物件導向軟體之架構(Object-Oriented Software Construction)探討

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

National Institute of Standards and Technology における超伝導研究及び生活

Linked: The New Science of Networks

Cloud computing with its three key facets (i.e., IaaS, PaaS, and SaaS) and its inherent advantages (e.g., elasticity and scalability) still faces several challenges. The distance between the cloud and the end devices might be an issue for latency-sensitive applications such as disaster management and content delivery applications. Service Level Agreements (SLAs) may also impose processing at locations where the cloud provider does not have data centers. Fog computing is a novel paradigm to address such issues. It enables provisioning resources and services outside the cloud, at the edge of the network, closer to end devices or eventually, at locations stipulated by SLAs. Fog computing is not a substitute for cloud computing but a powerful complement. It enables processing at the edge while still offering the possibility to interact with the cloud. This article presents a comprehensive survey on fog computing. It critically reviews the state of the art in the light of a concise set of evaluation criteria. We cover both the architectures and the algorithms that make fog systems. Challenges and research directions are also introduced. In addition, the lessons learned are reviewed and the prospects are discussed in terms of the key role fog is likely to play in emerging technologies such as Tactile Internet.

A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges

Mobile cloud computing (MCC) is an extension of cloud computing (CC) technologies. It provides seamless access of different cloud services to smart mobile devices (SMDs). There is no denying that CC can be scaled to a great extent in terms of computing, storage and other services, but the SMDs used for accessing those services are limited on battery capacity, storage and computing power due to their small form factors. The limitations of SMDs can be minimised/resolved by using MCC platforms. Though MCC is advantageous in many ways, it has its own inherent challenges and issues due to the heterogeneous hardware and software platforms used by SMDs and CC platforms, which makes it difficult to have interoperable services and the development of applications for those devices. This paper studied recently (from 2012 onwards) proposed/developed middlewares for the Internet of things (IoT), cloud of things (CoT), context-aware middlewares (CaMs) and mobile cloud middlewares (MCMs). Different middleware architectures are chosen, as in many cases, these technologies converge in terms of features, functions and services they provide. The study finds that the present middlewares lack in providing an integrated solution that complies with interoperability, portability, adaptability, context awareness, security and privacy, service discovery, fault tolerance requirements. At the end of the paper, the challenges pertaining to achieve portability, interoperability, context awareness, security are discussed and identify the gaps in the existing approaches in MCC interoperability and context adaptability.

Middleware Frameworks for Mobile Cloud Computing, Internet of Things and Cloud of Things: A Review

Cloud-based computing services trend has significantly increased the building of new cloud data centres in the recent times. These data centres consume a large amount of electrical energy to run the Information Communication Technology (ICT) devices and for its cooling, as a result of that emission of \(CO_2\) and Greenhouse Gas (GHG) has increased largely. This paper proposes a Sustainable Software Development Life Cycle (SDLC) Model. This model would contribute towards making the cloud computing systems more energy efficient and the ICT a green and sustainable development ecosystem. The Green Cloud Computing will be considered as an implementation platform for the proposed Sustainable SDLC. It is proposed that reusable, modular and cloud-shared development approach be used to make the software development and the software more energy efficient. The SDLC will use a cloud repository which will be accessible as per software development organizations cloud deployment model.

Sustainable Software Development with a Shared Repository

In recent times classifier ensembles have become a mainstay in data mining and machine learning. The combination of several classifiers generally results in better performance and accuracy as compared to a single classifier. There are many different methods and techniques for constructing ensembles. Most of the time however, when these ensemble classifiers are constructed, the data used in the construction of ensemble classifiers becomes redundant. This redundant data results in a loss of accuracy and an increase in memory and system overhead. Therefore by removing this redundant data we can reduce the memory and system overhead as well as obtain an increase in accuracy. The redundant data can be eliminated by using a technique called feature selection. Feature selection is used to select the most relevant features while performing any task. There are many different feature selection algorithms such as memetic algorithms, sub-modular feature selection, etc. The feature selection technique can be used to choose the relevant data and eliminate the redundant data. The way to eliminate redundant data in ensemble classifiers is to perform classifier ensemble reduction. This paper discusses using feature selection and in particular employing recursive harmony search to perform classifier ensemble reduction via feature selection. The final ensemble classifier will be a reduced set of the original ensemble classifier, while maintaining diversity and accuracy of the original one.

Recursive Harmony Search Based Classifier Ensemble Reduction

Cloud computing provides a wide access to complex applications running on virtualized hardware with its support for elastic resources that are available in an on-demand manner. In cloud environment, multiple users can request resources simultaneously and so it has to be made available to them in an efficient manner. For the efficient utilization, these computing resources can be dynamically configured according to varying workload. Here in this paper, we proposed an efficient resource management system to allocate elastic resources dynamically according to dynamic workload.

Allocation of Cloud Resources in a Dynamic Way Using an SLA-Driven Approach

The recommendation system uses prior obtained information about the user to present user inteseted data. Personalized results aim to provide relevant information to the user based on the user’s basic information or activity with the system. The user’s basic information can be modeled into a user profile using ontology. Ontology is the systematic representation of various entities in a domain and the relationships between them. In this paper, we aim to present the conceptual model for a job recommendation system that uses ontology-based user profiles. The system collects basic information and models into a user profile. The dynamic aspects such as favorite jobs list and recently viewed jobs are then used as a source of data for the system. The recommendation algorithm works on the input given to present the list of relevant jobs to the user.

K. Chandrasekaran

Papers

Middleware Frameworks for Mobile Cloud Computing, Internet of Things and Cloud of Things: A Review

Sustainable Software Development with a Shared Repository

Recursive Harmony Search Based Classifier Ensemble Reduction

Allocation of Cloud Resources in a Dynamic Way Using an SLA-Driven Approach

Improving Job Recommendation Using Ontological Modeling and User Profiles