectural design, and architectural design environments. What do we mean by the term " software architecture " ? If we look at the current uses of the term " architecture " , we find that it is used in different ways, often making it difficult to understand what aspect is being addressed. Among the various meanings are (a) the architecture of a particular artifact, as in " the blueprints describe this building, " (b) an architectural style, as in " that church is an example of Gothic architecture, " and (c) the general study of architecture , as in " he has an advanced degree in architecture. " To clarify the meaning of the term " architecture " with respect to software systems, it is helpful to observe that the recent emergence of interest in software architecture has been prompted by two distinct trends. The first is the recognition that over the years designers have begun to develop a shared repertoire of methods, techniques, patterns and idioms for structuring complex software systems. For example, the box and line diagrams and explanatory prose that typically accompany a high-level system description often refer to such patterns as a " pipeline " , a " blackboard-oriented design " , or a " client-server system ". Although these terms rarely have precise definitions, they permit designers to describe complex systems using abstractions that make the overall system intelligible. Moreover, they provide significant semantic content that inform others about the kinds of properties that the system will have: the expected paths of evolution, its overall computational paradigm, and its relationship to similar systems. The second trend is the recent interest in exploiting specific domains to provide reusable frameworks for product families. This is based on the idea that common aspects of a collection of related systems can be extracted …

Software Architecture: Practice, Potential, and Pitfalls Panel Introduction

It would be a mistake to infer from the title of this session that discussions of emerging issues were postponed until this final session of the workshop. On the contrary, all of the preceding sessions devoted a large proportion of the discussion to identifying issues that are poorly understood and much in need of further research. In leading this session, Sam Redwine did a remarkable job of condensing this multi-leveled, wide ranging spectrum of issues into a coherent outline. His presentation consisted of two major parts: first he delivered (without interruptions) a summary of the issues discussed in previous sessions; next he highlighted topics for future work and outlined specific actions that should be taken. This was followed by a discussion covering a range of topics relating to Redwine’s presentation, the agenda for the next meeting, and other topics.

Emerging issues (session summary)

The last decade and a half of Nico Habermann's research career focused on software engineering, and in particular on software development environments. His earlier work was oriented more towards operating systems and programming language research. We take this opportunity to look back at his research, putting it in a larger perspective, identifying some general themes that characterize his contributions to software engineering in particular, and to computer science in general. >

Nico Habermann's research: a brief retrospective

Ozone Widget Framework (OWF) is an event-based web platform for lightweight integration of widget applications. This technical report presents a formal model of OWF’s widget composition mechanism. First, we present a detailed description of Ozone’s end user composition mechanism. Then, we describe our architectural modeling approach and its value for analysis of OWF widget compositions. We go through the process of creating an architectural style to represent assemblies of Ozone widgets, reviewing modeling decision points and style alternatives. This work was supported in part by the Office of Naval Research (ONR-N000140811223). Additional support was provided by the Center for Computational Analysis of Social and Organizational Systems (CASOS). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, or the U.S. government.

Architectural Modeling of Ozone Widget Framework End-User Compositions (CMU-ISR-14-108)

: One increasingly important technique for improving software-based system integrity is providing systems with the ability to adapt themselves at run time to handle such things as resource variability, changing user needs, and system faults. Traditionally system self repair has been handled within the application, and at the code level. An alternative approach, and the approach taken under this effort, is to use architectural models, maintained at run time, as the basis for system reconfiguration and repair. An architecture can provide a global perspective on the system, enabling high-level interpretation of system problems. This in turn, allows one to better identify the source of the problem. Moreover, architectural models can make integrity constraints explicit, helping to ensure the validity of any system change. This effort demonstrated how to generalize architecture-based adaptation by making the choice of architectural style an explicit design parameter in the framework. This allows system designers to pick an appropriate architectural style to expose properties of interest, provide analytic leverage and map cleanly to existing implementations and middleware.

http://www.dtic.mil/dtic/tr/fulltext/u2/a433766.pdf

David Garlan

Papers

Software Architecture: Practice, Potential, and Pitfalls Panel Introduction

Emerging issues (session summary)

Nico Habermann's research: a brief retrospective

Architectural Modeling of Ozone Widget Framework End-User Compositions (CMU-ISR-14-108)

RAINBOW: Architecture-Based Adaptation of Complex Systems